**Drug Eluting Balloon**

S. Sharma1, N. Kukreja2 and D. A. Gorog2,3 *1Frimley Park Hospital NHS Trust,*

*2East and North Hertfordshire NHS Trust, 3Imperial College, London UK*

#### **1. Introduction**

126 Coronary Interventions

Turco, M.A.; Buchbinder M.; Popma, J.J.; Wissman, N.J.; Mann, T.; Doucet, S.; Johnson, W.L.;

Vermeersch, P.; Agostoni, P.; Verheye, S.; Van der Heuvel, P.; Convens, C.; Bruining, N.;

Vlodaver, Z . &Edwards, J.E. (1973). Pathologic analysis in fatal cases following saphenous vein coronary arterial bypass. *Chest*, Vol 64, No.5, (November 1973), pp. 555-563. White, J. (2006). TCT 2005 Late-breaking trials promise to influence practice patterns. *J*

Trial. *J Am Coll Cardiol*, Vol. 50, No. 3, (July 2007), pp. 261-267.

*Interven Cardiol*, Vol 19, No. 1, (February 2006), pp. 113-116.

*Catheter Cardiovasc Interv,* Vol 68, No 3, (September 2006), pp. 379-388. Vermeersch, P.; Agostoni, P.; Verheye, S.; Van der Heuvel, P.; Convens, C.; Bruining, N.;

(March 2011), pp. 300-309.

(December 2006), pp. 2423-2431.

Vein Grafts. The Randomized BARRICADE (Barrier Approach to Restenosis: Restrict Intima to Curtail Adverse Events) Trial. *J Am Coll Cardiol Intv*, Vol. 4, No. 3,

Greenberg, J.D.; Leadley, K. & Russell,M.E. (2006). Pivotal, randomized U.S. study of the SymbiotTM covered stent system in patients with saphenous vein graft disease: eight-month angiographic and clinical results from the Symbiot III Trial.

Van den Branden, F. & Van Langenhove, G. (2006). Randomized double-blind comparison of Sirolimus-eluting stent versus bare-metal stent implantation in diseased saphenous vein grafts. Six month angiographic, intravascular ultrasound, and clinical follow-up of the RRISC Trial. *J Am Coll Cardiol*, Vol. 48, No. 12,

Van den Branden, F. & Van Langenhove, G. for the DELAYED RRISC (Death and Events at Long-term follow-up AnalYsis: Extended Duration of the Reduction of Restenosis In Saphenous vein grafts with Cypher stent) Investigators. (2007). Increased late mortality after Sirolimus-eluting stents versus bare-metal stents in diseased saphenous vein grafts. Results from the randomized DELAYED RRISC

Since the first percutaneous transluminal coronary angioplasty (PTCA) performed by Andreas Gruntzig in 1977 the technology has evolved significantly. Progress of PTCA has seen the development of many devices, some of which are still in use and many others that have fallen in disuse. The main limitation of the plain old balloon angioplasty (POBA) was the problem of elastic vascular recoil causing abrupt vessel closure and restenosis. The patho-mechanism of restenosis that occurs following balloon angioplasty involves negative vascular remodeling, elastic recoil and thrombosis at the site of injury {Moreno, 1999}. While the thrombus formation can be reduced by use of antiplatelet drugs, the restenosis threat remains. Early restenosis occurred in as many as 30% of angioplasty cases. This led to the development of the metal stent to exert radial force on the vessel wall and thus prevent elastic recoil. Although stents reduced restenosis, their use led to the realisation of a different and new challenge of in stent restenois (ISR). This occurs mainly due to neointima formation {Mach, 2000,Mudra et al, 1997, Hoffman et al, 1996, Kearney et al, 1997} that is principally composed of proliferating smooth muscle cells (SMC) and extra cellular matrix {Geary et al, 2003, Grewe et al, 1999}. By the late 1990s, it was acknowledged that although the incidence of ISR was lower than that of restenosis following balloon angioplasty {Serruys et al, 1991}, it occurred in 15–30% of patients, and possibly more frequently in certain subgroups {Holmes et al, 2002}.

#### **2. Treatment of restenosis**

Over the years there have been intensive research efforts to identify possible pharmacotherapeutic regimens to prevent the neointimal restenotic process. Although most experimental studies and some small initial clinical studies showed promise, subsequent large randomized trials have been disappointing {Faxon, 1995, Bertrand et al, 1997, Boccuzzi et al, 1998, Serruys et al, 2000, Faxon, 2002}. Failure to achieve significant reduction in ISR with systemic drug therapy led to the exploration of the concept of local drug delivery. Local drug delivery (LDD), in theory, should achieve greater local drug concentration with lower overall dose compared to systemic therapy, to help achieve maximal tissue effects while minimizing undesired systemic toxicity. It also has the advantage of being able to utilize drugs with low systemic bioavailability or short half-life. Many devices have been

Drug Eluting Balloon 129

folded balloon, which is homogenously coated with paclitaxel embedded in contrast medium coating. Paclitaxel (3 μg/ 373mm2 balloon surface) is the pharmacologically active substance whereas the contrast medium has a matrix builder function to facilitate

Fig. 1. Photograph of SeQuent (top) and the paclitaxel coated SeQuent® Please balloon

The ideal drugs for local delivery should be lipophilic in nature, rapidly adsorbed and have a high retention rate by the vessel intima, in order to exert maximal beneficial effects {Baumbach et al, 1999}. Paclitaxel is a lipophilic drug and bind tightly to various cell constituents {Rowinsky et al, 1995}, resulting in effective local retention at the site of delivery { Creel et al , 2000} and it exerts a long-lasting effect in the cell due to structural alteration of the cytoskeleton. Thus, paclitaxel, with its lipophilic nature, combined with the fact that adding a small amount of hydrophilic contrast medium {Scheller et al, 2003} enhances its solubility, makes it well suited for delivery on a drug-delivery balloon. Various other drugs like, sirolimus, zotarolimus, rapalog and others are being studied currently as a possible alternative to paclitaxel for coating the PTCA balloon {Schnorr et

The first preclinical study was conducted by Scheller et al {Scheller et al, 2004}. In this study stainless steel stents (n = 40; diameter: 3.0–3.5 mm; length: 18 mm) were implanted in the left anterior descending and circumflex coronary arteries of pigs. Both conventional uncoated and three different types of paclitaxel-coated coronary angioplasty balloons were used, and contact with vessel wall was maintained for 1 min. The results were assessed by quantitative angiography and histomorphometric studies of the stented arteries. There was a marked reduction (up to 63%) of parameters characterizing ISR in the paclitaxel-coated balloon group, without evidence of increased inflammation in proximity to the stent struts or any effect on re-endothelialisation of the struts. They also showed that paclitaxel-coated balloons lose only 6% of the drug when introduced into the coronary circulation and retracted without inflation. Approximately 80% of the drug is released during inflation, suggesting

catheter (bottom).

al, 2010}.

**3.1 Pre- clinical data**

immediate release of drug during balloon inflation {Scheller & Speck, 2009}.

developed to administer drugs or genetic material locally to the site of injury {Sharma et al, 2011}. Studies have shown that local administration of pharmacologic agents directly at the site of coronary intervention is an effective means of delivering sufficient amount of drug into the injured arterial tissue site to cause an anti-restenotic therapeutic effect { Schwartz et al, 2004}.

Various approaches for local drug delivery have been tried including nanoparticles, contrast media and drug delivery balloons, such as porous {Herdeg et al, 2000} and double balloons {Oberhoff et al, 2001}. Other options for treating ISR included POBA using either conventional or cutting balloons, implanting a stent inside the stent, rotablation or brachytherapy. However, most of these techniques were not adopted into widespread clinical use due to their various shortcomings and limitations. Until recently, stent-based local drug delivery using drug-eluting stents (DES) is still considered the percutaneous treatment of choice for coronary restenosis. As the process of neo intimal hyperplasia occurs locally due to endothelial injury caused by the metallic stent, it seems logical to use the stent itself to deliver a drug locally in order to overcome this problem.

Thus DES have become the mainstay of intervention for coronary atherosclerotic disease {Kirtane et al, 2009}. However, DES use is limited by small but unpredictable risk of late stent thrombosis due to withdrawal of antiplatelet therapy { McFadden et al, 2004}, delayed mal-apposition {Kozuma et al, 1999}, delayed vascular healing as a result of initial antiproliferative effect {Jakabcin et al, 2008}, or a hypersensitivity reaction to the drug, polymer coating or both { Finn et al, 2007}.

ISR continues to occur with DES, although at a lower rate compared to BMS. However, the relative massive increase in number of DES implantations in recent years means the problem of ISR although less in relative terms compared to BMS, still causes a problem in absolute terms requiring a significant number of repeat procedures every year globally and remains a treatment challenge {Maisel, 2007}. Treatment of restenosed DES with a second DES is associated with risk of subsequent restenosis of up to 43% {Lemos et al, 2008}. The ideal treatment of a coronary stenosis would eliminate both the stent and the polymer related late problems, while at the same time deliver an antiproliferative agent to reduce the risk of restenosis.


**14. Conclusion**

To sum up, the application of the relationship-based approach in this study is strengthened by the existing study in destination branding which highlighted there is limited or no empirical studies in the relationship building approach in developing a destination brand [45].


**5. Technical equipment for research of biological effects of EM field**

In present time four research institutions here in the Czech Republic run research projects focused on studies of interactions between EM field and biological systems. These institutions are technically supported by Dept. of EM Field of the Czech Technical University in Prague. In this contribution we would like to give more details about that projects and obtained technical results (i.e. description of developed exposition systems).

Three of discussed projects (1 in Germany and 2 here in Czech Republic) are basic research for simulation of the microwave hyperthermia treatment. Other two projects (both in Czech Republic are focused on simulation of the case of exposition by mobile phone.

In the modern view, cancer is intended as a complex illness, involving the cells that undergo to transformation, their environment, and the general responses at biochemical and biological levels induced in the host. Consequently, the anti-cancer treatment protocols need to be multi-modal to reach curative effects. Especially after the technical improvements achieved in the last 15 years by bio-medical engineering, microscopy devices, and molecular biology methods, the combinations of therapeutic procedures are growing in interest in basic and clinical research.

