◦ Brand and retail tools:

1. Higg Brand and Retail Module (BRM): enables brands and retailers of all sizes to measure the environmental and social and labor impacts of their operations across a product's life cycle (from materials sourcing through its end of use). The environmental impacts measured include greenhouse gas (GHG) emissions, energy use, water use, water pollution, deforestation, hazardous chemicals, and animal welfare. The social and labor impacts measured include child labor, discrimination, forced labor, sexual harassment and gender-based violence in the workplace, non-compliance with minimum wage laws, bribery and corruption, working time, occupational health and safety, and responsible sourcing.


*2.3.1 Analytical method*

The reference method for the measurement of carbon monoxide concentration is based on the absorption of infrared radiation (IR) by the gas in a non-dispersive photometer [15]. CO absorbs IR radiation maximally at a wavelength of 4.7 micrometers (μm), which is in a spectral range where few other atmospheric species absorb to interfere with accurate quantification.

**Table 1.**

*WHO standard concentration of particulate matter at different stages of measurement.*

Carbon dioxide and water vapor can make major interference with the measurement of carbon monoxide. Removal of water vapor from the sample air is necessary to avoid positive interferences in the determination of CO concentration and is achieved by a permeation tube or drier that selectively removes water vapor from the sample gas without removing CO.

The concentration of CO can be determined using the Beer–Lambert law which relates the concentration of an absorbing species to the degree of light attenuation. The analytical method is suitable for stable installations at fixed-site monitoring stations.

Currently, potable carbon monoxide individual-level exposure measurements become familiar. Measurements of personal exposures are based on the electrochemical reactions between carbon monoxide and deionized water, which are detected by specially designed sensors.

The following guideline values (ppm values rounded) and periods of timeweighted average exposures have been determined in such a way that the COHb level of 2.5% is not exceeded, even when a normal subject engages in light or moderate exercise:


Meet the editors

Dr. Guillermo Huerta-Cuellar completed his doctoral studies at the Research Center in Optics, and a postdoctoral stay at the Potosino Institute of Scientific and Technological Research. He is currently Tenure Professor-Researcher at the University of Guadalajara, Mexico, is recognized for the desirable profile PRODEP, and is a member of both the National System of Researchers Level I and the consolidated research group Complex Systems,

Optics and Innovation. Dr. Huerta-Cuellar has more than eight years' teaching experience at undergraduate, masters, and doctorate levels. He has made research stays in Mexico, Italy, Spain, Russia, and the United States of America. He has produced 31 indexed articles, five book chapters, and 12 congress articles. To date, he has directed and completed six theses at undergraduate level and two at masters level, and has a PhD in progress.

Dr. Roghayeh Imani is a semiconductor physicist. Her main expertise is in nanostructured semiconductor growth, characterization, and optoelectronic devices. Parallel to experimental research, she is also studying computer modeling and simulation to design and predict new types of semiconductors. She received her bachelor and master degrees in physics, and obtained her undergraduate training at the Laboratory of Thin Film and Surface

Physics. She was awarded her doctoral degree in Nanoscience-Physics in November 2015 from the Electrical Engineering Faculty of Ljubljana University. During her PhD program she conducted impressive research into the biomedical application of multifunctional nanostructured semiconductors, and in 2017 her PhD was selected as a top-10 research subject in Ljubljana University. So far, her postdoctoral research in Sweden has been focused on the development of optoelectronic devices based on nanostructured semiconductors.


**6. Potential application of SPP strategy to other lakes in the world**

In this section, some typical lakes in the world will be discussed, and the application of the proposed SPP strategy to these lakes will be assessed.


*3.5. UV–Vis Analysis*

.

UV-Vis measurements in the absorption mode were carried out to reveal the electronic structure and size effect of as prepared nanoparticles. The optical band gap energy for synthesized nanoparticles has been calculated using the Tauc's relation: (α*h*ν) *n* = *A h*ν − *Eg* , where, α is the absorption coefficient, hν is incident photon energy of light, A is a constant, Eg denotes the band gap energy and n is constant that depends on the nature of optical transition (n = 2 and 0.5 for direct and indirect transition respectively) [32]. Figure 5b shows the plot of (αhν) 2 versus hν for Zn doped α-Fe2O3 nanoparticles that exhibits a direct band gap with n = 2. The charge transfer in α-Fe2O3 takes place between occupied O2− 2*p* state to empty Fe3+ 3*d* upper state that is responsible for direct band gap transition in Fe2O3. It is found that pure Fe2O3 nanoparticles has band gap of 2.66 eV which is higher than the reported band gap of 2.1 eV for pure Fe2O3 [1]. This indicates existence of Fe3+ in lower spin state that results in higher value of band gap for pure Fe2O3. Moreover, the obtained results show reduced band gap from 2.66 eV for pure Fe2O3 to 2.31 eV for Zn 4% and then increases for higher Zn concentration. This decrease in band gap may be ascribed to an increase in structural disorder or defects with increase in Zn doping up to 4% concentration. In addition, this decrease in band gap may also be due to partial hybridization between Zn t2g and O 2p states to empty Fe t\*2g 3d orbitals. A report by Mashiko et al. explained the decrease in band gap on the basis of decrease in residual in-plane strain [33]. Based on the above considerations, the sequence of band gap for synthesized samples is Zn 6% > Pure Fe2O3 > Zn 2% > Zn 4% which agrees well with experimental data and measured band gap values are given in Table 2.

**Table 2.** The calculated band gap, valence band and conduction band positions corresponding to pure Fe2O3 , Zn 2%, Zn 4% and Zn 6% nanoparticles.

Band edge positions, bandgap as well as the overall band structure of semiconductors play an important role in photocatalytic applications. The energy position of the band edge level can be controlled by the electronegativity of the dopants, as well as by the quantum confinement effects. The valence band and conduction band edge potential of a semiconductor can be deduced from the relation [34,35],

$$E\_{VB} = \chi - E\_{\varepsilon} + 0.5 \, E\_{\mathcal{g}} \tag{3}$$

$$E\_{\rm CB} = E\_{\rm VB} - E\_{\rm g} \tag{4}$$

where, EVB and ECB are the valence band and conduction band edge potential, respectively, χ is the absolute electronegativity of a semiconductor oxide and its value for Fe2O3 is 5.87 eV, Ee represents the energy of free electrons, which is about 4.5 eV on hydrogen scale. The calculated valence and conduction band edge position for synthesized samples are given in Table 2.


**Acknowledgements**

ornamental resources, water supply, and nursery function although other techniques such as factor income, replacement cost, and contingent valuation can also be applied [27]. For cultural services, techniques such as contingent valuation, travel cost, and hedonic pricing are impor‐ tant [27]. With growing emphasis on valuation of grassland goods and services, there is a need to conduct specific studies that will provide values for such goods and services. Such infor‐ mation will further improve the value of grasslands, thus further emphasizing the importance

Grasslands have a multi-functional role, providing food for herbivores as well as other goods and services such as carbon sequestration, nature conservation, and recreation. The goods and services provided by grasslands have direct and indirect monetary value. This study was conducted to a) provide a strategy for assessing the economic value of goods and services from grasslands, b) identify variables that influence the value of grassland goods and services, and c) identify gaps in knowledge which require more information to improve the valuation process. The study was conducted in three stages. Firstly, information regarding grasslands in the Province of Manitoba, Canada was collected based on grass type (native, naturalized or tame/seeded), land use (hay, pasture and other) and ownership (private, crown and nongovernmental organizations). This was followed by identification of goods and services that could be expected from these grasslands. The identified goods and services were then valuated. Market prices were utilized to value grassland goods and services where transactions occur in the marketplace while the benefit transfer method was used to infer monetary values of those goods and services that are not typically sold through the marketplace. Sensitivity analysis was performed to account for market price fluctuation or uncertainties in benefit transfer. The total economic value of Manitoba grasslands was derived by summing economic

Although the study identified 21 goods and services provided by Manitoba grasslands, only perennial forage production, carbon sequestration, nutrient cycling, water regulation, waste treatment, soil erosion control, soil formation, recreation, and wildlife habitat were included in the valuation. Genetic, medicinal, and ornamental resources, water supply, nursery function, biological value, and cultural services, while important, could not be assigned monetary value due to lack of data. The total economic value of Manitoba grasslands was estimated to be approximately \$1,436 million/yr (\$630 ha/yr), with a range of \$1,203 million/yr (\$527 ha/yr) to \$1,880 million/yr (\$810 ha/yr). The two most highly valued goods and services in Manitoba grasslands were perennial forage production and carbon sequestra‐ tion. Multidisciplinary research, focusing on economic valuation of non-market goods and services will provide more relevant transfer values than those obtained from other ecosystems

This study shows that goods and services in complex ecosystems such as temperate grasslands can be valued using market and non-market based valuation methods. Improvements in

values of goods and services for which supporting data was available.

and will greatly improve estimates of grassland value.

of maintaining productive grasslands.

