The long-term forecasting error comparison of different methods using CATS data.
\r\n\tThe discovery of Nylon by Wallace Hume Carothers, a Harvard-educated world-renowned organic chemist born in Burlington, IA in 1896, successfully crowned the attempts developed by E.I du Pont de Nemours & Company to investigate the structure of high molecular weight polymers and to synthesize the first synthetic polymeric fibre.
\r\n\tWhen it hit the market, it was in the form of stockings and all the women in the US wanted to get their hands on a pair. Despite the successful launch of Nylon on the synthetic fibre market and the high expectations created by its extraordinary features, the unexpected war events in 1941 diverted the production of the new synthetic fibre almost exclusively on military applications. Parachutes, ropes, bootlaces, fuel tanks, mosquito nets and hammocks absorbed the production of Nylon, which helped to determine the WWII events. When the war ended and production returned to pre-war levels, consumers rushed to the department stores in search of stockings, accessories and high-fashion garments.
\r\n\tEven if the world of high fashion now seems to more appreciate the use of natural fibres, Nylon is one of the most widely used polymers for the production of technical fibres and fabrics, automotive and micromechanical components. The global nylon 6 & 66 market is expected to reach USD 41.13 billion by 2025, by the following growth at 6.1% CAGR owing to the Increasing focus on fuel-efficient and less polluting vehicles.
\r\n\t
\r\n\tThe amazing success story of Nylon still continues. While its wide availability inspired the development of innovative applications, such as the additive manufacturing, on the other hand, proper disposal after use of high amounts of Nylon resin energised the development of efficient recycling methodology, including chemical recycling. Moreover, the production of Nylon precursors from biomass has become desirable due to the depletion of fossil hydrocarbons and to reduce greenhouse gas (GHG) emissions. This unique combination of technical and socio-economic driving forces is one that aims to further promote the development of Nylon as one of the most suitable ""best polymers"" with a low ecological footprint.
\r\n\t
\r\n\tThe aim of this publication is to unveil the relationships between the chemical structure and the outstanding properties of the broad family of polyamides and to describe the most recent use of Nylon in fostering new applications and promoting a culture aware of environmental sustainability.
Normal fertility was previously defined as the ability to conceive within 2 years of regular unprotected sexual intercourse [1, 2]. Recently, infertility has been defined as failure to conceive within 1 year of regular unprotected sexual intercourse in women less than 35 years old, or within 6 months of unprotected sexual intercourse in women older than 35 years old [2, 3].
According to a World Health Organization (WHO) report, the prevalence of infertility has increased since 1990, and the most recent data from 2010 estimated the worldwide incidence of infertile couples to be approximately 48.5 million (Figure 1) [4]. In the United States, the percentage of married women aged 15–44 years old who were infertile decreased from 8.5% in 1982 (2.4 million women) to 6.0% (1.5 million) in 2006–2010 [5]. In Taiwan, the total female fertility rate decreased from 7.04 million in 1951 to 1.175 million in 2015 [6]. Taiwan has thus become one of the countries with the lowest fertility rates in the world. In China, the 1‐ and 2‐year infertility rates in newly married couples were 12.5 and 6.6%, respectively [7]. In 2004, a WHO evaluation of Demographic and Health Surveys (DHS) data estimated that more than 186 million ever‐married women of reproductive age were infertile, translating into one in every four couples [8].
Worldwide prevalence of primary female infertility (2010). Infertility prevalence is indexed based on the age of the female partner; age‐standardized prevalence among women aged 20–44 years old is shown here [4].
A low fertility rate results in a low birth rate and an aging country. According to the Taiwan Population Policy White Paper, the total population of Taiwan is expected to decrease after 2022 [9]. A review showed that infertility or subfertility is associated with intimate partner violence (IPV) in low‐ and middle‐income countries (LMICs) [10]. Certain fertility treatments may increase the risk of ovarian or breast cancer [11, 12], while others have poor pharmacological efficacy in infertile women older than 40 years [13]. Around one‐fifth of all subfertile couples seeking fertility treatment have clinically significant levels of depression, anxiety, and suffering, but the effects of educational and psychological interventions on mental health outcomes and pregnancy outcomes including distress, and live birth or ongoing pregnancy rates, is unclear owing to the very low quality of evidence [14].
Around 15% of couples have difficulty conceiving [15]. The etiology of infertility can be broadly classified into female‐ and male‐related factors. Among distinguishable female factors, which are responsible for 81% of female infertility, the most common are ovulatory disorders (25%), endometriosis (15%), pelvic adhesions (12%), tubal blockage (11%), other tubal abnormalities (11%), and hyperprolactinemia (7%) [16]. Male factor infertility affects around 30–55% of all couples and is the most difficult form of infertility to treat [17]. The causes of male infertility include environmental disruptors, genetic defects, physiological and endocrine failure, and testicular pathologies [18].