The combination of applied biological research together to the physical sciences can offer important perspectives in anticancer therapy (e.g. different methodologies and technical devices for application of energies to pathological tissues).

The modern bioengineering knowledge applied to traditional tools, as the microscopy, has largely renewed and expanded the fields of their applications (e.g.: in vivo imaging), pushing the interest for direct morpho-functional investigations of the biomedical problems.

#### **5.1 Waveguide applicator**

Very good results of EM field expositions in biological experiments can be obtained by simple but efficient waveguide applicators see example in Fig. 19. Waveguide offer a very big advantage – in approximately of fifty percents of its aperture the irradiated electromagnetic field is very near to a plane wave, which is basic assumption for good homogeneity of the heating and optimal treatment penetration.

Here described system is being used (shared) for research projects by two institutions (Institute of Radiation Oncology in Prague and Institute of Microbiology of the Czech Academy of Sciences).

Fig. 19. Waveguide applicator for biological experiments

Prospective Applications of Microwaves in Medicine 523

Considering the necessary effective heating depth for the planned experiments, we have found 915 MHz to be suitable frequency. As an excellent compatibility of the applicator with non-invasive temperature measurement system (ultrasound or NMR) is a fundamental condition for our project, we should have to use non-magnetic metallic sheets of minimised dimensions to create the conductive elements of the applicator. Therefore the applicator itself (see Fig. 22.) is created by two inductive loops tuned to resonance by capacitive elements (Vrba, 1993). Dimensions of these resonant loops were designed by our software, developed for this purpose. Coupling between coaxial feeder and resonant loops (not shown in Fig. 22.) as well as a mutual coupling between resonating loops could be adjusted to

a) b) Fig. 22. a) Arrangement of discussed microwave hyperthermia applicator, b) Photograph of

Resonant loops

The position of the loops is fixed by perspex holder. There is a special cylindrical space for experimental animal in lower part of this perspex holder. As the heated tissue has a high dielectric losses, both loops are very well separated and so no significant resonance in heated area can occur. From this follows, that either the position of the loops with respect to

First measurements to evaluate the basic properties of the discussed applicator were done

heated area or the distance between the loops is not very critical.

Fig. 21. Temperatures during experiments

optimum by microwave network analyser.

Treatment area

Area for experimental animal

the discussed applicator

reflections),

on agar phantom of muscle tissue:

Aperture of this waveguide is 4.8 x 2.4 cm and it is excited at frequency 2.45 GHz. Effective heating is in the middle of the real aperture – its size is approximately 2.4 x 2.4 cm. Waveguide is filled by teflon to reduce its cut-off frequency. Power from generator is possible to control from 10 to 180 W, in these experiments we work between 10 and 20 W mostly.


**Abstract**

Sleep disorders are closely intertwined with different kinds of headache disorders. In some forms of headaches, this association is profound, such as in hypnic headache, where headaches only occur during sleep, or in cluster headache, which has connection to the REM sleep. In other headaches, the association with sleep is more subtle, but nevertheless, very relevant–for instance in migraine, where sleep deprivation or excessive sleep may act as a trigger for migraine, while sleep has a curative effect on the migraine attack. This chapter focuses in the relationship between sleep disorders and headaches focusing on the five primary forms of headaches: migraine, tension-type headache, paroxysmal hemicrania, hypnic headache, and secondary form of headaches such as obstructive-sleep-apnea-related headaches and medication overuse headaches (MOH).

**Keywords:** sleep, headaches, migraines, insomnia, sleep deprivation

## **1. Introduction**

The association between headaches and sleep is well known for more than 100 years [1]. Some headaches emerge from specific sleep stages, other headaches originate from nonspecific sleep stages, and finally some are triggered by insufficient sleep or over-sleeping and are relieved by sleep. Changes in sleep pattern is a known factor that predisposes and perpetuates headaches in an acute or chronic fashion. Sleep disorders are associated with both, primary headache syndromes such as migraines, tension-type headaches, cluster headaches, hypnic headaches, and secondary headache disorders such as headaches-on-awakening, and MOH [2]. Primary headaches are headaches where pain and associated features such as nausea or light sensitivity are the illness, and secondary headaches are headaches that result from an underlying medical disorder. The number of sleep disorders associated with headaches is also vast, such as OSA, insomnia, RLS, etc. [3]. In general, at times, more than one type of sleep disorder may coexist in a single patient, and the same applies to headache disorders.


**3. Optimization Model**

The focus of this article is on the round-trip carsharing service, which received relatively less attention in the research community [52]. Hence, a novel approach for round-trip carsharing fleet placement is proposed. There are three benefits that the approach offers. The first benefit is that automation of the fleet placement process removes the need for traditional manual allocation. The second benefit is the higher placement precision with the inclusion of a Geographic Information System (GIS). Finally, the third benefit is in proposing an approachable fleet management problem to the research community that may also be beneficial to similar applications.

The proposed approach emphasizes the user coverage and ease of access with a constraint of being applicable to the real-world scenario by utilizing the two components mentioned below. These two goals are of real concern in practice and are often expressed by experts in this area.

In this section, the graph instance used in this article is first defined for the FPP since the graph instance is closely related with the problem definition. Second, the Fleet Placement Problem (FPP) model is formulated. Finally, FPP is proven that it is an NP-hard in a strong sense.

#### *3.1. Graph Instance Definition*

The street map can be modeled as an undirected weighted graph to represent users' ability to walk on the streets in both directions where they need to pick up or leave the shared vehicle (see Figure 1). The street-level graph is modeled as follows:

$$G = (V, E, P, W).$$

The set of nodes *V* is composed of two subsets: *V* = *S* ∪ *B*, where *S* is the subset of street nodes (i.e., nodes on roads and streets), while subset *B* contains buildings. Both *S* and *B* are of type node, hence they are naturally members of vertices in *G*.

Buildings contain users. A weight *pi* ∈ *P* associated with each node *bi* ∈ *B* corresponds to the estimation of the number of people living in this building. The weight for each node in *S* is set to zero, making the assumption that target users are only located in buildings. This is similarly to the study taken by Daniels and Mulley [53]).

The set of edges *E* consists of two subsets: *E* = *R* ∪ *L*, where *R* is a set of streets/roads, and *L* is a set of links connecting residential buildings to nearby streets.

Each edge (*u*, *v*) ∈ *R* is valuated by a weight, *wi* ∈ *W*, representing the walking distance from *u* to *v* where *u*, *v* ∈ *S*.

Each edge (*u*, *b*) ∈ *L* where *u* ∈ *S* and *b* ∈ *B* is valuated by 0. In other words we consider the distance between the building and its nearest adjacent street is considered negligible. This process is called "snapping" and is common in every routing service where the starting point is first projected on a road before starting to build a route [54].

**Figure 1.** An illustration of a graph instance.

#### *3.2. Fleet Placement Problem*

The concept of a virtual station is created in this work, In the FPP model, carsharing stations are placed on the streets. A station is a virtual area on the city map defined by two elements, a center point, and a radius. Figure 2 illustrates this concept. A car is placed on a road which depicts a center point. A circle represents a radius of that center point. These two elements constitute the virtual station. A car can be picked up and returned to anywhere in that circle (station area). The coverage of a station is determined by the given maximum walking distance illustrated as a green line in the figure.

Due to the round-trip nature of the service, each car taken from the station needs to be returned to this station after completing the trip. The typical customers are people in residential areas (as shown in Figure 2) covered by the carsharing stations, who walk to pick up the nearest vehicle. Under the aforementioned assumptions and constraints, FPP can then be formulated.

**Figure 2.** An illustration of a virtual area of a station on streets.


**1. Introduction**

14 Neuroendocrinology and Behavior

Fail 2008;10:60–62.

Dynamics 2007,236:3309-3320.

Exp. Opinion Biol. Ther. 2007, 499-514.

blood. StemCells 20.0.8,26,20.83-20.92

[5] Koch AM, Zink S, Singer H, Dittrich S. B-type natriuretic peptide levels in patients with functionally univentricular hearts after total cavopulmonary connection. Eur J Heart

[6] Kolcz J, Tomkiewicz-Pajak L, Wojcik E, Podolec P, Skalski J. Prognostic significance and correlations of neurohumoral factors in early and late postoperative period after Fontan

[7] Kucia M, Wu W, Ratajczak MZ Bone marrow-derived very smali embryonic like stem cells (VSEL) – their developmental origin and biologica/ signijicance. Deve/op.

[8] Kucia M, Zuba-Surma E, Wysoczynski M, Wu W, Ratajczak J, Ratajczak MZ Adult marrow-derived vety small embryonic-like stem cells (VSEL SC) and tissue engineering

[9] Wojakowski W, Tendera M, Kucia M, Zuba-SurmaE, Paczkowska E, Ciosek J, Ha/asa M, Król M, Kaźmierski ,Ocha A, Ratajczak J, Machaliński B, Ratajczak MZ Mobilization of Bone Marrow-Derived Oct-4+SSEA-4+ Vely Smali Embryonic-Like Stem Cells in

[10] Kucia M, Wysoczynski M, Wan W, Zuba-Surma EK, Ratajczak J, Ratajczak MZ Evidence that very small embryonic like (VSEL) stemcells are mobilized intoperipheral

[11] Kucia M, Ratajczak J, Ratajczak MZ. Bone Marrew as a SOUlU oj Circulating CXCR4

[12] Kucia M, Dawn D, Hunt C, Wysoczynski M, Majka M, Ratajczak 1, Rezzoug F. lldstad ST, Bolli R, Ratajczak M.Z Cells expressing markers of cardiac tissuecommittedstemcells reside in the bonemarrow and are mobilized into peripheral

Patients with Acute myocardial infarction . J Am Col Cardiol. 20.09,53, 1-9

bloodfollowing myocardial infraction. Cir Research 20.04, 95, 1191- 1199.

Tissue Commilled Stem Cells (TCSC). Bio!. Celi 2005,97, 133-/46.

procedure. Interact Cardiovasc Thorac Surg. 2011 Jul;13(1):40-5.