**5. Conclusion**

164 Agroecology

This study was funded by Beef Cattle Research Council (BCRC), Manitoba Beef Producers, Canada Manitoba Agri-food Research and Development Initiative (ARDI), MITACS, Green‐ Cover, and Manitoba Rural Adaptation Council (MRAC). The authors would like to extend sincere appreciation to Bill Gardner (MAFRI), Glen Friesen (MAFRI), Don Flaten (University of Manitoba), Rick Andrews (Ducks Unlimited), and Wybo Vanderschuit (Riding Mountain National Park) for sharing their insights on Manitoba grasslands.


**Treatment of Complex Regional Pain Syndrome in Children and Adolescents: A Structured Literature Scoping Review**

**Andrea Vescio , Gianluca Testa , Annalisa Culmone, Marco Sapienza , Fabiana Valenti, Fabrizio Di Maria and Vito Pavone \***

Department of General Surgery and Medical Surgical Specialties, Section of Orthopaedics and Traumatology, Surgery, AOU Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy<EMAIL_ADDRESS>(A.V<EMAIL_ADDRESS>(G.T<EMAIL_ADDRESS>(A.C<EMAIL_ADDRESS>(M.S<EMAIL_ADDRESS>(F.V<EMAIL_ADDRESS>(F.D.M.) **\*** Correspondence<EMAIL_ADDRESS>
Received: 22 October 2020; Accepted: 18 November 2020; Published: 20 November 2020

**Abstract:** Background: Complex regional pain syndrome (CRPS) is characterized by chronic, spontaneous and provoked pain of the distal extremities whose severity is disproportionate to the triggering event. Diagnosis and treatment are still debated and multidisciplinary. The purpose of this systematic review is to analyze the available literature to provide an update on the latest evidence related to the treatment of CRPS in growing age. Methods: Data extraction was performed independently by three reviewers based on predefined criteria and the methodologic quality of included studies was quantified by the Newcastle–Ottawa Quality Assessment Scale Cohort Studies. Results: At the end of the first screening, following the previously described selection criteria, we selected n = 103 articles eligible for full-text reading. Ultimately, after full-text reading and a reference list check, we selected n = 6. The articles focused on physical (PT), associated with cognitive behavioral (CBT) and pharmacological (PhT) treatments. The combination of PT + CBT shows the most efficacy as suggested, but a commonly accepted protocol has not been developed. Conclusions: Physical therapy in association with occupational and cognitive behavioral treatment is the recommended option in the management of pediatric CPRS. Pharmacological therapy should be reserved for refractory and selected patients. The design and development of a standard protocol are strongly suggested.

**Keywords:** pediatric; growing age; complex regional pain syndrome; reflex sympathetic dystrophy; multidisciplinary; physical therapy; cognitive behavioral therapy; drugs; pharmacological treatment; occupational therapy

#### **1. Introduction**

First described in the 17th century as "causalgia" [1], complex regional pain syndrome (CRPS) is characterized by chronic, spontaneous and provoked pain of the distal extremities whose severity is disproportionate to the triggering event [2]. Three different CRPS subtypes have been distinguished:

CRPS type 1 affects children and adolescents aged 5 to 17 years, with a peak incidence around the 13th year of age, and it is more frequently found in women (70% of cases) [3,4]. The pathogenic mechanism is still unclear, although several hypotheses have been proposed. Genetic factors, altered microcirculation and traumas, such as sprains, fractures and surgical procedures, contribute to pain symptoms. Anxiety, somatization and familial and school problems could also play a role. Chronic pain, generally unilateral and limb localized, autonomic and motor dysfunction and trophic disorders are the main symptoms of CRPS type 1. There are two presentations of the syndrome: the "warm" one, with red, warm, swollen skin that usually occurs in the acute phase, and the "cold" one, with blue/purple, cold, sweaty skin, which is usually associated with the chronic phase [3–6]. The diagnosis of CRPS type 1 is clinical and based on the Budapest diagnostic criteria [4]. However, diagnosis remains challenging due to the lack of validated diagnostic tests and the difficulty of differential diagnosis. Laboratory and imaging tests can be helpful in the event of diagnostic doubt [1]. Treatment is multidisciplinary, and it is mostly based on physical and psychological therapy and medications; only in selected subjects is treatment invasive [7]. The purpose of this systematic review was to analyze the available literature to provide an update on the latest evidence related to the treatment of CRPS type 1 in children and adolescents, highlighting the multidisciplinary approach.

#### **2. Materials and Methods**

#### *2.1. Literature Search Strategy*

A systematic review of the current literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [8]. On 20th September 2019, three independent authors (SM, VF and DiMF) performed a systematic review of two different medical electronic databases (PubMed and Web of Science). To achieve the maximum sensitivity of the search strategy, a search string was used ("(complex regional pain syndrome OR reflex sympathetic dystrophy OR Sudeck's atrophy) AND (pediatric OR adolescent OR children OR childhood) AND (treatment OR management)").

#### *2.2. Selection Criteria*

The reference lists of all retrieved articles were reviewed for further identification of potentially relevant studies, and the articles were assessed using the inclusion and exclusion criteria. The following inclusion criteria were used when screening titles and abstracts: (a) studies of any level of evidence; (b) studies written in the English language; (c) studies reporting clinical or preclinical results; (d) published studies in peer review journals on the treatment of complex regional pain syndrome type 1. The exclusion criteria were as follows: (a) review articles, (b) case reports, (c) articles written in other languages, (d) diagnosis or differential diagnosis of complex regional pain syndrome type 1. We also excluded all the remaining duplicates, articles dealing with other topics and those with poor scientific methodology or without an accessible abstract. Reference lists were also hand-searched for further relevant studies.

#### *2.3. Data Extraction and Criteria Appraisal*

All data were extracted from article texts, tables and figures. Three investigators (SM, VF and DMF) independently reviewed each article. Discrepancies between the three reviewers were resolved by discussion and consensus. The final results and any remaining controversy on the reviewed article were reviewed and discussed with the senior investigators (VA and CA), who served as independent reviewers and assessed study quality. Conflicts about data were resolved by the senior surgeon (PV). Reference lists from the selected papers were also screened. The PRISMA flowchart for the selection and screening method is provided in Figure 1.

**Figure 1.** PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) flowchart of the systematic literature review.

#### *2.4. Risk of Bias Assessment*

A risk of bias assessment of all selected full-text articles was performed according to the Newcastle–Ottawa Quality Assessment Scale Cohort Studies (NOS) [9]. The NOS contains eight items, categorized into three dimensions including selection, comparability and—depending on the study type—outcome (cohort studies) or exposure (case-control studies). For each item, a series of response options is provided. A star system is used to perform a semi-quantitative assessment of study quality, such that the highest quality studies are awarded a maximum of one star for each item, except for the item related to comparability, which allows the assignment of two stars. The NOS ranges between zero and nine stars. The assessments were performed by two authors (VA and CA) independently. Any discrepancy was discussed with the senior investigator for the final decision. All the raters agreed on the final result of every stage of the assessment (Appendix A). In the systematic review, studies classified with more than six stars were included.

#### **3. Results**

#### *3.1. Study Selection*

From the search of PubMed and Web of Science, 264 articles were included in the review, and 24 studies were selected after duplicate exclusion. Following the inclusion and exclusion criteria, the first screening was performed. A total of 103 papers were considered eligible. Finally, after the full-text reading, reference list check and risk of bias assessment, six studies were included. A PRISMA [8] flowchart of the method of selection and screening is provided (Figure 1).

The main focus of the included studies was related to physical (PT), cognitive behavioral (CBT) and pharmacological (PhT) treatments. A summary of the results is provided in Table 1.