\nAccording to the theory of traditional Chinese medicine, syndromes of female infertility can be classified as the following: kidney deficiency, stagnation of liver qi, static blood blocking in uterine, and accumulation of phlegm‐wetness in the body. Constitution has a great effect on the syndrome patterns of many diseases including infertility. A report showed that the constitution ratio of yang deficiency, phlegm damp, and yin deficiency was 29.5, 20.0, and 21.0%, respectively, in sterility patients [19]. This report also pointed that kidney deficiency syndrome was positively correlated with yin deficiency and yang deficiency constitution, blood stasis syndrome was positively associated with blood stasis and yang deficiency constitution, and liver stagnation syndrome was positively correlated with phlegm damp and damp heat constitution.
\nA history of regular menses with molimina (breast tenderness, bloating, cramping, mood changes) is suggestive of ovulation in the majority (95%) of women [20]. Anovulation and oligo‐ovulation lead to infertility because an oocyte is not available every month for fertilization. A woman\'s fecundability reaches a peak in her late‐twenties and decreases with advancing age, with a more rapid decline after her mid‐thirties [21]. Ovarian ageing causes a progressive loss of the finite pool of primordial follicles and a decrease in the quality of oocytes, mainly because of the accumulation of chromosomal abnormalities [22]. Polycystic ovarian syndrome (PCOS) is a common ovulatory disorder, and patients with this diagnosis are often obese and oligomenorrheic. They tend to have difficulty conceiving owing to ovulatory dysfunction as well as poor oocyte quality and endometrial receptivity [23]. However, the results of epidemiologic data obtained mainly from comparative studies and cohorts, have concluded that the role and size (<6 cm) of ovary cysts in infertility is controversial [24].
The rate of infertility among women with endometriosis ranges from 30 to 50%, and dysfunction is due to various mechanisms including pelvic adhesions, abnormal tubal transport, implantation defects, and intraperitoneal inflammation, which can decrease oocyte quality or oocyte‐sperm interactions [25].
Most adhesions occur after surgical procedures, but can also occur following infection, ischemia, endometriosis, or reaction to a foreign body, and these cause infertility by distorting pelvic anatomy and by blocking the fallopian tubes [26]. Peritubal adhesions negatively affect ovum transportation, while periovarian adhesions inhibit ovum release and ovulatory function [27, 28].
Tubal anomalies that contribute to infertility include congenital absence and major diverticula, duplication of the tubes, tubal occlusion, and hydrosalpinx [29, 30]. Other causes of tubal infertility include endometriosis, intrauterine contraceptive devices, infections (gonorrhea, chlamydia trachomatis, and genital tuberculosis), and postoperative complications of abdominal surgery [31].
Symptoms of hyperprolactinemia include amenorrhea, oligomenorrhea, infertility, decreased sexual desire, and habitual abortion. Women may also have signs of chronic hyperandrogenism such as acne and hirsutism, which may be related to increasing dehydroepiandrosterone sulfate (DHEAS) secretion from the adrenal glands [32].
History taking and physical examination are paramount to understanding the etiology of the infertility, and should be undertaken in both partners after 1 year of trying to conceive. In certain cases, investigation is indicated after 6 months of unprotected intercourse, such as when the female partner is over 35 years old or has a history of oligomenorrhea or amenorrhea, known or suspected endometriosis or tubal disorders, a past history of chemotherapy or radiation therapy, and in couples in which the male partner is known to be subfertile.
\nA menstrual history (menstrual interval and characteristics) should be elicited in all female patients in order to understand their ovulatory cycles. For instance, regular monthly cycles with premenstrual symptoms (breast tenderness, ovulatory pain, bloating) suggest that the patient is ovulating, whereas severe dysmenorrhea may indicate endometriosis.
\nA personal and lifestyle history should also be obtained from all infertile couples, including details about occupations; ages; stressors; levels of exercise; diets; and consumption of alcohol, tobacco, and other substances which can influence fertility [33]. It is also important to take a sexual history, including an evaluation of the frequency of intercourse and of underlying problems such as sexual dysfunction. Infrequent or inappropriately timed sexual intercourse can result in infertility.
\nClinicians should elicit a full medical, surgical, and obstetric history including information about the number of previous pregnancies, type and number of deliveries, and number of abortions (including spontaneous and induced) [32]. A gynecological history should evaluate any history of pelvic inflammatory disease, sexually transmitted infections, and treatment of abnormal pap smears, as well as uncover any history of procedures or medications, which could be related to infertility. In addition, a review of systems should be conducted in order to evaluate whether a patient has symptoms of dyspareunia, hypo‐/hyperthyroidism, pelvic or abdominal pain, galactorrhea, or hirsutism [34].