**Abstract**

This surgical field has now progressed and becoming an established subspecialty involving various surgical disciplines worldwide. Various complex CMF syndromes reported in syndromic craniosynostosis include Crouzon, Apert and Pfeiffer syndromes. These syndromes carry specific functional discrepancies associated with the affected structural anomaly and may therefore have functional issues involving the brain, eye and airway among others. As corrective surgery is often indicated depending on the affected vital functions, other factors that need to be considered are patient's age, comorbidities, urgency, available expertise and patient's overall prognosis based on the degree of anomaly. As such, the corrective surgery can be categorized into; (1) intermediate which is performed at an early phase and aimed to improve or salvage important vital functions such as the brain, eye, airway or feeding which are important for the child's development and, (2) definitive treatment aimed at permanently correct the functional discrepancies. Intermediate corrective surgery may include invasive procedures such as ventriculo-peritoneal (VP) shunts, tarsorrhaphy, adenotonsillectomy and tracheostomy whereas definitive corrective surgery may include surgical procedures such as monobloc, Le Fort III osteotomy, posterior cranial vault expansion and mandibular advancement. This chapter will elaborate on the indications, types, challenges in the management and the proposed prevention measures in corrective surgery for specifically for syndromic craniosynostosis patients.

**Keywords:** corrective surgery, syndromic craniosynostosis, craniofacial surgery


*Review* **Biologically Inspired Collagen**/**Apatite Composite Biomaterials for Potential Use in Bone Tissue Regeneration—A Review**

### **Barbara Kołodziejska, Agnieszka Kaflak and Joanna Kolmas \***

Chair of Analytical Chemistry and Biomaterials, Department of Analytical Chemistry, Medical University of Warsaw, ul. Banacha 1, 02-097 Warsaw, Poland<EMAIL_ADDRESS>(B.K<EMAIL_ADDRESS>(A.K.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +48-22-5720755

Received: 7 March 2020; Accepted: 7 April 2020; Published: 9 April 2020

**Abstract:** Type I collagen and nanocrystalline-substituted hydroxyapatite are the major components of a natural composite—bone tissue. Both of these materials also play a significant role in orthopedic surgery and implantology; however, their separate uses are limited; apatite is quite fragile, while collagen's mechanical strength is very poor. Therefore, in biomaterial engineering, a combination of collagen and hydroxyapatite is used, which provides good mechanical properties with high biocompatibility and osteoinduction. In addition, the porous structure of the composites enables their use not only as bone defect fillers, but also as a drug release system providing controlled release of drugs directly to the bone. This feature makes biomimetic collagen–apatite composites a subject of research in many scientific centers. The review focuses on summarizing studies on biological activity, tested in vitro and *in vivo*.

**Keywords:** collagen; hydroxyapatite; biomimetic material; scaffold; bone regeneration; biocomposite


**5. Conclusions**

In this article, the **IEGBNDS** algorithm via Newton-Raphson's method and general Bischi-Naimzadah duopoly system (**GBNDS**) has been suggested. Newton-Raphson's method has been used for shuffling the rows/columns of the plain image. **GBNDS** has been used to producing chaotic sequences to diffusion phase of image encryption algorithm. The extracted chaotic sequences from the **GBNDS** is extremely random based on the NIST statistical tests. Many security experiments are applied to evaluate the efficiency of our algorithm. The **IEGBNDS** algorithm has a large key space (1014(*k*+1) + 10168(>>2100), the histograms of the generated cipher images are close to the uniform distributions, all entropy values for the cipher images based on **IEGBNDS** algorithm are close to 8, all correlation coefficient values for the cipher images are close to zero. The **IEGBNDS** algorithm outperforms some recent algorithms at least in one of the two measures, highly sensitive to small changes of the secret key, can be robust against the noise and data loss attacks, and can hold out against the plaintext attacks. In comparison to several recent algorithms, the **IEGBNDS** algorithm has a small running time. NIST statistical tests for 100 cipher images by the **IEGBNDS** algorithm are performed and all tests are passed. Finally, quantum image encryption algorithm based on **GBNDS** will be designed in the future to increase the security of the current algorithm.

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

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data sharing not applicable.

**Acknowledgments:** I deeply thank Shehzad Ahmed for his proof reading the paper.

**Conflicts of Interest:** The author declares no conflict of interest.


**3. Geology of the study area**

The study area is under laid by the rock formations ranging from Archaean to Permo-carboniferous age (**Table 1**). The rock formations include quartz-mica-schist, banded-biotite-hornblende-gneiss/granite, and coarse-grained sandstone (**Figure 2**). Major part of the study area is occupied by quartz-mica-schist. The second dominant rocks in the study area are biotite-hornblende granitic/gneisses with migmatite and pegmatite patches, which are observed from the northern part of the study area. A small portion in the eastern and southern parts is covered by coarse-grained sandstones [11]. A very small area in the eastern part is occupied by gabbro. Mineralogically, quartz-mica-schist is composed principally of quartz and mica, and generally of muscovite and biotite. The modal composition of quartz-mica schist shows 25% of muscovite, 15% biotite, 30% orthoclase, 20% quartz, and 10% plagioclase and chlorite, together with some opaque minerals [12]. The biotite-hornblende granitic/gneisses appear in their light and dark color banded texture. The light color band is composed of quartz and plagioclase feldspar, while the dark color is composed of biotite, hornblende, and opaque's. The modal composition of biotite-hornblende granitic/gneisses have 34.25% of quartz, >3.75% k-feldspar, 20.35% of plagioclase, 35% of biotite,

#### **Table 1.**

*Geological succession of the study area [10].*

**Figure 2.** *Geological Map of Study area.*

5.2% hornblende, 0.5% chlorite,0.15% of sphene, 0.15% of zircon, 0.3% of epidote and 0.10% fluorite and 0.4% opaques [13]. Since the coarse-grained sandstones of Gondwana age are considered to be the weathered product of Eastern Dharwar Craton, it is essentially composed of quartz and little proportion of feldspars, together with accessories of biotite, hornblende, apatite, epidote, fluorite, sphene, zircon, etc. The volume percentage of the different minerals depends on the cementing material and environment of deposition. The muscovite and biotite micas, fluorite minerals contribute to the higher F levels in this study.


**Meet the editor**

Editor, Dr. Wen-Ping Cao received his BEng degree in electrical engineering from Beijing Jiaotong University, China, in 1991, and the PhD degree in electrical machines and drives from the University of Nottingham, UK, in 2004. He is currently a Chair Professor of Electrical Power Engineering and the Head of Power Electronics, Machines and Power System (PEMPS) Group at

Aston University, Birmingham, UK. His research interests include design, fault analysis and condition monitoring of power components and electrical systems.

Editor, Dr. Jin Yang received his BEng and MSc degrees from North China Electric Power University, China, in 2003 and 2006, respectively, and his PhD degree from the University of Glasgow, UK, in 2011. He is now a lecturer at the School of Engineering and Applied Science, Aston University, UK. His research interests include operations and optimization of power transmission and

distribution networks, protection of renewable power generation systems and smart grid and low-carbon technologies.

## Contents

**Preface XI**

#### **X** Contents

## Preface

**Section 3 Interconnection and Integration of Microgrid Systems 149**

Luis I. Minchala, Youmin Zhang and Oliver Probst

Chapter 9 **Energy Management in Microgrids: A Combination of Game**

Zhenyu Zhou, Fei Xiong, Chen Xu and Runhai Jiao

Chapter 10 **Operation of Plug-In Electric Vehicles for Voltage Balancing in**

Chapter 11 **Hybrid AC/DC Microgrid Mode-Adaptive Controls 255** Eneko Unamuno and Jon Andoni Barrena

**Theory and Big Data‐Based Wind Power Forecasting 209**

Guido Carpinelli, Fabio Mottola, Daniela Proto and Angela Russo

**Integration of Renewable Energy Sources into Microgrids 151**

Chapter 7 **Design of an Energy Management System for Secure**

Chapter 8 **FutureGrid: Use of Microgrids in Underserved**

**Unbalanced Microgrids 233**

Andrew Hubble and Taha Selim Ustun

**Communities 175**

**VI** Contents

Microgrids are an indispensable element of "smart grids" when incorporating distributed generation from renewable energy sources (such as wind, solar and bioenergy) into the power grids. Microgrids can be AC, DC or in a hybrid form and can operate in connection with the main grid or in islanding mode. As the penetration of new and renewable energy sources continues to increase especially in developing countries and in rural areas, the con‐ trol, operation and fault profile of the systems can be technically challenging. This book re‐ sponds to the latest development of microgrids and attempts to provide some solutions to the control, system integration, fault and disturbance issues when involving renewable power generation (on the generation side) and electric vehicles (on the consumption side). In addition, some chapters also provide some insight into management, operation, data and economic analysis of the microgrid systems to enrich the value of the book.

This book contains 11 chapters and is organized into 3 sections. Section I outlines AC/DC mi‐ crogrid concepts, design and control considerations. Section II deals with operational and technical aspects of microgrids, followed by Section III to address the issues associated with the interconnection and integration of microgrid systems. The editors would like to acknowl‐ edge all the contributing authors and the Publishing Process Manager Ms. Maja Bozicevic.

> **Dr. Wen-Ping Cao and Dr. Jin Yang** Aston University Birmingham, United Kingdom

**AC/DC Microgrid Concepts, Design and Control Considerations**


**1. Introduction**

Wood as a building material has traditionally been used for different types of loadbearing structures, decking, façades cladding, doors and windows. Wood, due to its versatile character, is often used in both rustic and modern buildings, as well as frequently applied as a material for retrofitting of existing structures. The trend for raising sustainable buildings, in addition to increasing environmental awareness observed nowadays, leads to the reactivation of bio-architecture as an alternative to other construction techniques [1,2]. However, the major limitation for choosing natural wood for external use is the lack of confidence that many architects, designers and customers have towards this material, mostly due to aesthetic preferences. The speed and pattern of the colour change are difficult to predict and are rarely taken into consideration at the early phases of building design [3].