#### **Table 1.**Results of selected studies.

#### **Table 1.** *Cont.*

#### *3.2. Physical Therapy and Cognitive Behavioral Treatment*

Three included studies contained a combination of physical therapy and cognitive treatment. Sherry et al. [10] reported the complete resolution of pain symptoms in 74.7% of the sample. Seven subjects did not have remission. One child was dysfunctional with CRPS pain, and five had persistent mild pain but were fully functional. The authors highlighted suicide attempts (*p* = 0.026), an eating disorder (*p* = 0.028), reporting less pain initially (*p* = 0.021) and scoring higher on the Brief Symptom Inventory subsets for depression and paranoid ideation (*p* 0.037 and 0.048, respectively) as predictors of recurrence. Lee et al. [11] divided patients into two different groups: PT + CBT for 3 weeks vs. PT + CBT for 6 weeks. Both the cohorts showed improvement in all pain and physical functioning outcome measures with short- and long-term follow-up, without differences in pain scores, recurrent episodes of CRPS or participation in school or activities. Logan et al. [12] assessed 56 patients aged 8–18 years at admission and at discharge, and every parameter evaluated had statistically significant improvements. Thirty-two percent of patients required an assistive device at admission, while none required one at discharge.

#### *3.3. Pharmacological Treatment*

Petje et al. [13] assessed functional outcome in patients treated with an intravenous infusion of iloprost, a prostacyclin analogue. The drug was administered 6 h per day on 3 consecutive days. Among the side effects noted were headache on the first day of infusion in all patients and flushing and vomiting on the second day in three patients. A decrease in systolic blood pressure of an average of 7 mm Hg (5–15 mm Hg) in the first 30 min after administration was detected in almost the entire cohort. Improvement of the visual analogue scale (VAS) score was found (*p* < 0.05). Relapse of CPRS was experienced by two patients, the first after 3 months. Brown et al. [14] analyzed the outcome of 29 patients refractory to PT + CBT. Fourteen subjects underwent amitriptyline administration, and 15 underwent gabapentin administration. After the 6-week trial, both cohorts showed improvement in pain symptoms, sleep disturbances and functionality. No statistically significant differences were found (*p* = 0.77) between the drugs. Similar adverse events were recorded (*p* = 0.77). Donado and colleagues [15] investigated the use of continuous regional anesthesia (epidural or peripheral catheter) in subjects refractory to PT + CBT. Their data showed significant changes between admission and discharge for pain (*p* < 0.0001), without significant changes throughout the 4-month period after admission (*p* > 0.05).

#### **4. Discussion**

According to our data, the physical therapy combined with cognitive behavioral treatment should be considered as the most appropriate first-line approach in CRPS-affected children and adolescents. The pharmacological therapy was found to be efficient in the PT + CBT failure case; the use of drugs is useful only in selected patients subjected to adjuvant physical and cognitive protocol. Nowadays, the lack of comparison treatment studies and specified outcome measurements does not allow for more detailed analysis or the development of a standard treatment.

CRPS is a common but not completely understood disorder, with no available data about the incidence of pediatric CRPS [5] because the diagnosis is uncertain and underestimated. Early diagnosis is as important as treatment; in fact, a longer disease course and sequelae [2] are associated with late identification. Unfortunately, no specific diagnostic tools have been developed for children and adolescents, so the adult criteria are used [5]. Orthopedists have a key role in the recognition of the disease due to very little evidence, no common consensus among the physicians and a lack of guidelines [6]. As reported by Berde and Lebel [16], often, the choice of treatment may vary according to the experience and resources of the clinician. Several treatments have been described [2], including acupuncture, transcranial magnetic stimulation and invasive procedures, but the efficacy has been proven for the combination of physical and cognitive behavioral therapy and only pharmacological

treatments. The most established treatment is a program of physical rehabilitation and cognitive behavioral therapy [15]. The goal is to restore normal function, increase the joint motion range and load tolerance and strength and concurrently assist the child in accepting and managing the pain [2].

Different protocols were illustrated in selected studies, highlighting the absence of a standard treatment protocol. Sherry and colleagues [10] suggested aerobic training and progressive resistive exercises, in addition to hydrotherapy, desensitization with towel rubbing, hand massage, textured fabric rubs and contrast baths (2 and 38 ◦C). During the patient's hospitalization, 5–6 h of daily exercise therapy and 45 min to 3 h of home exercise program (HEPs) were performed. In the Logan et al. trial [12], the patients underwent open-chain and closed-chain activities and an individualized HEP, and each child's functional goals, such as playing a specific sport, were incorporated into the physical schedule. In addition, this multidisciplinary rehabilitation approach addresses the entire pain experience, incorporating desensitization, exposure to feared activities, skills for coping with pain and changes to social responses to pain. Lee et al. [11] designed a protocol including transcutaneous electrical nerve stimulation, progressive weight bearing, tactile desensitization, massage, contrast baths and an HEP. Six weekly sessions of individual CBT incorporating pain management strategies, including relaxation training, deep breathing exercises, biofeedback and guided imagery, were also included. Patient compliance, nurse care and parent treatment programs [17] are crucial to promote successful remission from pain and restoration of functional ability. Despite a rigorous rehabilitation program, Sherry et al. described their patients as a motivated and eager to please sample [10]. Lee et al. [11] recorded compliance varying between 78% and 82%. No adverse events were recorded in the three studies, but the remission rate varied between 79% and 100% [10–12]. Several authors have investigated the role of PT + CBT in pediatric CRPS type 1, especially the brain and neurological changes and treatment action on the central nervous system. Frot et al. found evidence of emotional integration of pain in CRPS patients [18]. Lebel et al. [19] concluded that some changes in the brain persist, especially in the amygdala and basal ganglia, even after symptomatic recovery [20]. Diers et al. [21] demonstrated that behavioral extinction training reduces the emotional involvement in processing painful stimuli and induces a shift to a more sensory-discriminative way of pain processing post-treatment. Kregel et al. [22] emphasize that conservative treatments for patients with chronic musculoskeletal pain may induce both functional and morphological changes in predominantly prefrontal brain regions. For these reasons, non-invasive treatments are often recommended, even in recurrent forms [2,4].

On the other hand, the literature presents evidence of good outcomes after intravenous infusion of drugs and regional nerve blockades. Three pharmacological trials were selected in the study, and different molecules were investigated. Petje et al. [13] assessed the outcome of iloprost intravenous infusion, an analogue of prostacyclin, which induces transitory complete sympathicolysis and avoids the anxiety associated with a lumbar sympathetic blockade. Despite the good rate of response, relevant adverse reactions such as headache, flushing, vomiting and a decrease in systolic blood pressure were recorded in all cohorts; consequently, the same authors do not suggest iloprost as primary therapy. Other drugs proposed for treating neuropathic pain were gabapentin and amitriptyline. The first avoids the release of neurotransmitters acting on voltage-gated calcium channels [23], and the latter neuropathically reinforces the serotonin transporter [24]. Brown et al. compared the two molecules in a refractory PT + CBT schedule. The series revealed that both drugs are effective in reducing pain scores and improving sleep, without significant differences. However, ventricular conduction abnormalities were noted in the gabapentin group, while amitriptyline was linked to QT prolongation, torsade de pointes and sudden cardiac death. For these reasons, the use of both drugs in selected patients and with proper monitoring may be considered. Several invasive options have been proposed, including the use of continuous regional anesthesia with epidural or peripheral catheters, which demonstrate a reduction in pain score and improvement in function score at short- and long-term follow-up. On the other hand, in the Donado et al. series [15], 39% of the sample did not experience clinical improvements in pain symptoms, and 43% had no functional advantages.

Nevertheless, the authors suggested the treatment in addition to an active PT + CBT protocol. All the studies included in the systematic review emphasized the utility of PT + CBT, even when additional approaches were undertaken. The comparison of management with versus without rehabilitation was considered ethically unacceptable [2]; however, the outcome may not be related to a single treatment, and the results have been influenced by conservative treatment even in pharmacologic protocol studies. Future research directions should focus on the identification of disease onset mechanisms and the development of more defined, proper and easy-to-use diagnostic tools.

The design of high-quality, prospective, large-cohort, long-term follow-up studies is strongly encouraged, as is the design of a specific assessment score.

Limits of the study are several and included the heterogeneity of the scores utilized in the objective clinical assessment of the patients. VAS score is an unspecific tool which aims to evaluate the pain, a limited feature of CPRS. At the same time, some authors used more specific scores, such as FDI and PFD, which were not initially developed for CRPS. In addition, due to the challenging diagnosis and long, individual and expensive treatments, studies by Brown et al. [14], Petje et al. [13] and Lee et al. [11] included small size samples, but the described protocols were evaluated with great interest for future prospective use. Moreover, the literature contains limited high-quality studies: despite major randomized prospective studies being included in the review, no control group article or direct comparisons between treatments have been published. The results of drug-related trials could be influenced by different factors, such as longer follow-up after the first or second PT + CBT treatment. The absence of standard protocols and the lack of randomized, blinded prospective trials are the main limits in the comparison of study results.