\nA family history of infertile couples should record in detail whether any family members have birth defects, mental retardation, genetic mutations, or fertility issues. The most common inherited cause of infertility is fragile X syndrome, which presents as premature ovarian failure (POF) in women, and which can lead to developmental delay or learning problems in men [35].
The physical examination can uncover signs indicative of latent causes of infertility. The patient\'s body mass index (BMI) and fat distribution should be measured and calculated, as an abnormally low BMI is related to infertility, whereas abdominal obesity is associated with insulin resistance [34].
\nIn women, the presence of vaginal and cervical discharge or anatomic abnormalities may indicate an underlying infection or Müllerian anomaly, respectively. If the uterus is enlarged, irregular, or lacks mobility, this may suggest the presence of a uterine abnormality such as endometriosis, leiomyoma, or pelvic adhesions. Chronic pelvic inflammatory disease or endometriosis presents with tenderness, or with masses in the adnexa or posterior cul‐de‐sac (pouch of Douglas), while endometriosis also has palpable tender nodules at the rectovaginal septum or uterosacral ligaments [35].
\nPatients with Turner syndrome have absent periods, and distinctive morphological features including a squarely shaped chest, and a stocky, short body habitus. Those with hypogonadotropic hypogonadism have primary amenorrhea and unremarkable secondary sexual characteristics. The presence of galactorrhea, thyroid gland anomalies, or signs of androgen excess such as acne, hirsutism, virilization, and male pattern baldness indicate an endocrinopathy (e.g., polycystic ovarian syndrome, adrenal disorders, hyper‐ or hypothyroidism, hyperprolactinemia) [33, 35].
\nIn men, anatomic abnormalities or discharge from the penis, scrotum, and urethral meatus may indicate the presence of an inguinal/femoral/scrotal hernia, cryptorchidism, or infection. Varicoceles, usually noted on the left side, are associated with infertility, whereas small, hard testes (<2 cm long) are suggestive of Klinefelter\'s syndrome [36].
Additional essential infertility evaluations include semen analyses, assessment of ovulatory function by laboratory tests, and a hysterosalpingogram to uncover underlying uterine abnormalities and evaluate tubal patency.
\nDiagnostic laparoscopy is recommended for women with suspected pelvic adhesions or endometriosis, during which chromotubation can assess tubal patency and hysteroscopy can assess the uterine cavity [29, 30].
\nWomen older than 35 years and those younger but with risk factors for POF should measure estradiol and follicle‐stimulating hormone (FSH) levels on day 3 in order to evaluate the ovarian reserve. Other tests such as antral follicle count, level of anti‐Müllerian hormone (AMH), and the clomiphene citrate challenge test (CCCT) should be performed if necessary [35].
Assisted reproductive technology (ART), such as artificial insemination (AI), in vitro fertilization and embryo transfer (IVF‐ET), and intracytoplasmic sperm injection (ICSI), is responsible for up to 4% of infants born in developing countries.
\nART has several iatrogenic complications including the risk of multiples, low luteal phase insufficiency, disabled embryo implantation, ovarian hyperstimulation syndrome (OHSS), and other perinatal and long‐term health conditions [37]. Furthermore, fertility treatments, especially IVF‐ET and ICSI, are costly and risky [38].
\nIntra‐uterine insemination (IUI), one form of AI, is a commonly used fertility treatment for couples with cervical factor infertility, unexplained subfertility, and subfertility in women with endometriosis after surgical resection [39]. IUI is more useful in some types of severe sexual dysfunction such a severe ejaculatory dysfunction or vaginismus; in cases of cervical factor or mild male factor infertility; and to prevent the transmission of sexually transmitted diseases such as hepatitis B/C virus (HBV/HCV) and human immunodeficiency virus (HIV) [40].
\nIt is a relatively simple surgical procedure whereby semen that has been washed in the laboratory is inserted into the uterine cavity by using a small catheter at the time of ovulation. IUI allows sperm to bypass the potentially hostile cervix, and thus increases the number of sperm that reach the uterine cavity and oocyte [40]. The technique can be performed either with or without added medications to encourage ovarian hyperstimulation (OH). In the latter method, follicular growth is monitored either via regular ultrasound monitoring to visualize the follicles or by measuring the preovulatory luteinizing hormone level rise in the serum or urine. In the former, ovulation is induced by an injection of human chorionic gonadotropin (hCG). Timed intercourse (TI), a less invasive method than IUI, involves giving couples information about cycle monitoring so that they can time intercourse appropriately.
\nNevertheless, a systematic review revealed that there is no difference in live birth or multiple pregnancy rates in most couples with unexplained subfertility treated with either IUI or TI, both with and without OH [39].
Traditional Chinese medicine (TCM) formulas have been used to treat female and male infertility for hundreds of years. Classically, the TCM formulas are combined with several single herbs to treat a specific disease. Some of the single herbs that have been used in women include Semen Cuscutae, Semen Lantaginis, Herba Leonuri Japonici, and Fructus Ligustri Lucidi, and were first recorded in the Chinese classic Shi Jing (the Book of Songs) over 2000 years ago.