Today's bio-based building materials, even if well characterized from the technical point of view, are often lacking reliable models describing their performance during service life and the operational durability is still a limiting factor in many applications

**Citation:** Sandak, A.; Sandak, J.; Noël, M.; Dimitriou, A. A Method for Accelerated Natural Weathering of Wood Subsurface and Its Multilevel Characterization. *Coatings* **2021**, *11*, 126. https://doi.org/10.3390/ coatings11020126

Received: 4 January 2021 Accepted: 19 January 2021 Published: 23 January 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

and environments. Wooden elements often suffer due to mechanical, environmental or biological alterations during their use phase. The most susceptible parts are unprotected surfaces since they are primarily exposed to ageing, weathering or decay.

Weathering is the general term used to define the slow degradation of materials exposed to weather condition [4]. The intensity of weathering depends on timber species, design solution, function of product and finishing technology applied for wood protection and on the specific local conditions. The corrosion rate of wood is low: approximately 6mm per century in the case of European softwood species [4]. In general, the process of wood weathering leads to a slow breaking down of surface fibers, their removal and, in consequence, a roughening of the surface and reduction of the glossiness. Formation of discontinuities on the surface simplify moisture penetration and allow access of wooddecaying biological agents into the material structure. As a result, mechanical performance of the load-bearing members might decrease.

A critical limitation of the state-of-the-art natural weathering procedures is a fact that the test is very long lasting. Diverse approaches for its acceleration were proposed, including amplification of the absorbed radiation by implementing additional solar light focusing optics, scheduled water spraying, temperature control of the sample, or automatic systems for exposing samples that continuously follow the Sun's position. An example of that sophisticated instruments is Equatorial Mount with Mirrors for Acceleration with Water (EMMAQUA) that is commercially available for testing diverse materials [5]. Even though, the amplification of the weathering process is limited, not to mention elevated costs for such complicated equipment and demanding procedure for the test itself. An alternative approach for accelerating the natural weathering of bio-based materials is reported here. The solution is based on the fact that the most affected part of the sample during natural weathering is the sample surface (subsurface) of few hundreds of micrometre thickness. A pilot experiment was designed to prepare wood micro-sections (ultra-thin samples) and to confront the mechanism of changes to the wood due to the action of atmospheric agents occurring in short term. Similar approach had already been used successfully for investigating the resistance of wood against biological corrosion [6], chemical corrosion [7] as well as atmospheric corrosion [8,9]. Use of extra thin samples permits direct investigation of the surface layer and its disintegration due to photochemical or mechanical degradation.

An artificial weathering is an established alternative for testing a material's resistance to deterioration due to climate related factors. The European standard EN 927-6 [10] describes such a method that is frequently used to assess wood coatings. It uses a specially designed device equipped with temperature control, light radiation by means of fluorescent ultraviolet range A (UVA) lamps, water spray and water condensation units. Another, equally common methodology relays on testing weathering resistance by simulating the Sunlight irradiance by means of xenon lamps [11]. Podgorski et al. reported relatively good agreement for the development of cracking when compared to results from natural weathering of coated wood, with the estimated acceleration factor of ×5 [12]. Even a higher acceleration factor of ×10 was identified there for a loss of surface glossiness. Development of cracking was faster in artificial weathering tests due to higher doses of degrading factors provided in shorter cycles but with precisely controlled exposure conditions. Most artificial weathering methods lack biological impacts as well as the influence of dirt that are present in typical exterior exposure environments [13]. Özgenç noticed significant differences in the colour alteration as well as in compression strength along fibres for samples subjected to the natural and artificial weathering conditions [14]. It was demonstrated that the reason for such differences laid in the lack of several minor factors that are not included in the artificial weathering protocols. These factors include air pollution, biological pests, and snow, as well as difference in the spectral composition of light irradiation, nature of the rain-driven wetting, and humidity variations occurring under natural conditions. Similar observations were reported by Ruther and Jelle, where solar radiation and wind-driven rain were identified as the main factor inducing colour changes [15]. However, the surface greying typically observed for the natural wood exposed to exterior conditions is caused

by mould growth and is hardly replicated in up-to-date artificial weathering tests. Outdoor exposed samples become usually darker at the first stage of weathering, to turn in to lighter tones afterward. This is supported by both visual assessment of inspectors as well as by objective spectra/colour measurements. On the contrary, samples exposed to laboratory weathering cycles turn darker to varying extend and do not exhibit initial increase in International Commission on Illumination (CIE) *a*\* (green–red) and CIE *b*\* (blue–yellow) values, typical to samples exposed outdoors. In addition, it was found that samples exposed over a longer period to artificial weathering show a form of unnatural bleaching, not present in wood samples exposed to natural weathering [15]. The overall conclusion is, therefore, that even if several advantages of artificial weathering make this an interesting testing methodology, the results from laboratory weathering can hardly be compared to the real outdoor weathering. The reason lays in different mechanisms and kinetics of the material deterioration, which are especially diverse in complex biological composites such as wood.

Several attempts to understand and model wood weathering have been conducted by various researchers. A review published by Brischke and Thelandersson [16] presents an overview on modelling methodologies used for outdoor performance prediction of wood products. According to the authors, dose-response models seem to be most frequently applied, where the changes in any material property are related to the dosage of the relevant environmental causes. In general, solar radiation and wind-driven rain are the two main driving factors influencing wood surface appearance [17]. They entail further internal stresses of bulk material imposed by cyclic wetting and temperature changes. Methodology for determining the weather dose of the building façade was also reported by Thiis et al. [18]. The authors stated that the surface temperature can be very different than the ambient air temperature, especially when the building elevation is exposed to direct sunlight. Therefore, variation of the exterior microclimate is also important for deterioration extent. Access to an extensive data set generated by different types of sensors is indispensable for the development of high-quality numerical models. The attempt for defining an original algorithm for calculation of "the weathering indicator", by merging the multi-sensor data and linking these to the surface performance indicators, was also presented by the authors [19,20].

New knowledge regarding the role of matrix polymers and their interaction on a molecular level permit discovering of complex biological structures [21]. In particular, microscopic and spectroscopic techniques allow better understanding of the ultrastructure of wood, which is a composite of diverse cell types and chemical components acting together to serve the needs of a plant [22]. Tracheids are the major component of softwood. Their cell wall is composed of several layers: middle lamella, primary wall and secondary walls (S1–S3) [23]. The thickness, chemical composition and structure (microfibril orientations) of those layers vary due to their role. Both primary and secondary walls contain pectin, cellulose, and hemicellulose, although in different proportions. The function of the middle lamella is adhesion of adjacent cells. The primary wall consists of a rigid skeleton of cellulose microfibrils. A layered secondary cell wall, composed of lignin, cellulose, and hemicellulose, provides strong mechanical strength [24].

Microstructure analyses of wood investigated by scanning electron microscopy (SEM) were conducted by several researchers. Hamed et al. [25] and Jingran et al. [26] used SEM for monitoring changes in archaeological wood. Nicole et al. [27] and Popescu et al. [28] investigated decayed wood. Singh et al. [29] used light and scanning electron microscopy for visualization of wood impregnation and imaging of the wood–matrix polymer interface in bio-composites [30]. Microscopic structural analyses of weathered wood were conducted by several researchers [31–33]. Ganne-Chédeville et al. [34] used SEM images for the evaluation of wood façade elements after cleaning treatments. SEM is an essential tool allowing observation of the structural changes in degraded wood tissues at the level of cell wall [25]. Even if the obtained information refers mostly morphological and structural changes of the wood, it might also indicate changes in its chemical structure. Spectroscopy

allows even more detailed investigation of chemical composition by revealing molecular structure of wood and functional groups of its constituents [35]. Specific utilization of spectroscopy for the assessment and monitoring of timber members has been proposed by several researchers and an overview was previously presented by the authors [36]. Two-dimensional correlation spectroscopy (2D-COS) used for the analysis of spectral data allowed the identification of small and weak peaks that are masked in one-dimensional spectrum by highlighting spectral changes caused by external disturbances (exposure time). The method was previously used for the evaluation of the degradation stage of wood [37], evaluation of heat-treated wood [38] or wood species discrimination [39].

The goal of this research was to investigate the feasibility of the novel approach for accelerated natural weathering of wood. An extent of the microstructure degradation and other alterations on the molecular, microscopic, and macroscopic levels recorded in ultra-thin wood samples were confronted with thick references. As a result, an acceleration factor relating weathering kinetics of ultra-thin samples with references was estimated. An additional objective was to develop a novel testing protocol resulting in at least comparable or even more detailed characterization with simplified sample preparation, but still assuring significant shortening of the natural weathering procedure.

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

#### *2.1. Experimental Samples*

Experimental samples were prepared from Norway spruce wood (*Picea abies* L. Karst.) originated from Italian Dolomite mountains (Trentino Region, Italy). A single log 0.5 m long, extracted from the bottom part of the trunk was spitted to assure radial surface opening. A board 30 mm × 50 mm × 500 mm (radial × tangential × longitudinal directions respectively) was cut out from the sapwood assuring defect free and uniform yearly ring structure. The average yearly ring width was 1.9 mm and latewood width 0.8 mm. No drying process was used to minimize wood properties alteration due to elevated temperature. Wet board was finally processed on the slicing planner (Super MECA-S, Marunaka Tekkonsho Inc., Shizuoka, Japan) to prepare ultra-thin experimental samples. This method allowed the preparation of a high number of defect-free specimens from a limited source of material, minimizing natural heterogeneity of biological material. Moreover, samples prepared in this way might be immediately characterized after weathering (e.g., direct SEM observation or spectroscopic measurement in transmission mode) without supplementary interference (e.g., microtome sectioning), which might affect their original state [40]. The thickness of samples was ~100 μm and the efficient surface exposed to weathering was 30 mm × 30 mm (width × length).

An additional set of reference (thick) samples was prepared from the same log as ultra-thin samples to assure minimal variance of the tested wood properties. Twelve wooden blocks of dimensions 30 mm × 10 mm × 50 mm were extracted and used for natural weathering.