#### **5. Conclusions**

Complex regional pain syndrome in children and adolescents remains a challenge for the physician. The definition is not clear or commonly accepted, and this results in several undiagnosed cases. Despite several diagnostic standards being proposed in adults, no specific diagnostic criteria in growing age patients have been developed and proper treatment is often dependent on the physician's experience and the treatment opportunities. A multidisciplinary approach is mandatory for a good outcome. Main findings of this review are represented by the consideration of physical, occupational and cognitive behavioral therapy as the first-line recommended options in the management of pediatric CPRS; pharmacological therapy can be utilized in failure cases. Unfortunately, the lack of a standard, less stressful and expensive protocol remains the main limit of the methodology. PhT often demands patient hospitalization and is reserved for selected subjects; the adverse events are common but considered as minor complications. Moreover, drugs or other treatments are not considered an alternative to the PT + CBT. To define proper diagnostic criteria, expert multidisciplinary committees as well as standard and commonly accepted guidelines and treatment protocol are essential and strongly encouraged. At the same time, the development of pilot studies for multicenter prospective trials could play a key role in the identification of more satisfactory treatments.

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

**Funding:** Received no external funding.

**Acknowledgments:** Thanks for funding "Open Access" to PIA.CE.RI. of University of Catania.

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

#### **Abbreviations**

Complex regional pain syndrome = CRPS; reflex sympathetic dystrophy = RSD; Preferred Reporting Items for Systematic Reviews and Meta-Analyses = PRISMA; Newcastle–Ottawa Quality Assessment Scale Cohort Studies = NOS; physical therapy = PT; cognitive behavioral therapy = CBT; pharmacological treatment = PhT

#### **Appendix A**

#### **Table A1.** Risk of bias assessment.

#### **References**

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Master Bedroom

People in high-density urban residential settings face social pressure in their living environments, so they prefer a particular space to remain alone when they return home [12]. A master bedroom's primary function is relaxation, rest and healthy sleep for adults or parents, where privacy is essential for confirming harmony in family life [12]. The definition of sleeping space changes over time and usually identifies a bedroom as one of the most private areas in a domestic environment, where freedom from excessive noise is desired [34]. It is a space where the occupant can take a step back from various in-house activities and enjoy privacy and noiselessness [48]. The bedroom also responds to human circadian rhythms [56]. According to Hendrassukma, the bedroom, for adults or parents, has the primary purpose of calming activities such as resting or sleeping, where the indoor environment strongly influences these activities [45].

The bedroom relates to occupants' feelings of comfort at home. Madsen explained that when a person becomes sick, the bedroom is the only homely spot in the domestic environment where the occupant feels comfort and relaxed. The author further described that suitable sounds and smells also make it a homelier place. If anyone changes the same bed to a different location, he/she may not feel the same comfort, with space security and belongingness influencing occupants' psychology [44]. This is because occupant habits such as lying, reading, relaxing, watching and other activities before going to sleep are closely correlated with existing bedroom scenarios. Consequently, the bedroom indicates an image of comfort, warmth and relaxation, with the bed as a prominent factor in that homely spot [44].

On the other hand, room shape and size influence the occupants psychologically in master bedrooms. A square room seems to be more crowded than a rectangle shape within an equal area. Not only that, but longitude direction may create vision rigidity and have a psychological impact [12]. According to Mridha, most developers generally prioritise making the master bedroom more comfortable and attractive than other domestic spaces within the apartment unit to attract local clients. In that case, the rooms' location, size and shape are essential to design factors in ensuring occupants' comfort in a master bedroom [57].

Indoor environmental elements, such as proper daylight, noise level, color and ventilation are essential for occupants' mental wellbeing in a master bedroom. According to Kennedy et al., noise at night in a bedroom is not desirable and another vital factor to consider for occupants' wellbeing. It is directly related to the quality of sound sleep. The author also recommended well-ventilated bedrooms for sleep health, whereas having a balcony creates extra facilities for the occupants' refreshment [67].

For daylight, window location is the most significant criterion for a bedroom. Not only the wall between the bedroom and balcony, but window louvers and height, and window glass type and opening system are correlated with ensuring natural ventilation, daylight and the external vision of a bedroom [41,57]. A room with outer vision seems more spacious than rooms without such an image—a window connected to the bedroom to guide people's sight [12]. Hendrassukma also mentioned that color significantly impacts occupants' psychology in personal spaces like a master bedroom [45].

Besides these, several studies emphasised the psychological comfort of familiar objects and pictures in bedrooms that may retell past events and positive memories. According to the literature, displaying memories and photographs is an integral part of visual relief and establishing personal spaces within the domestic setting. Bao also mentioned that the photos and mirrors on the wall could transfer vision and provide a sense of space expansion [12]. In personal spaces like bedrooms, occupants have different choices based on their sex, age and behavior. In general, women tend to beautify or decorate their private rooms more than men do in their domestic settings. Gosling elaborated that women have various choices, such as photos, lotions, jewelry, candles and others.

**Figure 8.** Relationship between spatial and environmental factors of master bedroom. (Illustration: Author, based on literature).

In contrast, men tend towards CDs, sports equipment and achievement-related items in personal spaces like bedrooms [68]. Finally, in 2015, Graham mapped occupants' desired psychological ambiance in their master bedrooms. According to the authors, romance, privacy, comfort, relaxation and love are the most prominent and frequent psychological ambiances of occupants for their master bedrooms [9]. The overall relationship between the spatial and environmental design factors of the master bedroom is illustrated in Figure 8.


*3.1. Phase Transformation*

Toughening phenomena related to phase transformation are well known for zirconiacontaining composites, where the phase transition from the tetragonal to monoclinic phase is the essential mechanism behind the enhanced toughness of zirconia used for the development of dental and orthopedic implants. This approach is based on stress induced phase transformations, which are mainly responsible for microstructural additional compressive stresses during the propagation of cracks, which increase the crack growth resistance *K*Ic.

Similar to precipitation hardening, the stabilization of particles in a metastable and thermodynamically unfavorable state requires overcoming an energy nucleation barrier. In this case, the modulation of particle volume can be achieved by the application of adequate tensile stress at the crack tip. Phase transformation is initiated by the presence of sufficiently large elastic energy. As the particle was metastable prior to the transformation, the decrease in stress due to an increase in volume does not hamper the process of transformation [201]. Moreover, compressive stress in radial direction and tensile stress in circumferential direction around a particle are superimposed to the external load during the transformation. These compressive residual stresses may result in the reduction of stress on the crack and hence may partially or completely close the crack. In addition, as the tensile stress is applied in circumferential direction around a particle it can generate microcracks, further leading to the dissipation of energy [202,203].

The stress induced transformation is also related to the free enthalpy reduction [204]. The addition of hydrostatic tensile stress strongly decreases the enthalpy of the phase with a larger volume. This, in turn, increases the driving force for the transformation, enabling the particle to overcome the nucleation barrier [205,206].

#### *3.2. Defect Size Reduction*

The main limitation of bioceramics' mechanical properties is related to their brittleness [207–209]. Ceramics generally exhibit higher compressive strength than tensile strength, essentially due to limitations in stress concentrations and crack propagation when micropores are flattened instead of dilated [210]. Bioceramics are characterized by very limited strain to failure and toughness, compared with ductile material counterparts (e.g., metals, polymers). Tensile stress could cause a fracture to propagate through the material, often causing failure in ceramic material.

Many defects can occur during the production, finishing, and application of ceramic because of the foreign particles, porous regions, or large grain sizes (Figure 3) [211].

**Figure 3.** Different types of processing-derived defects in bioceramics.

A great research effort has been dedicated to the design of ceramic microstructures with increased toughness and damage tolerance. In this context, the reduction of defect sizes can also be obtained by the incorporation of various ions such as nickel, silver, tantalum [212], and strontium [212–214].

Some requirements for reducing the size of defects include an efficient, fast, and reliable fine grinding, a compact design, and the versatility of the process [215].

The fracture mechanics of bioceramics is mainly affected by the powder particle size distribution. Grain size is usually tuned towards monomodal or multimodal distributions, in order to increase the packing density of particles [216]. It was reported that the largest grain may control the size of largest flaw [217]. Alternatively, grain size can be measured at the fracture origin [218]. The microstructure is affected by multiple factors (e.g., powder impurities, thermal treatments of powders, sieving size), complicating the possibility to understand the role of each factor [24]. This methodology has been in use for the production of ceramics to obtain a more homogenous microstructure [219–221].