\nThe results of one systematic review revealed that treatment with Chinese herbal medicine can increase pregnancy rates 2‐fold in a 4‐month period compared to western fertility drugs or IVF [41]. This report also stated that evaluating the quality of the menstrual cycle is essential in order to effectively treat female infertility by TCM. However, in a recent well‐controlled clinical trial, there was no significant difference in fertility outcomes after laparoscopy between women with minimal/mild endometriosis treated with oral contraceptives (OC), or OC and Dan&e mixture (composed of six herbs) [42]. A study from the National Health Insurance Research Database (NHIRD) in Taiwan has revealed the most commonly used TCM formulas for the management of female infertility [43]. At the top of the list are Dang‐Gui‐Sha‐Yao‐San and Wen‐Jing‐Tang; the former is used for abdominal pain during pregnancy, while the latter is used for dysmenorrhea and infertility, and acts by promoting blood circulation to prevent blood stasis, by warming the meridians to dissipate cold, and by tonifying qi to nourish the blood. The herbal formulas and single herbs commonly used for the treatment of female infertility are described below. The effects of these herbs on the endocrine regulation effects of the menstrual cycle and the ovulation rate will also be explored. Other commonly prescribed formulas that are used to relieve infertility‐related symptoms and diseases such as premenstrual syndrome (Jia‐Wei‐Xiao‐Yao‐San), irregular menstrual cycles (Zou‐Gui‐Wan, You‐Gui‐Wan), uterine fibroids (Gui‐Zhi‐Fu‐Ling‐Wan), diarrhea during menstruation (Shen‐Ling‐Bai‐Zhu‐San), dysmenorrhea (Shao‐Fu‐Zhu‐Yu‐Tang), abnormal uterine bleeding (Gui‐Pi‐Tang), amenorrhea, or oligomenorrhea (Si‐Wu‐Tang), do not fall within the scope of this review.
A recent study revealed that changes in the metabolic pathways, which regulate aromatic amino acids, tricarboxylic acid cycle, and sphingolipid metabolism may play an important role in the origin of Kidney‐Yang deficiency syndrome (KYDS)‐associated male infertility [44]. This research offered a new way for metabolomics analysis of seminal plasma to differentiate TCM syndromes of infertile males. The Chinese medicine Huzhangdanshenyin is used for male immune‐factor infertility and has been shown to be more effective than prednisone [45]. The medicine works by improving the antisperm‐antibody‐reversing ratio; and ameliorating sperm indexes such as sperm motility, viability, and density, without severe adverse effects. Another Chinese medicine, Bushen Shengjing Decoction (BSSJD), has the effect of decreasing semen levels of reactive oxygen species (ROS), improving the quality of sperm, and increasing the natural fecundity of patients with severe oligospermia and azoospermia (SOA), thus raising the viability of their sperm in order to increase the ovarian fertilization rate and clinical pregnancy rate in ICSI cycles [46].
Dang‐Gui‐Sha‐Yao‐San consists of Angelicae sinensis Radix, Paeoniae Radix, Poriz, Atractylodis ovatae Rhizoma, Alismatis Rhizoma, and Ligustici Rhizoma. According to the principles of TCM, Dang‐Gui‐Shao‐Yao‐San has the effect of nourishing liver blood, invigorating the spleen, and eliminating wetness. A previous study which used a Grading of Recommendations Assessment, Development and Evaluation method (GRADE) to evaluate the quality of evidence for Dang‐Gui‐Sha‐Yao‐San concluded that this formula was likely to be beneficial and safe for the treatment of primary dysmenorrhea [47]. Another clinical study showed that it may be useful for resolving the symptoms of mild or moderate hypochromic anemia secondary to uterine myoma‐induced menorrhagia [48].
Wen‐Jing‐Tang consists of Cinnamomi Ramulus, Evodiae Fructus, Ligustici Rhizoma, Angelicae sinensis Radix, Paeoniae Radix, Zingiberis Rhizoma Recens, Moutan Radicis Cortex, Ophiopogonis Tuber, Pinelliae Tuber, Ginseng Radix, Glycyrrhizae Radix, and Asini Corii Gelatinum. According to the principles of TCM, Wen‐Jing‐Tang has the effect of promoting blood circulation to dispel blood stasis, of dispelling cold by warming the meridians, of benefiting qi, and of nourishing the blood. Wen‐Jing‐Tang has been shown to effectively regulate endocrine conditions such as plasma LH and estradiol levels in PCOS patients with ovulatory dysfunction without taking eight‐principle pattern identification into consideration. The study concluded that Wen‐Jing‐Tang can be used to treat PCOS in women with various constitutions (as determined by the matching theory of eight‐principle pattern identification) in clinical management [49]. Another report showed that combined therapy with Wen‐Jing‐Tang and clomiphene induced ovulation without OHSS in infertile patients who did not respond to clomiphene citrate alone [50].