#### *2.2. Weathering Test*

Sets of both ultra-thin and thick samples (Figure 1b) were placed for natural exposure at 45◦ to the horizon, facing four cardinal directions: north, west, east and south in San Michele, Italy (46◦11'15" N, 11◦08'00" E, 206 m above sea level). The sample stand (Figure 1a) was installed on the roof of the building assuring no shadowing of surrounding structures and vegetation. The test was carried out from July 2014 to June 2015. Ultra-thin samples were collected before exposition (0) and after 1, 2, 4, 7, 9, 11, 14, 17, 21, 24 and 28 days of natural weathering. Thick samples were collected once per month for a duration of one year. The weather data observed during the test are summarized in Table 1. In addition, the early climate data for exposure location are provided in Table 2. Experimental samples together with non-weathered references were conditioned after collection in the dark climatic chamber (20 ◦C, 60% Relative Humidity (RH)) to the equilibrium moisture content of ~12%.

**Figure 1.** Experimental stand (**a**) and set of ultra-thin and thick wood samples (**b**) exposed to natural weathering in San Michele All'Adige (Italy).

**Table 1.** Weather data acquired during weathering progress of ultra-thin samples.

**Table 2.** Weather data acquired during weathering progress of reference (thick) samples.

#### *2.3. Macroscopic Observation: Colour Measurement*

Sample colour was measured by using ultraviolet-visible-near infrared (UV–VIS–NIR) spectrometer Maya2000pro, produced by Ocean Optics (Largo, FL, USA). Equipment calibration was carried out using white and black standards provided by the manufacturer. Changes in colour were also assessed by a colorimeter following the CIE *L*\**a*\**b*\* system where colour is expressed as three coordinates: *L*\* (lightness), *a*\* (red-green tone) and *b*\* (yellow-blue tone). CIE *L*\**a*\**b*\* colours were measured using MicroFlash 200D spectrophotometer (DataColor Int, Lawrenceville, NJ, USA). The selected illuminant was D65 and viewer angle was 10◦. All specimens were measured on three different spots over the weathered surface. Digital images of sample surfaces were also captured on an G2710 (Hewlett-Packard, Palo Alto, CA, USA) office scanner, in parallel to the colour measurements.

#### *2.4. Microscopic Observation: Scanning Electron Microscopy*

Small pieces of investigated samples were cut out and glued with carbon tape sticker to the sample holder. The samples were placed in SC7620 (Quorum Technologies, Laughton, UK) Mini Sputter Coater/Glow Discharge System device and then plasma coated for 90 s

with 10 nm gold/palladium (Au/Pd) layer. Prepared samples were investigated by using TM 3030 (Hitachi, Tokyo, Japan) Scanning Electron Microscope (SEM). An acceleration voltage of 15 kV was used for imaging of samples. Images were acquired with a dedicated software provided by the equipment producer.

#### *2.5. Molecular Observation: Fourier Transform–Infra Red Measurement*

All experimental samples were measured with an Alpha Fourier transform infrared (FT-IR) spectrometer produced by Bruker Optics GmbH (Ettlingen, Germany) equipped with universal sampling module allowing measurement in transmission. The spectral range measured was between 4000 and 600 cm−1. The spectral resolution of the spectrophotometer was 4 cm−1; each spectrum has been computed as an average of 32 successive measurements in order to minimize the measurement error. Three measurements were performed on each sample. Opus 6.5 (Bruker, Ettlingen, Germany), PLS\_Toolbox (Eigenvector Inc, Manson, IA, USA), available as an extension of the Matlab package (Mathworks Inc, Natick, MA, USA) and LabView 2019 (National Instruments Inc, Austin, TX, USA) software packages were used for spectral pre-processing and data mining. IR spectra before interpretation were corrected by extended multiplicative scatter correction (EMSC). EMSC was applied to minimize an effect of nonuniform optical density for measured wood specimens and related differences in the measured amplitude and baseline deviation [41]. Three replicate measurements on each sample were averaged and the resulting spectrum was baseline-corrected. A lot of research efforts have been directed toward systematizing the infrared peaks interpretations that resulted in several scientific publications [42–47]. These papers were an inspiration to review the available know-how and adopt it for the specific needs of this research (wood weathering). The complete list of peaks observed in investigated experimental specimens is summarized in Table 3.

Standard spectra interpretation was aided by two-dimensional spectra correlation (2DCOS), which permits direct deconvolution and determination of correlations between bands in the spectra; 2D-IR spectroscopy allows new information to be obtained, which cannot be acquired by using conventional IR and its derivative spectra. The algorithm proposed by Noda [48] was implemented for the needs of this research. All spectra were interpolated before analysis in order to compensate uneven exposure time, considered here as the 2DCOS disturbance factor.

#### **3. Results**

#### *3.1. Macroscopic Level*

The appearance of investigated samples after natural weathering in diverse exposure times is presented in Figure 2. The changes in colour are observed in parallel with samples disintegration evident from day 17. Samples become yellowish with the progress of degradation. It is noticed that weathering caused removal of wood fibers, presence of cracks and increase in brittleness of samples.

**Figure 2.** Appearance changes of ultra-thin (**a**) and reference thick (**b**) wood samples exposed to natural weathering in San Michele All'Adige (Italy). Note: arrow indicate weathering-induced damages to wood cells.

Visible range reflectance spectra of the test samples in this study shows that spruce tends to darken with weathering time, as shown by a decrease of the reflectance value (Figure 3). The reflectance value dropped after one day of exposure. Further changes, as observed after day 7 until the end of the experiment, were more consistent and resulted in a nearly constant VIS spectrum after three weeks of weathering. Analogous results were obtained by measurement of colour with colorimeter. The CIE *L*\**a*\**b*\* colour coordinates system is widely used to represent colours in three-dimensional space. According to the CIE Lab definition, *L*\* represents lightness (varying from 100-white, to 0-black). Similarly, *a*\* and *b*\* are chromaticity coordinates, where +*a*\*/−*a*\* corresponds to red/green, and +*b*\*/−*b*\* to yellow/blue tonality. A decrease of *L*\* was observed for all exposure directions (Figure 4). The value of *a*\* increased in the first days of exposure and from day 4 seems to be stable. The noticeable increase in *b*\* value, visible as yellowing of wood, is also observed for all exposure directions; however, it is followed by *b*\* values drop after one week of exposure.

**Figure 3.** Reflectance VIS spectra of the wood exposed to natural weathering.

**Figure 4.** Change of CIE *L*\**a*\**b*\* colour coordinates in ultra-thin wood samples exposed to natural weathering.

#### *3.2. Cellular Level*

The first sign of deterioration visible on the SEM images was openings of bordered pits membranes (torus) in radial walls of earlywood tracheids (Figure 5a). In the successive step, the membrane covering the piths was broken (Figure 5b) and presence of small diagonally oriented micro-checks was observed (Figure 5c). With the progress of degradation, an enlargement of the pith crack was noticed as a result of contraction of the cell wall caused by moisture variation (Figure 5d).

**Figure 5.** SEM images of earlywood exposed to the south for 2 days (**a**), 7 days (**b**), 9 days (**c**) and 21 days (**d**). Note: arrow indicate weathering-induced damages to wood cells.

Evidence of fungal infection (not visible by naked eye) was found after 17 days of natural exposure, indicating that the growth of microorganisms begins by deposit of their spores into the micro-cracks. Evaluation of the earlywood zones of samples exposed to four directions after 9 days proved that the degradation kinetics vary according to exposure direction. The experimental samples images at the final stage (day 28) are shown in Figure 6, which presents SEM images of latewood zone. Cracks propagate through the cell wall along the progress of decomposition. Pits are completely eroded, and the degradation products are continuously removed with rain. However, the northern samples seem to be relatively less affected by the weathering process (Figure 6c). The time-related progress of morphological changes of the investigated samples is systematically presented in Figure 7.

**Figure 6.** *Cont*.

**Figure 6.** SEM images of latewood exposed to different exposure sites: south (**a**), west (**b**), north (**c**) and east (**d**). Note: arrows indicate weathering-induced damage to wood cells.

**Figure 7.** Microscopic observation for samples exposed to natural weathering for the south direction.

#### *3.3. Molecular Level*

FT-IR spectra measured in transmission mode on samples at various stages of degradation clearly show the progress of changes related to functional groups of wood polymers (Figure 8). The band assignment (Table 1) allowed the identification of 25 peaks that might

be observed and interpreted. The most evident are changes in spectra intensity of bands #7, #8, #14 assigned to aromatic skeletal vibrations of lignin. In all the above-mentioned peaks, the intensity diminishes with weathering progress, consequently, the peaks disappear, indicating decomposition of corresponding chemical component (or functional group). The region 3900–2700 cm−<sup>1</sup> reflects essentially the amounts and structure of hydroxyl groups in various types of hydrogen bonding of wood components [44]. Even if bands assigned to hydroxyl groups in IR spectrum are better resolved after deconvolution, changes noticed in #24 and #25 reflect reduction in hygroscopic properties of wood exposed to the weathering process [43].

**Figure 8.** Changes to FT-IR transmittance spectra of Norway spruce wood due to southern exposure to natural weathering.

Peak #14, assigned to aromatic skeletal vibrations of lignin and C=O stretching, was selected to illustrate changes of its intensity due to weathering progress in different cardinal directions (Figure 9). In general, similar progress of intensity changes can be observed; however, the deviations are slightly less intense in the case of samples exposure to north compared with other directions. It is especially evident in the last week of exposure, where intensity of degradation is apparently lower than in the other three cardinal directions. For southern exposure, the highest changes are observed at the initial stage (up to first two weeks), followed by less intense variation in the second part of the experiment. Eastern exposure seems to follow the southern; however, after 14 days, the intensity is less pronounced. Western exposure is less affected up to three weeks. After that period, both (western and eastern) exposures reach the same degradation level as south.

**Figure 9.** Changes to FT-IR lignin peak (1508 cm−1) along the natural weathering test duration in samples exposed to diverse cardinal directions.