#### *3.3. Crack Deflection*

The propagation of cracks in bioceramics is a critical issue that can cause sudden failure of large structures. Crack deflection can be used as a strategy to increase the toughness of bioceramics. Some local areas in the bioceramics exhibit low resistance to crack propagation, resulting in crack deflection [222]. In particular, when a crack is deflected, the surface of the crack increases, leading to more energy required for crack propagation and an increase of fracture toughness [221,223]. The prediction of a crack path as the crack approaches a fiber can be based on an energy criterion or a stress criterion [217,219,220,224–227]. Young's modulus mismatches are also reported as mechanisms of crack deflection [211]. The microstructural paths for crack propagation in bioceramics generally reflect the grain boundaries [211,217,228–230].

The evaluation of crack deflection by disks, rods, and spheres [217] showed that (1) increased toughness as a result of crack deflection is dependent on particle shape and volume fraction, and is independent of particle size [231]; (2) a rod-shaped morphology is the most effective, followed by disk and sphere, for increasing the toughness [232]. An increase in volume fraction of up to 20% increases the toughness, however, very little increase was observed with a higher volume fraction.

The toughness significantly increases when using rods compared to toughness without deflecting particles [226,233]. Another cause of crack deflection is partial bridging by grains, occurring when a grain/whisker causes the deflection of a crack around it, hence leaving the grain/whisker to bridge the crack [234].

Interactions between crack bridging and crack deflection in silicon nitride containing rod-shaped grains and whiskers toughened alumina were observed, demonstrating that crack deflection is crucial for the development of crack bridging [235].

#### *3.4. Microcrack Formation and Crack Branching*

Stress-induced microcrack formation and crack branching represent irretrievable deformation phenomena associated with energy dissipation [151,236]. Microcracks appear to debond at a poorly bonded matrix particle interface [237,238]. The stress energy near the tip of the fracture can result in the formation of micro cracks at weak areas in the bioceramic, for example, due to the undesired orientation of grain boundaries.

It was observed that a microcrack can decreased crack resistance at the macrocrack tip, which encourages crack progression [239]. The microcrack's effect on fracture propagation can be examined in two ways: energy dissipation owing to microcrack generation [170] and change in local stress intensity factor by simulating the interaction of microcracks with the main cracks [240,241]. The crack shielding phenomena has a role in microcrack toughening because of two aspects: the material's lower elastic modulus as a result of microcracking and the microcracking-induced dilatation [242].


Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides

*Omar F. Althuwaynee, Badal Pokharel, Ali Aydda and Sinan Jasim Hadi*

#### **Abstract**

 In this study, two unsupervised clustering techniques were used to generate meaningful training and testing landslide sample locations to produce landslide susceptibility map in north of Kathmandu Valley, Nepal. First, the landslide inventory data was prepared after enormous aftershock of magnitude 7.3 Mw on May 12, 2015. Secondly, unsupervised clustering algorithms (k-means, expectationmaximization (EM) using Gaussian mixture models (GMM)) were used in R programming environment to generate two samples of datasets. Additionally, a dataset was generated using random sampling and that for sampling comparison purpose. Later, eight terrain-based topographic conditioning factors were selected from freely available digital elevation model derivatives (slope, aspect, elevation, roughness, topographic wetness index (TWI), stream power index (SPI), area solar radiation (ASR), and curvature). Eventually, a total of three datasets was used to run logistic regression model to produce the landslide susceptibility map (LSM). Using uncertainty optimization, statistical tests (ANOVA, Wald test, tenfold cross validation, and ROC of AUC value) were performed. The unsupervised samples show more significant behavior over the unstable characteristic of the randomly selected samples. Gaussian mixture model (GMM) algorithm scored the highest in model performance and prediction capability. The findings present an alternative sampling strategy that significantly increases the efficiency and stability of the landslide susceptibility model.

**Keywords:** landslide prediction, unsupervised clustering, logistic regression, cluster, Nepal

#### **1. Introduction**

Earthquake-induced landslide is a significant natural hazard especially in mountain areas and contributes in mountain-scale erosional budgets as well as life losses [1, 2]. An earthquake occurred on April 25, 2015 that struck at Barkpak, Gorkha, at a magnitude of 7.8 Mw and triggered landslides, rockfalls, and avalanches [3] which blocked roads and dammed rivers [4]. The biggest aftershock of magnitude 7.3 Mw on May 12, 2015, triggered many landslides as well. The major shock and the aftershock sequences produced almost 25,000 landslides with an approximate area of 87 km2 , most landslides concentrating in the central Nepal through the

 whole slopes in the central Nepal [5, 6]. The landslide in Rasuwa district only covers 20.12 km2 area with volume of 0.08 km3 [5]. The most affected districts are Rasuwa, Dhadging, Nuwakot, Sindhupalchowk, and Gorkha [7]. The landslides and debris triggered by major shock caused loss of human lives, households, schools, and other infrastructures [4].

Methods of selecting training and model validation in landslide susceptibility mapping (LSM) using data mining approaches are crucial, especially to reduce the result uncertainty and to reach a better representation for landslide spatial distribution. LSM usually starts with collecting and processing previous or current landslide inventory followed by selection of deterministic or stochastic-based (clustering, regression, artificial intelligence, etc.) susceptibility mapping techniques and then map production and validation. In modeling phase, inventory is classified as training data samples used as dependent factors with the independent (conditioning) factors of landslides to be fitted, while the testing data samples is used as qualitative measure to evaluate the predictive power of the classification rule [8, 9].

 Studies have been made to analyze the earthquake-triggered landslide distribution and simple spatial correlation by [10–13] and others. Landslides generally occur in cluster pattern [14], and this pattern indicates the high density of the events occurring at a specific location [15]. In the case of earthquake-triggered landslides, events are clustered near and around the epicenters [11]. A hypothesis states that landslide events usually happened in a cluster manner and not randomly (spatially and temporally) especially in a study area that has a complex geological underneath. Therefore, spatial data clustering algorithms can be used to extract some logical representation of such clusters.

Machine learning algorithm which evolved from pattern recognition study can be used in landslide susceptibility assessment [16]. Regression and classification are two types of supervised learning. When desired output class or event is unknown, unsupervised learning is applied to study the patterns of the input data and group similar into a class [17]. Clustering and dimensionality reduction are widely used unsupervised learning. Logistic regression is considered one of the robust multivariate methods to generate landslide susceptibility models that have been examined and validated based on the performance and stability by many authors in landslide research field [8, 18–23].

In this study, the landslide inventory map prepared after a second major aftershock on May 12, 2015, is processed, and three inventory datasets were produced, two clustered-based datasets using k-means and expectation-maximization (EM) using Gaussian mixture models (GMM) and one random-based dataset in R programming environment. The efficiency in each dataset is checked using statistical sets to prepare the final LSM using logistic regression method.

#### **2. Study area**

The study lies in southern part of Rasuwa district, north of Kathmandu Valley, approximately 55 km east from the epicenter (**Figure 1**). It covers an area about 329 square kilometers with an elevation range of 540–4320 m. The number of aftershocks between April 25, 2015, and May 12, 2015, recorded by the National Seismological Center, in Rasuwa and Nuwakot, reached 19 and 22, respectively. In between this time frame, the epicenters for 15 aftershocks have been recorded in the study area with a magnitude lying in the range of 4.0–5.0 ML. The distribution of landslides seems high after the major shock in April 25, and the small numbers of additional landslides are observed after aftershock of 7.3 Mw in May 12 [6].

*Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides DOI: http://dx.doi.org/10.5772/intechopen.87836*

**Figure 1.**

*Map of study area showing major settlements and epicenters for aftershocks of Gorkha earthquake 2015 in between April 25 and May 12, 2015.*

The peak ground acceleration (PGA) in study area is found in the range of 0.48–0.76 g [24]. In the scenario of Gorkha earthquake, landslide densities have no direct correlation with modeled PGA [6]. The densities, however, correlate with several factors such as slope, PGA, surface downdrop, specific metamorphic lithologies, and large plutonic intrusions [25]. The land in the study area is mostly occupied by forest followed by cultivated land, grassland, shrubland, debris deposit, barren land, and water body. The study area is connected to Kathmandu, capital city, by Pasang Lhamu Highway which faced obstruction several times due to the landslides induced by the earthquake. Trishuli is the main river draining the area. There were many landslides along the Trishuli River valley on both sides of the river from Dandagaun to the border [4]. The landslides caused significant damage at headworks, desander, penstock pipe, and powerhouse of hydropower project at Mailung Khola [4]. As of now, there have been very limited studies in damages which happened due to the coseismic landslides in this area. In Langtang Valley, north of the study area, the most devastating landslide induced by this earthquake had occurred in which the debris avalanche buried several villages killing 350 people [25].