Jia‐Wei‐Xiao‐Yao‐San consists of Moutan Radicis Cortex, Radix Paeoniae Rubra, Bupleuri Radix, Angelicae Sinensis Radix, Atractylodis Ovatae Rhizoma, Poria, Glycyrrhizae Radix, Zingiberis Rhizoma Recens, and Menthae Herba. According to the principles of TCM, Jia‐Wei‐Xiao‐Yao‐San disperses stagnated liver qi, suppresses heat, and nourishes the blood. According to the NHIRD in Taiwan, Jia‐Wei‐Xiao‐Yao‐San‐based Chinese herbal medicine combinations were most frequently used for PMS and primary dysmenorrhea [51]. However, the exact mechanism whereby Jia‐Wei‐Xiao‐Yao‐San improves fertility is unclear. In one study, Jia‐Wei‐Xiao‐Yao‐San had no effect on serum levels of E2 and FSH, but did improve climacteric symptoms, especially in patients with hormone replacement therapy resistance who strongly complained of psychological symptoms [52]. These findings imply that Jia‐Wei‐Xiao‐Yao‐San may affect fertility via as‐yet undiscovered mechanisms.
You‐Gui‐Wan consists of Rhizoma Rehmanniae Praeparata, Rhizoma Dioscoreae, Fructus Lycii, Fructus Corni, Eucommia ulmoides Oliv, Semen Cuscutae, Colla Cornus Cervi, Angelicae sinensis Radix, Radix Aconiti Praeparata, and Cinnamomum cassia Blume. According to the principles of TCM, You‐Gui‐Wan acts by gently reinforcing the Kidney‐Yang, supplementing body essence, and replenishing blood. Previous research has shown that You‐Gui‐Wan medicated serum can significantly increase the percentage of mature oocytes, and modulate mRNA expression of a number of signaling molecules including protein kinase A (PKA), cAMP‐response element binding protein (CREB), mitogen‐activated protein kinases (MAPK), protein kinase C (PKC), protein kinase G (PKG), maturation promoting factor (MPF), as well as concentrations of cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP), and nitric oxide (NO) [53]. It has also been reported that patients treated with You‐Gui‐Wan had higher rates of successful IVF compared to those treated with FSH (with or without normal serum). In animal studies, You‐Gui‐Wan has been shown to increase sperm fertilizing ability by increasing sperm acrosin activity and promoting the acrosome reaction, which resulted in a higher percentage of zygotes in mice treated with You‐Gui‐Wan compared to control mice [54].
Zou‐Gui‐Wan consists of Colla Cornus Cervi, Colla Plastri Testudinis, Rhizoma Rehmanniae Praeparata, Rhizoma Dioscoreae, Fructus Lycii, Fructus Corni, Radix Cyathulae, and Semen Cuscutae. According to the principles of TCM, You‐Gui‐Wan gently reinforces the Kidney‐Yang, supplements body essence, and replenishes the marrow. Zou‐Gui‐Wan has also been shown to affect gene expression within germ cells, whereas You‐Gui‐Wan has stronger effects on estradiol production during the differentiation of stem cells derived from human first trimester umbilical cords into oocyte‐like cells in vitro [55]. Another report showed that Zou‐Gui‐Wan promptly and effectively restores ovarian function in patients with POF after failed treatment with clomiphene citrate for 8 months [56].
Herba Cistanche, also called Rou Cong Rong in Chinese, originated from Cistanche deserticola Y.C. Ma. According to the principles of TCM, Herba Cistanche invigorates the kidney‐yin, and replenishes the vital essence and the blood. It is used as a roborant in a formula for chronic renal disease, impotence, female infertility, morbid leucorrhea, profuse menorrhagia, and senile constipation [57]. It also controls the hypothalamic‐pituitary‐adrenal (HPA) and HPG axes, which may induce a balanced and smooth sexual energy effect [58]. Herba Cistanche also has aphrodisiac effects and can increase serum levels of progesterone and testosterone, improve sperm count and sperm motility, and decrease the number of abnormal sperm [59].