Two-dimensional correlation spectroscopy (2D-COS) was developed by Noda and involved a series of spectra being recorded from one or more samples during or following perturbation [48]. The resulting spectral data matrix allowed identification of compositional differences within and between samples. Changes in data are visualized in 2D spectral maps. Correlations between measured variables is included in the synchronous matrix. The asynchronous matrix represents sequential variations occurring in the data. The peaks might be located on the diagonal or off the diagonal space. Peaks located on the diagonal (autopeaks) show regions of the spectrum that are changing with respect to the average spectrum of the series. Peaks off the diagonal (cross peaks) show the correlation intensity (positive or negative) of different bands [48].

Figure 10 presents homospectral correlation, where two identical assemblies of data (spectra) are used. The synchronous 2D IR correlation spectrum shows strong positive autopeak at #14. The magnitude of autopeak corresponds to the overall extent of spectral intensity variation observed for specific wavenumber. Presence of other regions assigned to functional groups in lignin (#12, #13 and #15) indicated changes in intensity during weathering progress, meaning that they are susceptible to external perturbation. In fact, the changes in intensity of peak #14 as observed on Figure 8 are very evident. Positive peaks are highlighted by red colour, in contrast to negative peaks marked in violet. The positive peak on the synchronous spectrum indicates that spectral bands corresponding to coordinates of that peak change continuously in the way that direction of changes (increase or decrease) is identical for both wavelengths. Conversely, the negative peak appears when one wavelength increases, while the second wavelength decreases. The sign of asynchronous spectrum allows identification the sequence of changes occurring at both confronted wavelengths [49]. Crosspeak at coordinates 17–14 represent negative intensity. It indicates that changes of the intensity of both bands are substantially different (aromatic skeletal vibrations of lignin and C=O stretching versus C=O–OH stretching in glucuronic acid in xylan).

**Figure 10.** 2D spectral correlation of the FT-IR spectra of Norway spruce wood exposed to south direction in San Michele All'Adige (Italy): (**a**) synchronous spectrum; (**b**) asynchronous spectrum

#### **4. Discussion**

According to Raczkowski [9], changes in wood structure, caused by natural weathering and expressed by the reduction in tensile strength, are more intensive during the summer months in comparison with autumn and winter; therefore, our tests were carried out in July. During the weathering experiment, the mean temperature oscillated around 20 ◦C, while relative humidity was slightly above 70%. Table 1 contains meteorological data captured during sample exposure. Three major rain events and an additional two days with small (2 mm) precipitation were observed. The total hours of insolation were 255, accounting for 581 MJ/m2. The test duration was limited to 28 days; however, according to Evans [50], the weathering conditions in 45◦ south exposure are accelerated relative

to vertically installed elements. The configuration of experimental samples (~100 μm thickness of strips) allowed their non-destructive and direct analysis with several methods, such as IR spectroscopy in transmission mode or SEM observation without microtome sectioning. Changes in surface texture, colour and chemical composition being an overall result of exposure to various environmental conditions such as moisture, heat and solar radiation were assessed at different levels providing a wide range information regarding degradation process.

The relatively short exposure time affected appearance of investigated wood. Yellowing of the wood is caused by the formation of chromophoric groups of secondary origin [51,52]. When the chromophoric groups leach from the wood, the wood colour changes further, eventually converting to grey shades. Due to limitation of experiment to one month, this phenomenon was not observed within this research. Analogous results regarding colour changes of wood due to weathering was reported previously by authors while performing artificial weathering test according to EN 927-6 [53]. The changes in colour were already evident after 28 h of artificial weathering. Specific shape of curves representing colour coordinates (CIE *b*\* measured for all cardinal direction and CIE *a*\* measured for north) disqualify their use for modelling general trends in the appearance change (Figure 4). In fact, wood might have similar colour values after receiving different weathering doses (e.g., CIE *b*\* of samples exposed for one and four weeks). The pattern of initial increase of CIE *b*\*, followed by its decrease, was also reported by other authors [51,54,55]. It was explained by lignin degradation and photooxidation of –CH2 and –CH(OH) groups, with the formation of secondary chromophoric structures (quinones).

Microscopic methods provide detailed information about surface morphology. Scanning electron microscopy is considered a valuable tool for the examination of the morphological characteristics of degraded wood at the level of cell wall, evaluating their damage and detecting eventual presence of decay [25]. As stated by Turkulin [56], observation of the effects caused by photo degradation may provide information about structural integrity of wood surface and help understanding the weathering process. The process of thin wood section destruction due to natural weathering was previously investigated by Raczkowski [9]. He stated that the degradation penetrates relatively deep and observed that the decrease of mechanical strength properties is very significant. According to Turkulin et al. [31], a weathered wood surface shows damage to several anatomical elements: development of cracks of bordered pits, presence of cracks and loss of membranes of the ray pits, and expansion of cracks that can be seen on the lumen surfaces, originating at single pits (Figure 5). Thinning of the cell walls is due to breakdown of lignin in the S2 layer of the cell wall [56]. Progress of weathering (UV degradation, leaching effect of water and mechanical damages caused by wind blow) leads to extensive destruction of the pit membrane and further crack propagation (Figure 5d). Pit openings are then enlarged and, in consequence, the whole pit erodes away. According to Sandberg [57], the orientation of these cracks suggests that these follow the fibril orientation in the S2-layer of the cell wall. Pandley and Pitman [58] reported that specific ridges on the S3 wall layer, wall checking, pit degradation and middle lamella breakdown are typical microscopic features characteristic for natural wood exposed to weathering.

Fungal infestation of investigated samples was noticed after the third week of exposure. Turkulin [56] observed first spores after only 4 days of exposure; however, more intense fungal growth was noticed by him after 41 days. In general, fungal development is constrained on the surfaces exposed to high sun radiation; therefore, intense UV absorbance slows down speed of colonization by fungi. However, summer 2014 was rainy and relatively cooler than usual. Consequently, it promoted growth of the fungi, even on samples with southern exposure. Owen et al. [59] observed that degradation caused by the combined weathering stimuli, including both UV light and precipitation, is considerably more intensive and rapid than exposure to each parameter separately. UV radiation leads to lignin decomposition (photolysis) and, in consequence, to delamination of surface fibres. In particular, the chromophoric groups of lignin are strong UV absorbers and responsible

for yellowing of the surface [51]. The other apparent result of the intensive delignification process is brittleness of the samples. Degradation of lignin was also observed while analyzing IR spectra. The most pronounced reduction of intensity was evident for band #14 and is related to degradation of lignin caused by UV radiation. Similar statements were derived by other researchers [51,60], who observed a decrease of peaks assigned to the lignin along the irradiation time in laboratory experiments. Lignin and extractives are considered as absorbers of UV radiation; therefore, these components suffer major degradation. When the sample system is under an external perturbation, various chemical constituents of the system are selectively excited, and the degrees and orders of the excitations are different [61]. In our case, weathering progress being external perturbation in the system affects molecular structure of investigated material. 2DCOS reveals characteristic behaviours of individual molecular constituents, in particular lignin susceptible to weathering process. Since the intensity of peaks on the autocorrelation spectrum is directly proportional to the relative importance of the intensity change in the original spectra, it can be stated that aromatic skeletal vibrations of lignin are the most vulnerable functional groups.

The observation of weathering process on three different scales, macroscopic, microscopic and molecular, allowed comparison of mechanisms occurring on various levels. It was assumed that the southern location might cause slightly more degradation of wooden surfaces, even in a relatively short time. It was related to longer exposure of UV radiation and, in consequence, more rapid changes of surface moisture. Northern location seems to be less susceptible to degradation, especially at the beginning of weathering. It corresponds to changes of integrated cumulative solar radiation (SR) calculated according to the method proposed by Grossman [62]. It can be clearly seen that the value of cumulative SR of southern exposure is double than northern (80 and 40 kW·m−2, respectively) [63]. This phenomenon might explain variation in weathering intensity between exposure sites.

The use of ultra-thin wood samples for investigation of material resistance to natural weathering as reported here reveals that mechanism of degradation as well as sequence of material deterioration follows directly state-of-the-art understanding of wood weathering processes. Investigation of the effect of natural weathering on the microstructure of radiata pine by means of SEM shows analogous deterioration sequence. Degradation of the pit membranes was followed by the gradual enlargement of the pit aperture to the approximate limit of the pit chamber, development of microchecks in the pit borders and to ultimate destruction of pit borders in earlywood tracheids in about 6 months [64].

The particular sample geometry (ultra-thin thickness) determined elevated kinetics of such changes and shortened response time of the material for the weather dose. Analogous approach was proposed by Wang et al. for other types of materials [65]. Their study was related to the thickness-dependent accelerated ageing method that leaded to the more severe ageing degree of the thinner specimens. The phenomenon was explained by the ratio of aged area to the total area. It was reported that in the thinner specimens the ratio was larger than in the thicker specimens. It is always a challenge to determine a specific quantifier of the weathering acceleration factor (AF). Jelle proposed calculation of AF by comparing natural and artificial weathering [66]. He assumed that the higher UV intensity (W/m2) and total energy (kWh/m2) in the weathering apparatus is proportional to the aging process. However, the natural weathering process is more complex, as includes several other than UV irradiation factors influencing the degradation rate [67]. This might be a reason to the wide array of reported AF values, ranging between 5 to 250, while comparing the laboratory results with outdoor tests in natural climate.

For the needs of this research a direct comparison approach was implemented by confronting above presented results with corresponding characterizations for thick wood samples (10 mm thickness) made of the same wood piece. A set of such thick samples was exposed during the natural weathering test in vicinity of thin samples. In that case, weathered samples were collected from the stand for duration of one year, one sample each lunar month. "Thick" samples were assessed with same analytical techniques as ultra-thin strips, by applying necessary amendments (such as Attenuated Total Reflectance (ATR) instead of

transmission mode of infrared spectrometer, or reflectance instead of transflectance when measuring colour spectrum). The trends of material changes can be then directly compared between thin and thick wood samples to understand the weathering process acceleration. Assuming that the sole difference between both sample types is kinetics of deterioration, it become possible to identify an acceleration factor. This factor was determined empirically by adjusting the time scale for diverse deterioration kinetics curves measured on ultra-thin and thick samples to assure an overlap of both trends. An example of such analysis is presented in Figure 11 where acceleration factor of ×3 was applied for the CIE *L* and CIE *a* colour curves in combination with the lignin deterioration trends at peak #14 (1507 cm<sup>−</sup>1). The numerical factor (determined through visual observations) can be therefore used to quantify the time relation between both confronted natural weathering configurations. It is clear that such an approach introduces certain over-simplification as nature of scanned parameters is different, as well as the weather dose distribution is very different during periods of one month (thin samples) and one year (thick samples). In any case, it provides a scientific basis for a statement that the use of ultra-thin samples for natural weathering may lead to shorten the test duration still preserving the complex physical and chemical mechanism of the material, particularly surface, deterioration when exposed to natural weather conditions.