#### **3. Data**

 A geological map is prepared by digitizing the map published by the Department of Mines and Geology (DMG) in 2011 [26]. The information on settlements,

roads, and streams are taken from topographic maps. The spatial database for the aftershocks within a study area is prepared using data in the website of the National Seismological Center (NSC), and that of PGA is taken from United States Geological Survey (USGS) website. The extension of landslides with head scarp and displaced body was compared using Google Earth, and outliers were removed. We have used high-resolution satellite image taken after the major aftershock on May 12, 2015, for preparing landslide inventory. A total of 683 landslide polygons are prepared and verified in field as well as in Google Earth.

#### **4. Methodology**

 Training and testing sample data for landslide susceptibility analysis were generated using different cluster-based data selection techniques, and then we used one commonly used sampling technique (random selection) that to compare, assess, and justify the nobility of the clustering based techniques. Firstly, two datasets were generated from unsupervised clustering algorithms: k-means and expectationmaximization (EM) using Gaussian mixture models (GMM) in R environment. And then using the results of each cluster technique, we randomly extracted 70 and 30% out of total landslide inventory to be used later for susceptibility analysis model training and testing, respectively. To add more competitive value to the current research hypothesis, we prepared an additional inventory by using the common spatial random selection procedure [8, 23, 27] and that for comparison purposes and hypothesis proofing.

#### **4.1 Unsupervised clustering algorithms**

Unsupervised clustering algorithm, as a significant data mining technique, is a process of self-organization of data without any prior knowledge on the category to which a particular data belongs [28].

The main advantage of using unsupervised clustering algorithms is that it helps to acquire the better understanding and identification of similar patterns of data that can be placed into a specific category or class [17]. An attempt has been made to generate and compare seven different landslide susceptibility models from random-based distribution assumption and other assumption related to focus on the event pattern nature for analyzing the landslide distribution patterns and the consequences in the study area.

#### *4.1.1 K-mean clustering algorithm*

 In k-mean clustering, the dataset is partitioned into "k" number of groups or clusters as specified by the analyst [29] where the data within a cluster are as similar as possible [28]. First k points, known as centroids, for each cluster is defined where the centroids are the center of a cluster chosen randomly by k-mean algorithm. Each data is selected and its distance from the centroid is calculated. The data is assigned to that cluster where its distance to the centroid is shortest. K-mean algorithm attempts to minimize the sum of squares within the cluster as mentioned in Eq. (1):

$$J = \sum\_{j=1}^{k} \sum\_{i=1}^{n} \left\| \mathbf{x}\_{i}^{(j)} - \mathbf{c}\_{j} \right\|^{2} \tag{1}$$

 In the given formula, n is the number of objects to be clustered, k is the number of clusters, and ‖x( i j) − cj‖ 2 is the distance function that lies between x( i j) and centroid cj.

K-mean clustering is the reliable and fast algorithm for partitioning the larger datasets but is sensitive to the outliers [28].

#### *4.1.2 EM clustering using GMM*

 Gaussian mixture model is a parametric statistical model [30] in which each cluster to be modeled by Gaussian distribution is characterized by three parameters: mean vector (*μk*), covariance matrix (∑*k*), and an associated probability in the mixture [28, 31]. Expectation-minimization (EM) algorithm is widely used to determine the parameters of GMM for a given set of data [32]. For estimating the model parameters, each cluster is centered at *μk* means keeping the increased density for the data points near this mean, and the geometric feature (shape, volume, and orientation) for each cluster is determined using the covariance matrix ∑*k* [28]. Mclust implements maximum likelihood to fit different models using the covariance matrix parameterizations [33], and the best model is selected using Bayesian information criteria (BIC) [28, 33, 34]. The model with highest BIC score is selected as the best.

#### **4.2 Logistic regression**

Logistic regression (LR) builds multivariate regression relation between a dependent variable and different independent variables [35]. LR estimates the probability of an event occurring by applying maximum likelihood estimation after transforming the dependent variable into logit variable [35, 36]. The logistic model is given as

$$P = \frac{1}{1 + e^{-\mathfrak{a}}} \quad \cdots \tag{2}$$

where P is the probability of occurrence of landslide and varies from 0 to 1 on s-shaped curve and z varies from -∞ to +∞ and is described by the given equation

$$z = b\_0 + b\_1 \varkappa\_1 + b\_2 \varkappa\_2 + \dots + b\_n \varkappa\_n \tag{3}$$

where b0 is the intercept of the logistic model, n is the number of independent variables, b1 is the slope coefficient of the model, and x1 is the independent variables. Thus, the formed linear model from Eq. (3) is the logistic regression model that represents the presence or absence of landslides on the independent variables [37].

 The process for LR was carried out in R environment, and later the coefficients for each independent variable were imported to GIS to perform the final LSM using Eqs. (2) and (3).

#### **5. Results and discussion**

In k-mean clustering algorithm, the optimum number of clusters was obtained using the elbow method is 7. This method analyzes the total within-cluster sum of squares (WSS) as the function of number of clusters, and the bend indicates that additional clusters beyond it are insignificant and do not improve the total WSS [28]. A higher number of clusters reduce the error of grouping pattern, while in the current application, applying more than 10 clusters will not be practical and ends with a complex clusters pattern. To test the hypothesis of complex cluster pattern, **Figure 2** shows that in k-mean clustering, increasing the clusters to more than the recommended still reveals new groups.

In GMM, the clusters were modeled using Gaussian distribution having three parameters: mean vector, covariance matrix, and associated probability in the mixture. The result model was then fit using EM algorithm. Mclust is a contributed R package for model-based clustering, classification, and density estimation based on GMM [38]. Bayesian informative criterion (BIC) for 14 available model parameterizations was obtained (**Figure 3**). BIC is associated with the variability of the dataset, and the higher its value, the better the model is. On the x-axis, the number of the clusters obtained from the algorithm is present, and BIC value is on the y-axis. Based on model criteria, three top models were recommended (VVV, 7; EVV, 9; EVE, 8, with BIC values, −26628.78, −26647.97, −26651.12, respectively). Thus, VVV (ellipsoidal, varying volume, shape, and orientation) with seven components was selected by model automatically, and to enable the comparison applicability between the cluster techniques, we have to keep the number of clusters consistent (using 7 clusters). Moreover, taking in consideration the highest value of BIC, the number of cluster is seven which means adding further clusters will not increase the value of BIC or give the better model performance.

By separating the non-Gaussian distribution into a combination of finite number of Gaussian distribution on the basis of unsupervised learning without any a priori knowledge, GMM can be used to model an arbitrary distribution [39]. From 683 landslide data and 7 clusters, we got the subspace dimension d = 6 (**Figure 4a**). This result shows the maximal possible separation among the clusters. Further,

**Figure 2.**  *Clusters obtained from k-mean algorithm.*

**Figure 3.**  *GMM BIC.*

*Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides DOI: http://dx.doi.org/10.5772/intechopen.87836*

**Figure 4.**

*GMM classification and uncertainty obtained from EM algorithm: (a) GMM classification plot and (b) uncertainty boundary.*

the measure of uncertainty for associated GMM classification was also obtained from EM algorithm in **Figure 4b**. The points that could not fit into any of the seven clusters are given in this plot. The larger symbols represent the more uncertain observations [28]. When the clusters intersect, the uncertainty regions lie on the overlaps [40]. In this case, the landslides have mostly occurred within in the swath zone between two largest shocks of 7.8 and 7.3 Mw, which seem to have created the overlapping of the points among the clustering causing uncertainties.

Generalized linear models are used to generalize the classic linear models based on the normal distribution [41]. ANOVA is used to analyze the deviance for one or more generalized linear model fits, and it is similar to analysis of variance [42]. Smaller variances in conditioning factors reflect the higher likelihood in occurrence of landslides. Though this test gives the likelihood of a factor, it fails to give the predictive power of the model.