Semen Cuscutae, also called Tu Si Zi in Chinese, originated from Cuscuta chinensis Lam. According to the principles of TCM, Semen Cuscutae tonifies the kidney and is also believed to arrest spontaneous emission and prevent abortion. It has a multitude of other uses, including antiaging and anti‐inflammatory, antiabortifacient, and aphrodisiac, among others [60]. One study has demonstrated that flavonoids obtained from semen cuscutae (FSCs) can be used in the treatment of ovarian endocrine dysfunction in psychologically stressed rats through increasing luteinizing hormone receptor (LHR) expression in the ovaries and estrogen receptor (ER) expression in the hippocampus, hypothalamus, and pituitaries, but without any effect on follicle‐stimulating hormone receptor (FSHR) expression in the ovaries [61]. Another study demonstrated that total flavones from semen cuscutae (TFSC) treatment can improve Kidney‐Yang deficiency symptoms by recovering the levels of testosterone and increasing androgen receptor (AR) mRNA and protein expression in the testicles and kidneys [62].
Herba Leonuri Japonici, commonly called Chinese motherwort, originated from Leonurus japonicus Houtt (Labiatae). Related variants of this species include Leonurus sibiricus auct. pl., Leonurus artemisia (Lour.) S.Y. Hu., Leonurus heterophyllus sweet, and Stachys artemisia Lour [63]. According to the principles of TCM, Herba Leonuri Japonici promotes blood flow to regulate menstruation and induces diuresis to alleviate edema. It is also referred to as Yi Mu Cao in Chinese, which translates literally into “beneficial herb for mothers,” and is used to manage dysmenorrhea, amenorrhea, menoxenia, lochia, edema, and other gynecological problems but is contraindicated in pregnancy due to the possibility of stimulating the uterus [63]. The aqueous extract from the aerial part of Leonurus artemisia has the potential to treat dysmenorrhea by increasing the serum progesterone level, inhibiting inflammation, relaxing uterine spasms, and decreasing prostaglandin F2α (PGF2α) and prostaglandin E2 (PGE2) concentrations in uterine smooth muscle [64].
As with TCM and single Chinese herbal therapy, acupuncture and moxibustion have also been used to treat female or male infertility for hundreds of years. Traditionally, acupuncture and moxibustion were performed by inserting needles or burning moxa sticks into specific points (acupoints) on the meridians. Acupuncture and moxibustion work by regulating energy flow, also called Qi in Chinese, over the meridians. Newer therapeutic methods include electro‐acupuncture (EA), laser‐acupuncture, burning moxa granules on the top of the needles, points pasting, and far‐infrared moxibustion. Some meridians or acupoints have been indicated for the management of gynecological or obstetric problems, and these include Taichong (LR 3), Taixi (KI 3), Sanyinchiao (SP 6), and Gongsun (SP 4). These points were the earliest recorded in the Chinese classic Huangdi Neijing (the Classic of Inner Canon of Huangdi) around the time of the Han dynasty, and in the Chinese classic Zhenjiu Jiayi Jing (the A‐B Classic of Acupuncture and Moxibustion) during the Jin dynasty.
\nGonadotropin‐releasing hormone (GnRH) is released by the hypothalamus and stimulates ovulation and sperm production in women and men, respectively. Thus, its deficiency contributes to both male and female infertility [65]. One study showed that repeated EA on the arcuate nucleus (Arc) can regulate the function of the HPG axis by suppressing Arc discharge, serum testosterone, sperm count, and GnRH mRNA expression [66]. Therefore, electrical stimulation may be an effective alternative to medications to regulate the HPG axis [67]. Another clinical trial in humans showed that 10‐Hz EA stimulation of the abdominal acupuncture points ST‐29 (guilai) increased testicular blood flow (TBF), but simple needle insertion and 2‐Hz EA stimulation did not [68]. The combination of acupuncture and moxibustion treatment has also been shown to increase the percentage of normal‐form sperm in infertile patients with oligoastenoteratozoospermia in a prospective, controlled, and blinded study, but the mechanisms remain unknown [69]. Another study revealed that acupuncture can improve quick sperm motility, increase the normal sperm ratio, and improve fertilization rates and embryo quality in cases of idiopathic male infertility with failed ICSI [70]. According to the results of a systemic review from China regarding the treatment of male infertility, acupuncture appears to be as effective as TCM and more effective than western medicine alone, and its ability to improve sperm concentration and increase the level of grade a pulse b sperm is increased when applied together with either TCM or western medicine [71].