**Figure 11.** Confrontation of (**a**) CIE *L*, (**b**) CIE *a* and (**c**) lignin peak #14 of IR spectrum deterioration kinetics curves for experimental data acquired from thin and thick wood samples exposed to natural weathering, assuming acceleration factor AF = 3.

An important limitation of using ultra-thin samples for natural weathering testing is a fact of losing a complex interaction of different layers within material bulk. It implicates a dramatic change to the diffusion of moisture and heat within bulk material. However, in the context of the wood weathering it is clear that the most relevant changes occur in the subsurface of the material. The majority of extensive chemical–physical reactions (photolysis, hydrolysis, etc.), deposition of biological and abiotic agents, as well as moisture induced deformations take place on the surface of sample and progress into bulk interior only after erosion of the external layers. The electromagnetic radiation in the range of photodegrading UV light penetrates wood to the extent of 50–100 μm, that corresponds to the thickness of samples used in this research. Other important missing aspect in the proposed "accelerated natural weathering" approach is a fact that an ultra-thin sample is missing the mechanical support of the bulk. The support constrains moisture-induced deformations and serves as a physical barrier (or buffer) slowing down the moisture and heat transfer. It affects also the number and extend of microcracks formation. Despite the above limitations, using ultra-thin wood samples is a correct approach for assuring replication of the real mechanisms of the weathering occurring in "thick samples". That cannot be assured in alternative up-to-date artificial weathering methods.

#### **5. Conclusions**

Understanding the mechanisms of weathering and the role of altering factors is fundamental to assess the actual conditions of timber structures. It is also essential for future performance, and, possibly, to ensure long-term preservation and maintenance. Even if it is impossible to fully integrate results obtained by assessment of thin wood samples with standard wooden samples, the presented approach allowed rapid and reliable assessment of the complex chemical and structural changes occurring within the surface layers.

With the progress of degradation, samples become yellowish. Weathering also caused removal of wood fibers, presence of cracks and, an increase in brittleness of samples. Spectroscopic analysis provided information regarding susceptibility of functional groups to natural weathering. Gradual changes in absorbance intensity of CH deformation of aromatic skeletal vibration of lignin confirmed the vulnerability of this component to photodegradation. Scanning electron microscopy (SEM) was used here for examination of the morphological characteristics of naturally weathered thin wooden samples. The structural degradation was initiated from openings of bordered pits membranes in radial walls of earlywood tracheids. In the following steps, the membrane covering the piths was broken and diagonally oriented micro-checks were observed. The progress of weathering led to entire destruction of the pit membrane and whole pit erosion.

It was confirmed that earlywood was more susceptible to damage than latewood, which was explained by the fact that cells in earlywood have thinner and weaker walls and, in consequence, lower density. It was also observed that western and northern exposure sites are slightly less affected by weathering. First signs of fungal infestation, not visible by naked eye, were observed in the third week of natural weathering.

Even if the weathering test was conducted for a relatively short time (28 days), the progress of ultra-thin wood sample alteration was similar as usually noticed for thick wood surfaces. However, such an extent of changes occurred at shorter exposure times. The estimated acceleration factor was ×3. The research methodology presented here can be directly used for determining the weather dose-response models essential to predict the future service life performance of timber elements. It may also serve as a screening technique for a rapid estimate of the material's resistance to deterioration. In that case, all the natural weathering factors are fully considered, in contrast to artificial weathering procedures limiting the real exposure conditions.

**Author Contributions:** Conceptualization: A.S., J.S., M.N., and A.D.; methodology: A.S., J.S., M.N., and A.D.; software: J.S.; validation: A.S., J.S., and M.N.; formal analysis: A.S.; investigation: A.S., J.S., M.N., and A.D.; resources: A.S. and J.S.; data curation: J.S. and A.S.; visualization: J.S.; supervision: J.S. and A.S.; project administration: A.S.; funding acquisition: A.S., J.S., M.N., and A.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors gratefully acknowledge the European Commission for funding the InnoRenew project (Grant agreement #739574 under the Horizon2020 Widespread-Teaming program) and the Republic of Slovenia (investment funding of the Republic of Slovenia and the European Union European Regional Development Fund) and infrastructural ARRS program IO-0035. Part of this work was conducted during project Archi-BIO (BI/US-20-054) funded by ARRS and CLICK DESIGN "Delivering fingertip knowledge to enable service life performance specification of wood"–(No. 773324) supported under the umbrella of ERA-NET Cofund ForestValue by the Ministry of Education, Science and Sport of the Republic of Slovenia. ForestValue has received funding from the European Union's Horizon 2020 research and innovation programme.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The experimental samples were prepared and weathered during Round Robin test of COST FP1006. The support of COST FP1303 for funding Short Term Scientific Mission of Anna Sandak is highly acknowledged. Authors would also like to thank Thomas Volkmer for inspiring discussion.

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


**3.2 Neural networks**

The basic functional unit of the nervous system is the neuron [22]. Neurons function by receiving an input, processing the signal, and generating an output signal [23, 24]. Anatomically speaking, neurons are capable of consolidating up to thousands of neurotransmitter-driven synaptic inputs simultaneously via dendritic extensions, processing a highly transformed version of the original inputs in the soma, and producing a singular output through its axon in the form of an action potential [25]. Importantly, neuronal outputs are not generated at a fixed rate but rather are a function of whether or not the signal summation (excitatory - inhibitory inputs) exceeds a predefined threshold value in order to successfully depolarize the neuron and induce an action potential [26–28]. After traveling through the axon, the action potential signal is transmitted to a multiplicity of neurons synapsed at the axon terminal.

Broadly speaking, artificial neural networks (ANNs) model the biological principles of neuronal signaling in order to stratify and solve complex, nonlinear problems [29]. Considered a subfield of ML, ANNs refer to a digital machine learning algorithm based upon the concept of a biological neuron. Comparatively, where neurons rely on neurotransmitter signaling inputs ANNs leverage binary, categorical, or numeric data sets [5]. Transformation of input signals at the soma into an action potential is akin to an ANN arithmetic-based calculation of inputs into an output [30].

Although the theory underlying ANNs was first developed in the 1980s, premier advances in computational power and training data acquisition at scale have enabled its extensive application in recent years. In neurosurgery, ANNs have grown to be increasingly utilized in diagnostics, prognostics, and management [31]. Deep learning (DL) is yet another class of algorithms increasingly studied in the literature. Although similar to neural networks in principle, the term "deep" refers to the increasing depth of layers present in the neural network – typically accepted to imply at least three layers [32].

The ability to analyze non-linear data by ANNs is ideal for assisting neurosurgeons in clinical decision-making [33]. In particular, ANNs have been widely demonstrated to be superior to traditional analytical methods, especially as it pertains to clinical imaging tasks [34]. Even so, significant challenges still exist which limit the widespread use of ANNs and DL in neurosurgery and medicine at large, including insufficient data, obscured interpretability, reliability of data, high threshold of processing power, and data privacy [3].

#### **3.3 Natural language processing**

Natural language processing (NLP) is another subfield that falls under the scope of ML. As its name implies, the goal of NLP is to better enable human-computer communication by leveraging natural human language to better perform data abstraction processes [35]. In other words, the computer functions to *understand* human-generated text inputs by breaking down sentences into their constituent parts and applying algorithms to derive meaningful outputs. There are two primary divisions within the field of NLP: rules-based models and machine-based models. A rules-based model boasts minimal set-up costs, however is burdensome to scale for large datasets and inflexible as language usage evolves over time; conversely, machine-based models are preferable for large datasets as it can circumvent the rigidity of rules-based model while adapting to evolutions in human lexicon over time [36]. Three methodological approaches that dominate the application of NLP to neurosurgery are classification, annotation, and prediction [37]. Classification involves providing further diagnostic information, and informing the surgeon's decision making in the preoperative phase. Annotation entails automatizing the annotation of a large amount of data (e.g., radiological images) by identifying specific phenotypes related to a disease condition, enabling the NLP algorithm to train on much larger amounts of data and better extrapolate clinical outcomes. Prediction exploits previous data (e.g., free text notes) to predict patient surgical outcomes and enable the neurosurgeon to arrange the resources necessary for their care accordingly. Machine-based NLP as applied to neurosurgery and medicine at scale remains in its infantile stages, though its possibilities rise with the emergence of Large Language Models.

## **3.4 Large language models**

Large Language Models (LLMs) like ChatGPT, developed by OpenAI, are a new wave of AI technology that have profound implications for diverse fields, including healthcare. Educated on a colossal quantity of textual data, these models grasp the delicate intricacies and nuances of human language, thereby equipping them to form pertinent and contextually relevant responses to a broad spectrum of prompts [38].

In March 2023, the performance of ChatGPT and GPT-4 was assessed on a 500-question mock neurosurgical written boards examination. Using Self-Assessment Exam 1 from the American Board of Neurological Surgery (ABNS), Ali et al. fed questions in single best answer, multiple-choice format. ChatGPT and GPT-4 achieved scores of 73.4 and 83.4%, respectively, relative to the question bank user average of 73.7% [39]. Both the question bank users and the LLMs exceeded the previous year's passing threshold of 69%, demonstrating the models' potential technical utility [39].

In a clinical context, including neurosurgery, LLMs could serve multiple purposes. Firstly, they could play a significant role in patient education, simplifying complex neurosurgical procedures, and providing insights into the recovery process in an accessible language [40]. Secondly, these models could help facilitate medical research, from identifying new hypotheses to aiding in clinical decision-making by providing summaries of recent research, medical literature, or guideline updates relevant to specific cases [41].