 For all clusters elevation has negative relation, whereas curvature, slope, SPI, aspect, and ASR have positive relation with the landslides. In contrary, TWI shows negative relation to landslide occurrence for k-means and positive for the remaining clusters. Likewise, roughness is negative for all other clusters. The areas with low TWI value suffered from more landslides due to this earthquake [23]. The low value of TWI indicates lower-order drainage that increases the instability [23]. Moreover, the dominant portion of landslides triggered by the earthquake intersects with the channel [6] which means there is significant correlation between drainage system and landslides. In this regard, SPI can be considered as an important influential factor. In general, the higher slope and higher relief are considered to be positively correlated with the landslide density [43].

Slope and elevation clearly stand out as the most influencing factors for all datasets. SPI, other than in EM cluster, is an influencing factor in the six datasets. SPI is a significant conditioning factor for landslides in study area due to its positive logistic regression coefficients and Pr(<Chi) value less than 0.05 in ANOVA table. The study area is drained by Trishuli, a major river in central Nepal which might be the reason for the instability of slopes during intensive shaking. Roughness shows its influence on landslides in k-means. TWI, curvature, and aspect are less significant for all clusters.

In most of the validation test, slope has occurred as the most influencing factor. The study area is dominated by the Lesser Himalayan rocks, and the observations

made by [25] show the high concentration of landslides in the slopes composed of metamorphic rocks of the Lesser Himalayan Sequence. Comparatively, few landslides occurred in moderate slope, and most of them were confined in the confluence of the steep slopes [6].

This might not indicate the direct relation of the slope with the landslide because the area of landslide concentration was in the transition from moderate slope to the steeper slope. Also, for the steeper slopes and high relief farther from fault plane, the number of landslides decreased [6]. The low relief and slope along with other

**Figure 5.**

*Variable importance for different datasets.*

#### **Figure 6.**

*Prediction rates: (a) prediction rate for original inventory; (b) prediction rate for EM; and (c) prediction rate for k-means.*

*Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides DOI: http://dx.doi.org/10.5772/intechopen.87836*

factors like the earthquake source, the fluvial connectivity, and the proximity of landslide to the direct connection with streams/rivers are high for these coseismic landslides [6] which might be the reason for significant correlation of SPI and TWI with the landslides. Therefore, slope along with the earthquake source characteristics, SPI and TWI, made the impact in triggering the landslides (**Figure 5**).

 K-fold cross validation was performed to assess accuracy of the model for training as well as testing data. The result in testing data helps to compare the model performance for different datasets (**Figure 6**). In addition, ROC curve was prepared to analyze success and prediction rate. Eventually, EM using GMM cluster has registered the highest accuracy in predicting landslide occurrence with accuracy of 95.76% followed by k-means with 95.75%. The lowest accuracy is for random sampling of original inventory (93.00 %). The kappa index is also high for EM which makes it a better model. EM using GMM cluster has the highest prediction rate (0.95) (**Figure 7**).

#### **Figure 7.**  *Comparison of accuracy and kappa index for different datasets.*

**Figure 8.**  *Landslide susceptibility map using dataset obtained from GMM.*

GMM modeling is one of the feasible tools for preparing landslide susceptibility model [44]. This is because GMM is feasible in the case of cluster covariance, unlike other algorithms. The LSM is prepared using the best model, that is, GMM (**Figure 8**).

The high susceptibility index can be observed in the transition of low and high elevation as well as near to the water body.

 The area has the metasedimentary rocks of the Lesser Himalaya, and PGA effects were higher for the Lesser Himalaya, and slope increases in the Higher Himalaya [6]. The rock strength for those near water bodies might be weak due to more erosivity than rock in steeper slopes. On this basis, we cannot take only slope or SPI as the cause of landslide occurrence. The physiographic transition, weathering degree based on the rock strength, effects of PGA with reference to the topography, and SPI and TWI all combined are the major influencing factor for the landslides triggered by this earthquake. In this earthquake, southward slope suffered more landslides than others [45]. In this regard, ASR can be considered to create similar impact like slope and aspect, as it is controlled by aspect and angle. Due to these controlling factors, roughness and curvature were not much significant.

#### **6. Conclusion**

 This research aimed to find and relate the best clustering patterns of earthquake-induced landslide locations of April 25 to May 12, 2015, and then use the resultant data pattern with topographic conditioning factors to produce LSM in north of Kathmandu Valley, Nepal. In this study, a novel sampling strategy was tested to increase the efficiency of the LSM rather than on merely random spatial classification. The obtained results allowed us to conclude: first, the applied unsupervised clustering sampling strategy shows more significant behavior over the random sampling, and EM using GMM cluster scored the highest accuracy in prediction of landslide occurrence, and second, all conditioning factors contribute to landslide occurrence in the study area, but SPI and TWI are significant. As for future work, the current findings will be tested and verified using another dataset with limited inventory; moreover, we encourage more research to be carried out to prove the model hypothesis in the case of rainfall-induced landslide susceptible areas.

#### **Acknowledgements**

The authors thank the Department of Mines and Geology (DMG), Nepal, for its kindness in making the geological data available.

*Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides DOI: http://dx.doi.org/10.5772/intechopen.87836*

#### **Author details**

Omar F. Althuwaynee1,2, Badal Pokharel3,4, Ali Aydda5 and Sinan Jasim Hadi2 \*

1 Department of Geoinformation Engineering, Sejong University, Seoul, Republic of Korea

2 Department of Real Estate Development and Management, Ankara University, Ankara, Turkey

3 Department of Geology, Tri-Chandra Multiple Campus, Kathmandu, Nepal

\*Address all correspondence to<EMAIL_ADDRESS>
© 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, provided the original work is properly cited.

#### **References**

[1] Dadson S, Hovius N, Chen H, Brian Dade W, Lin JC, Hsu ML, et al. Earthquake-driven increase in sediment delivery from an active mountain belt. Geology. 2004;**32**(8):733-736. DOI: 10.1130/G20639.1

[2] Hovius N, Meunier P, Lin CW, Hongey C, Chen YG, Dadson S, et al. Prolonged seismically induced erosion and the mass balance of a large earthquake. Earth and Planetary Science Letters. 2011;**304**(3-4):347-355. DOI: 10.1016/j.epsl.2011.02.005

[3] Chiaro G, Kiyota T, Pokhrel RM, Goda K, Katagiri T, Sharma K. Reconnaissance report on geotechnical and structural damage caused by the 2015 Gorkha earthquake, Nepal. Soils and Foundations. 2015;**55**(5):1030-1043. DOI: 10.1016/j. sandf.2015.09.006

[4] Shrestha AB, Bajracharya SR, Kargel JS, Khanal NR. The Impact of Nepal's Gorkha Earthquake-induced Geohazards. International Centre for Integrated Mountain Development (ICIMOD); 2015

[5] Martha T, Roy P, Mazumdar R, Govimarndharaj KB, Vinod Kumar K. Spatial characteristics of landslides triggered by the 2015 Mw 7.8 (Gorkha) and Mw 7.3 (Dolakha) earthquakes in Nepal. Landslides. 2017;**14**(2):697-704. DOI: 10.1007/s10346-016-0763-x

[6] Roback K, Clark MK, West AJ, Zekkos D, Li G, Gallen SF, et al. The size, distribution, and mobility of landslides caused by the 2015 Mw7.8 Gorkha earthquake, Nepal. Geomorphology. 2018;**301**:121-138. DOI: 10.1016/j.geomorph.2017.01.030

[7] Regmi A, Dhital M, Zhang J, Su LJ, Chen XQ. Landslide susceptibility assessment of the region affected by

the 25 April 2015 Gorkha earthquake of Nepal. Journal of Mountain Science. 2016;**13**(11):1941-1957. DOI: 10.1007/ s11629-015-3688-2

[8] Althuwaynee O, Pradhan B, Park HJ, Lee JH. A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena. 2014;**114**:21-36. DOI: 10.1016/j. catena.2013.10.011

[9] Brenning A. Spatial prediction models for landslide hazards: Review, comparison and evaluation. Natural Hazards and Earth System Science. 2005;**5**(6):853-862

[10] Rodríguez CE, Bommer J, Chandler R. Earthquake-induced landslides: 1980-1997. Soil Dynamics and Earthquake Engineering. 1999;**18**(5):325-346. DOI: 10.1016/ S0267-7261(99)00012-3

[11] Keefer DK. Landslides caused by earthquake. Geological Society of America Bulletin. 1984;**95**(4):406-421. DOI: 10.1130/0016-7606(1984)95<406:LCBE >2.0.CO;2