There is evidence to suggest that acupuncture stimulation of acupoints of the conception vessel, spleen, kidney, and bladder meridians improves clinical symptoms in patients with diminished ovarian reserve (DOR), and also lowers serum FSH, LH, and estradiol [E(2)] levels through regulation of the hypothalamic‐pituitary‐ovarian (HPO) axis [72]. A randomized, prospective, controlled clinical study revealed that acupuncture during the luteal phase of IVF/ICSI cycles increased clinical pregnancy and ongoing pregnancy rates [73]. Acupuncture improves IVF outcomes through four potential mechanisms: (1) by increasing blood flow to the uterus; (2) by regulating neuroendocrinological factors and the ovaries; (3) by modulating cytokine levels; and (4) by decreasing levels of anxiety, stress, and depression [74]. A successful pregnancy relies on the presence of adequate uterine blood flow and endometrial thickness, and these factors are especially important in pregnancies conceived through IVF and ET [67]. A study of infertile women with a high pulsatility index (PI) and downregulated with a GnRH analog to exclude any fluctuating endogenous hormone effects on the PI, revealed that EA reduced uterine artery blood flow impedance [75]. However, the literature on the efficacy of acupuncture treatment for endometriosis, immune and pelvic inflammatory disease‐related infertility or subfertility is sparse. One study on women with steroid‐induced polycystic ovaries demonstrated that EA modulates the neuroendocrinological state of the ovaries by inhibiting endothelin‐1 and nerve growth factor (NGF), and NGF mRNA expression, most likely by modulating sympathetic activity in the ovaries [76]. Another similar study on estradiol valerate‐induced polycystic ovaries demonstrated that EA treatments change the neuroendocrinological state in the ovaries by suppressing corticotropin‐releasing factor, which may play an important role in reproductive failure [77]. Finally, the results of the Fertility Problem Inventory (FPI) and Beck Anxiety Inventory (BAI) questionnaires revealed that women suffer greater anxiety and sexual infertility stress than men [78]. High levels of stress affect female hormone levels and disrupt ovulation by affecting the HPO axis [67]. These studies all highlight a need for additional research into the potential benefits of acupuncture and moxibustion for the management of infertile patients.
Infertility results in a country with a low birth rate and an aging population, and thus there is vested interest in treating this problem by using both complementary and alternative therapies, in addition to conventional western medicine. There is increasing scientific evidence to support a role for TCM in the management of male and female infertility, but further studies are needed to elucidate the efficacy of this alternative therapy.
AI | artificial insemination |
AMH | anti‐Müllerian hormone |
AR | androgen receptor |
ART | assisted reproductive technology |
BAI | Beck Anxiety Inventory |
BMI | body mass index |
BSSJD | Bushen Shengjing Decoction |
cAMP | concentrations of cyclic adenosine monophosphate |
CCCT | the clomiphene citrate challenge test |
cGMP | cyclic guanosine monophosphate |
CREB | cAMP‐response element binding protein |
DOR | diminished ovarian reserve |
E2 | estradiol |
EA | electro‐acupuncture |
ER | estrogen receptor |
FPI | Fertility Problem Inventory |
FSCs | flavonoids from Semen cuscutaes |
FSH | follicle‐stimulating hormone |
FSHR | follicle‐stimulating hormone receptor |
GnRH | gonadotropin‐releasing hormone |
GnRHa | gonadotrophin‐releasing hormone analogue |
GRADE | Grading of Recommendations Assessment, Development and Evaluation |
HBV/HCV | hepatitis B/C virus |
hCG | human chorionic gonadotropin |
HIV | human immunodeficiency virus |
HPG | hypothalamic‐pituitary‐gonad |
ICSI | intracytoplasmic sperm injection |
IUI | intra‐uterine insemination |
IVF‐ET | in vitro fertilization and embryo transfer |
KYDS | Kidney‐Yang deficiency syndrome |
LH | luteinizing hormone |
LHR | luteinizing hormone receptor |
MAPK | mitogen‐activated protein kinases |
MPF | maturation‐promoting factor |
mRNA | messenger ribonucleic acid |
NGF | nerve growth factor |
NHIRD | National Health Insurance Research Database |
NO | nitric oxide |
OCs | oral contraceptives |
OH | ovarian hyperstimulation |
OHSS | ovarian hyperstimulation syndrome |
Pap | Papanicolaou |
PCOS | polycystic ovary syndrome |
PGE2 | prostaglandin E2 |
PGF2α | prostaglandin F2α |
PHA | hypothalamic‐pituitary‐adrenal |
PKA | protein kinase A |
PKC | protein kinase C |
PKG | protein kinase G |
PMS | premenstrual syndrome |
POF | premature ovarian failure |
ROS | reactive oxygen species |
SOA | oligospermatism and azoospermia |
TBF | testicular blood flow |
TCM | traditional Chinese medicine |
TFSC | total flavones from Semen cuscutae |
TI | timed intercourse |
WHO | World Health Organization |
Artificial neural networks (ANN), which are mathematical models for function approximation, classification, pattern recognition, nonlinear control, etc., have been successfully applied in the field of time series analysis and forecasting instead of linear models such as 1970s ARIMA [1] since 1980s [2, 3, 4, 5, 6, 7]. In [2], Casdagli used a radial basis function network (RBFN) which is a kind of feed-forward neural network with Gaussian hidden units to predict chaotic time series data, such as the Mackey-Glass, the Ikeda map, and the Lorenz chaos in 1989. In [3, 4], Lendasse et al. organized a time series forecasting competition for neural network prediction methods with a five-block artificial time series data named CATS since 2004. The goal of CATS competition was to predict 100 missing values of the time series data in five sets which included 980 known values and 20 successive unknown values in each set (details are in Section 3.1). There were 24 submissions to the competition, and five kinds of methods were selected by the IJCNN2004: filtering techniques including Bayesian methods, Kalman filters, and so on; recurrent neural networks (RNNs); vector quantization; fuzzy logic; and ensemble methods. As the comment of the organizers, the different prediction precisions were reported though the similar prediction methods were used for the know-how and experience of the authors. So the development of time series forecasting by ANN is still on the way.