Another promising application lies in the realm of medical documentation. LLMs could help transcribe doctor-patient conversations, draft surgical reports, or summarize patient histories, thereby streamlining administrative tasks and allowing physicians to focus more on patient care [42]. Continuing Medical Education could also benefit from LLMs. By simulating complex clinical scenarios or generating case studies, these models could serve as an effective teaching tool for medical trainees [43].


**7. Conclusions**

Here, we applied isotopic measurements as a direct technique to determine dietary patterns related to social class at the site of Xiaohucun, Henan Province, China. For the most part, the population was found to be consuming a predominately C4 diet (millets), although a single individual (M30) was found to have a mixed C4/C3 diet and could have been an immigrant to the community. No difference was found in the δ13C results of the individuals buried with bronze vessels (−9.1 ± 2.8%) and the individuals buried without bronze vessels (−8.2 ± 0.7%) but significant differences were present in the δ15N values: individuals buried with bronze vessels (10.3 ± 1.6%) vs. individuals buried without bronze vessels (8.0 ± 0.9%). Isotopic results were then compared by the number of burial coffins that an individual had: double, single, or without coffin. No difference was found in the δ13C values, but variations were observed in the δ15N values: double (10.2 ± 1.7%) > single (8.8 ± 1.8%) > no coffin (8.0 ± 1.3%), possible evidence of increased animal protein consumption with higher social status. Lastly, isotopic results and status were examined by the number of coffins and tomb size. Again, no correlation was seen with δ13C, but a linear correlation (R2 = 0.85) was found for the δ15N values of the elites. Thus, additional social stratification could have existed among the elites with owners' wealthy/power enough to build larger tombs and possibly consuming more animal protein in their diets. These preliminary results of this pilot study offer a glimpse of the social hierarchy that existed during the late Shang Dynasty.

**Author Contributions:** Conceptualization, N.W.; resource, L.J.; Formal analysis, Y.S.; writing—original draft preparation, N.W.; writing—review and editing, N.W., X.J. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 41603009, 41771223, The Ministry of education of Humanities and Social Science project, grant number 16YJCZH100 and National Social Science Foundation of China, grant number 18CKG023.

**Acknowledgments:** The authors thank Hu Yaowu (Fudan University, Shanghai) and Li Xiaoqiang (Institute of Vertebrate Paleontology and Paleoanthropology, CAS, Beijing) for supporting this study. Thanks to Guo Yi (Zhejiang University, Hangzhou) and Chen Xianglong (Institute of Archaeology, CASS, Beijing) for helpful comments. Wang Tingting and Zhang Xinyu (Institute of Vertebrate Paleontology and Paleoanthropology, CAS, Beijing) are thanked for collagen preparation.

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


**3. Theoretical study on pressure fluctuation of high-pressure common rail fuel injection system**

The fluctuation characteristics of fuel pressure in the pipeline of high-pressure common rail fuel injection system can be represented as one-dimensional partial differential equations of unstable compressible flow, as shown in Eq. (1).

$$\begin{cases} \frac{\partial p}{\partial t} + u \frac{\partial p}{\partial \mathbf{x}} + a^2 \rho \frac{\partial u}{\partial \mathbf{x}} = \mathbf{0} \\ \frac{\partial p}{\partial \mathbf{x}} + \rho \frac{\partial u}{\partial t} + \rho u \frac{\partial u}{\partial \mathbf{x}} + 2\kappa \rho u = \mathbf{0} \end{cases} \tag{1}$$

where *u* is the fuel flow velocity, *a* is the fuel pressure wave propagation velocity and *κ* is the fuel flow resistance coefficient.

*Pressure Fluctuation Characteristics of High-Pressure Common Rail Fuel Injection System DOI: http://dx.doi.org/10.5772/intechopen.102624*

The above-mentioned partial differential equations can be converted into ordinary differential equations as follows.

$$\begin{cases} \left(\frac{\mathrm{d}x}{\mathrm{d}t}\right)\_{\mathrm{R}} = u + a, & \frac{\mathrm{d}p}{\mathrm{d}t} + a\rho \frac{\mathrm{d}u}{\mathrm{d}t} + 2a\kappa\rho u = 0\\ \left(\frac{\mathrm{d}x}{\mathrm{d}t}\right)\_{\mathrm{L}} = u - a, & \frac{\mathrm{d}p}{\mathrm{d}t} - a\rho \frac{\mathrm{d}u}{\mathrm{d}t} - 2a\kappa\rho u = 0 \end{cases} \tag{2}$$

where (d*x*/d*t*)R and (d*x*/d*t*)L are the trace lines of pressure waves propagating to the right and left inside the pipeline. The above equations directly reflect the propagation relationship of the pressure wave in the pipeline of high-pressure common rail fuel injection system. When the fuel pressure wave propagates to a specific position in the pipeline, the pressure at this position rises, that is, d*p* is positive, and the pressure wave is a compression wave. Conversely, the pressure drops. d*p* is negative, and it is an expansion wave. Assuming that the right direction of *x* axis is the positive direction, the fuel pressure wave propagating along the forward direction is called the right–traveling wave, while the fuel pressure wave propagating along the negative direction is called the left–traveling wave. The right–traveling wave is represented by d*p*R and d*u*R, and it has the following relationship.

$$\mathbf{d}p\_{\mathrm{R}} = \mathfrak{a}\rho \mathbf{d}u\_{\mathrm{R}} \tag{3}$$

While the left–traveling wave is represented by d*p*L and d*u*L, which has the following relationship.

$$\mathbf{d}p\_{\mathbf{L}} = -a\rho \mathbf{d}u\_{\mathbf{L}} \tag{4}$$

The pressure wave in the pipeline of high-pressure common rail fuel injection system is divided into left-traveling wave and right-traveling wave according to the direction of propagation and divided into compression wave and expansion wave according to the change of fuel pressure caused by propagation. Therefore, pressure waves in the pipeline can be divided into the following four types.

#### a. Right-traveling compression wave

This kind of fuel pressure wave propagates along the positive direction of *x*-axis, where d*p*R is positive and d*u*R is also positive, that is, the fuel pressure and velocity along the x-forward propagating direction increase.

#### b. Right-traveling expansion wave

This kind of fuel pressure wave propagates along the positive direction of the *x* axis, but d*p*R is negative and d*u*R is also negative, that is, the fuel pressure and velocity along the x-forward propagating direction decrease.

#### c. Left-traveling compression wave

This kind of fuel pressure wave propagates backward along the *x*-axis, where d*p*L is positive and d*u*L is negative, that is, the fuel velocity decreases along the x-forward propagating direction. However, the fuel pressure and velocity of left-traveling compression wave along with the propagating direction increase.

#### d. Left-traveling expansion wave

This kind of fuel pressure wave propagates backward along the *x*-axis, where d*p*L is negative and d*u*L is positive, that is, the fuel velocity along the x-forward propagating direction increases.

Supposing the right-traveling wave arriving at *x* position of the fuel pipeline at moment *t* is d*p*R and d*u*R, and the left-traveling wave is d*p*L and d*u*L. According to the pressure wave synthesis theory, the total pressure variation d*p* and velocity variation d*u* are

$$\begin{cases} \mathbf{d}p = \mathbf{d}p\_{\mathbb{R}} + \mathbf{d}p\_{\mathbb{L}}\\ \mathbf{d}u = \mathbf{d}u\_{\mathbb{R}} + \mathbf{d}u\_{\mathbb{L}} \end{cases} \tag{5}$$

where d*p* and d*u* are called synthetic pressure waves at (*x*, *t*). In fact, after the fuel pressure wave is synthesized, each single-traveling wave continues to propagate along its determined direction. Therefore, the above equation can also be understood as the pressure fluctuation in a certain position in the pipeline can be decomposed into lefttraveling waves and right-traveling waves.

When the pressure wave propagates to the boundary surface, another returned pressure wave can be obtained based on the pressure wave and the boundary condition at the moment, which is the reflected wave. Assuming that the right end of the highpressure common rail fuel injection system is closed, the boundary condition is *u* = 0 and d*u* = 0. At this time, if there is a right-traveling wave d*p*R arriving on the left, there must be a left-traveling reflected pressure wave d*p*L, so that d*u* = d*u*R + d*u*L = 0. It can be seen that the fuel reflected pressure wave from the pipeline end boundary surface is the result of coupling the boundary condition with the propagating fuel pressure wave.

There are three boundary conditions for pressure wave propagation and reflection in the pipeline of high-pressure common rail fuel injection system. The boundary type at common rail and high pressure fuel pipeline is outlet isobaric end (outlet opening end). The boundary type is the closed-end when the nozzle needle valve is closed. The boundary type is the orifice flow ends when the needle opening nozzle and injector injection.

#### a. Outlet isobaric end

There are the following equations when the right-traveling wave d*p*R reaches the open end of the right end.

$$\begin{cases} \mathbf{d}p\_{\mathbb{R}} = a\rho \mathbf{d}u\_{\mathbb{R}} \\ \mathbf{d}p\_{\mathbb{L}} = -a\rho \mathbf{d}u\_{\mathbb{L}} \\ \mathbf{d}p = \mathbf{d}p\_{\mathbb{R}} + \mathbf{d}p\_{\mathbb{L}} = \mathbf{0} \\ \mathbf{d}u = \mathbf{d}u\_{\mathbb{R}} + \mathbf{d}u\_{\mathbb{L}} \end{cases} \tag{6}$$

Thus, d*p*L = �d*p*R, d*u*L = d*u*R, d*u* = 2d*u*R, d*p* = 0.

It can be seen that the signs of the incident pressure wave and reflected pressure wave are opposite when the pressure wave propagates to the common rail. However, the absolute value of the amplitude of the pressure wave is the same and the wave velocity is the same. The changing of the pressure at the pipeline end is zero and the changing of the velocity is twice that of the incident pressure wave. The reflection with the property of incident wave and reflected wave is opposite (an expansion wave, the other is compression wave) and the absolute value of the amplitude of the pressure wave is the same is called complete negative reflection. Therefore, the pressure wave reflection at common rail and high pressure fuel pipelines is a complete negative reflection.