[12] Keefer KD. Statistical analysis of an earthquake-induced landslide distribution—The 1989 Loma Prieta, California event. Engineering Geology. 2000;**58**(3-4):231-249. DOI: 10.1016/ S0013-7952(00)00037-5

[13] Keefer KD. Investigating landslides caused by earthquakes—A historical review. Surveys in Geophysics. 2002;**23**(6):473-510. DOI: 10.1023/A:1021274710840

[14] Jarman D. Large rock slope failures in the highlands of Scotland: *Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides DOI: http://dx.doi.org/10.5772/intechopen.87836*

Characterisation, causes and spatial distribution. Engineering Geology. 2006;**83**(1-3):161-182. DOI: 10.1016/j. enggeo.2005.06.030

[15] Althuwaynee O, Pradhan B. An alternative technique for landslide inventory modeling based on spatial pattern characterization. In: Rahman A et al., editors. Geoinformation for Informed Decisions, Lecture Notes in Geoinformation and Cartography. Cham: Springer; 2014. pp. 35-48

[16] Marjanovic M, Bajat B, Kovacevic M. Landslide susceptibility assessment with machine learning algorithms. In: Proceedings 2009 International Conference on Intelligent Networking and Collaborative Systems 4-6 Nov. 2009. 2009. pp. 273-278

[17] Swamynathan M. Mastering Machine Learning with Python in Six Steps. Berkeley, CA; 2017

[18] Ayalew L, Yamagishi H. The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology. 2005;**65**(1-2):15-31. DOI: 10.1016/j.geomorph.2004.06.010

[19] Chau KT, Chan JE. Regional bias of landslide data in generating susceptibility maps using logistic regression: Case of Hong Kong Island. Landslides. 2005;**2**(4):280-290. DOI: 10.1007/s10346-005-0024-x

[20] Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Chang Ryu I, et al. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Natural Hazards. 2013;**65**(1):135-165. DOI: 10.1007/s11069-012-0347-6

[21] Lee S, Pradhan B. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 2007;**4**(1):33-41. DOI: 10.1007/s10346-006-0047-y

[22] Lombardo L, Mai PM. Presenting logistic regression-based landslide susceptibility results. Engineering Geology. 2018;**244**:14-24. DOI: 10.1016/j.enggeo.2018.07.019

[23] Shrestha S, Kang TS, Suwal KM. An ensemble model for co-seismic landslide susceptibility using GIS and random forest method. ISPRS International Journal of Geo-Information. 2017;**6**(11):365. DOI: 10.3390/ijgi6110365

[24] USGS, U. S. G. S. M7.8-36 km E of Khudi, Nepal [Internet]. 2015. Available from: http://earthquake.usgs.gov/ earthquakes/eventpage/us20002926

[25] Kargel JS, Leonard GJ, Shugar DH, Haritashya UK, et al. Geomorphic and geologic controls of geohazards induced by Nepal's 2015 Gorkha earthquake. Science. 2016;**351**(6269). DOI: 10.1126/ science.aac8353

[26] DoMa Geology. Geological Map of Central Nepal. Kathmandu, Nepal; 2011

[27] Althuwaynee O, Pradhan B, Lee S. Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences. 2012;**44**:120-135. DOI: 10.1016/j.cageo.2012.03.003

[28] Kassambara A. Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning. USA: STHDA; 2017

[29] MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1(14). University of California Press; 1967. pp. 281-297

[30] Shental N, Bar Hillel A, Hertz T, Weinshall D. Computing Gaussian mixture models with EM using equivalence constraints. In: Sebastian T, Lawrence S, Bernhard S, editors. Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press; 2004

[31] Reynolds DA, Rose RC, Smith MJT. A mixture modeling approach to text-independent speaker ID. The Journal of the Acoustical Society of America. 1990;**87**(S1):S109-S109. DOI: 10.1121/1.2027823

[32] Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological). 1977:1-38. Available from: https://www.jstor.org/ stable/2984875?seq=1#metadata\_info\_ tab\_contents

 [33] Fraley C, Raftery A, Murphy T. Scrucca L:MCLUST Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. USA: Department of Statistics University of Washington; 2012. Available from: http://www.stat. washington.edu/mclust/

[34] Schwarz G. Estimating the dimension of a model. Annals of Statistics. 1978;**6**(2):461-464

[35] Atkinson P, Massari R. Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy. Computers & Geosciences. 1998;**24**(4):373-385. DOI: 10.1016/ S0098-3004(97)00117-9

[36] Dai FC, Lee CF. Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology. 2002;**42**(3-4):213-228. DOI: 10.1016/ S0169-555X(01)00087-3

[37] Bai S, Wang J, Lu GN, Zhou PG, Hou SS, Xu SN. GIS-based and logistic regression for landslide susceptibility mapping of Zhongxian segment in the Three Gorge area, China. Geomorphology. 2010;**115**(1-2):23-31. DOI: 10.1016/j.geomorph.2009.09.025

[38] Scrucca L, Fop M, Murphy TB, Raftery A. Mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal. 2016;**8**(1):289

[39] Qiu L, Yuan S, Mei H, Fang F. An improved Gaussian mixture model for damage propagation monitoring of an aircraft wing spar under changing structural boundary conditions. Sensors. 2016;**16**(3):291. DOI: 10.3390/ s16030291

[40] Fraley C, Raftery AE. Enhanced model-based clustering, density estimation, and discriminant analysis software: MCLUST. Journal of Classification. 2003;**20**(2):263-286. DOI: 10.1007/s00357-003-0015-3

[41] Lindsey KJ. Applying Generalized Linear Models. New York: Springer Science & Business Media; 2000

[42] Hastie TJ, Pregibon D. Generalized linear model. In: Chambers JM, Hastie TJ, editors. Statistical Models in S. London: Chapman and Hall; 1993. pp. 209-247

[43] Lu N, Godt J. Hillslope Hydrology and Stability. Cambridge University Press; 2013. pp. 1-453

[44] Timonin V, Bai SB, Wang J, Kanevski M, Pozdnukhov A. Landslide data analysis with Gaussian mixture model. In: International Congress on Environmental Modelling and Software, Barcelona, Spain. 2008

[45] Gnyawali K, Adhikari B. Spatial relations of earthquake induced

*Perceptual Structure and Pattern Modeling of Aftershock-Induced Landslides DOI: http://dx.doi.org/10.5772/intechopen.87836*

landslides triggered by 2015 Gorkha Earthquake Mw=7.8. In: Mikoš M, Casagli N, Yin Y, Sassa K, editors. Advancing Culture of Living with Landslides. WLF 2017. Cham: Springer; 2017. pp. 85-93

**637**

**Chapter 51**

**Abstract**

Comparing the Accuracy of

on Hourly and Daily Mean

Outdoor Temperature

*Merve Kuru and Gülben Çalış*

the energy prediction models.

**1. Introduction**

commercial buildings, regression analysis

Energy Prediction Models Based

It is well known that outdoor temperature highly effects energy consumption in buildings. Accordingly, outdoor temperature is an important parameter for constructing energy prediction models, however; the effect of using data with different time-intervals on the accuracy of models needs to be investigated. This chapter aims at investigating the impact of hourly and daily disaggregated data on the performance of energy models. Data were collected between January and December, 2015 from a commercial building located in Saint-Quentin-en Yveline, France. The daily and hourly HVAC electricity consumption were modeled based on daily mean and hourly outdoor temperature, respectively. The results show that the correlation between daily mean outdoor temperature and daily HVAC electricity consumption is stronger compared to the model based on hourly disaggregated data. Moreover, the correlation coefficient between daily HVAC electricity consumption and daily mean outdoor temperature was obtained as 0.82, whereas it was 0.70 between hourly HVAC electricity consumption and hourly outdoor temperature. The results indicate that hourly disaggregated data does not necessarily improve the accuracy of

**Keywords:** energy prediction models, outdoor temperature, disaggregated data,

Electrical energy used for heating and cooling in buildings, businesses, and industry consume around half of the energy used in the European Union [1]. Therefore, understanding and predicting the energy consumption of heating, ventilating, and air-conditioning (HVAC) systems are beneficial for energy engineers, electricity providers, end-users, and policy makers in addressing energy sustainability challenges, the expansion of distribution networks, energy pricing, and policy development. In order to have accurate prediction models, the most influencing factors have to be included in the models [2]. Many researchers state that the outdoor temperature is an important factor that effects the HVAC energy consumption in buildings. Wu et al. [3] indicated that the outdoor temperature has a significant effect (R value = 0.905), whereas solar radiation has little somewhat

#### **Chapter 51**


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