\nAs a kind of classifiers or a kind of function approximators, the advances of the ANN are bought out by the nonlinear transforms to the input space. In fact, units (or neurons) with nonlinear firing functions connected to each other usually produce higher dimensional output space and various feature spaces in the networks. Additionally, as a connective system, it is not necessary to design fixed mathematical models for different nonlinear phenomena, but adjusting the weights of connections between units. So according to the report of NN3—Artificial Neural Networks and Computational Intelligence Forecasting Competition [5], there have been more than 5000 publications of time series forecasting using ANN till 2007.
\nTo find the suitable parameters of ANN, such as weights of connections between neurons, error back-propagation (BP) algorithm [6] is generally utilized in the training process of ANN. However, due to every sample data (a pair of the input data and the output data) is used in the BP method, noise data influences the optimization of the model, and robustness of the model becomes weak for unknown input. Another problem of ANN models is how to determine the structure of the network, i.e., the number of layers and the number of neurons in each layer. To overcome these problems of BP, Kuremoto et al. [7] adopted a reinforcement learning (RL) method “stochastic gradient ascent (SGA)” [8] to adjust the connection weights of units and the particle swarm optimization (PSO) to find the optimal structure of ANN. SGA, which is proposed by Kimura and Kobayshi, improved Williams’ REINFORCE [9], which uses rewards to modify the stochastic policies (likelihood). In SGA learning algorithm, the accumulated modification of policies named “eligibility trace” is used to adjust the parameters of model (see Section 2). In the case of time series forecasting, the reward of RL system can be defined as a suitable error zone to instead of the distance (error) between the output of the model and the teach data which is used in BP learning algorithm. So the sensitivity to noise data is possible to be reduced, and the robustness to the unknown data may be raised. As a deep learning method for time series forecasting, Kuremoto et al. [10] firstly applied Hinton and Salakhutdinov’s deep belief net (DBN) which is a kind of stacked auto-encoder (SAE) composed by multiple restricted Boltzmann machines (RBMs) [11]. An improved DBN for time series forecasting is proposed in [12], which DBN is composed by multiple RBMs and a multilayer perceptron (MLP) [6]. The improved DBN with RBMs and MLP [6] gives its priority to the conventional DBN [5] for time series forecasting due to the continuous output unit is used; meanwhile the conventional one had a binary value unit in the output layer.
\nAs same as the RL method, SGA adopted to MLP, RBFN, and self-organized fuzzy neural network (SOFNN) [7]; the prediction precision of DBN utilized SGA may also be raised comparing to the BP learning algorithm. Furthermore, it is available to raise the prediction precision by a hybrid model which forecasts the future data by the linear model ARIMA at first and modifying the forecasting by the predicted error given by an ANN which is trained by error time series [13, 14].
\nIn this chapter, we concentrate to introduce the DBN which is composed by multiple RBMs and MLP and show the higher efficiency of the RL learning method SGA for the DBN [15, 16] comparing to the conventional learning method BP using the results of time series forecasting experiments. Kinds of benchmark data including artificial time series data CATS [3], natural phenomenon time series data provided by Aalto University [18], and TSDL [18] were used in the experiments.
\nThe model of time series forecasting is given as the following:
\nDenote t = 1, 2, 3, …, where T is the time, n is the dimensionality of the input of function f(x), \n
A deep belief net (DBN) composed by restricted Boltzmann machines (RBMs) and multilayer perceptron (MLP) is shown in Figure 1.
\nThe structure of DBN for time series forecasting.
Restricted Boltzmann machine (RBM) is a kind of probabilistic generative neural network which composed by two layers of units: visible layer and hidden layer (see Figure 2).
\nThe structure of RBM.
Units of different layers connect to each other with weights \n
Here \n
where \n
Multilayer perceptron (MLP) is the most popular neural network which is generally composed by three layers of units: input layer, hidden layer, and output layer (see Figure 3).
\nThe structure of MLP.
The output of the unit \n
Here n is the dimensionality of the input, K is the number of hidden units, and \n
The learning rules of MLP using error back-propagation (BP) method [5] are given as follows:
\nwhere \n
The learning algorithm of MLP using BP is as follows:
\nStep 1. Observe an input \n
Step 2. Predict a future data \n
Step 3. Calculate the modification of connection weights, \n
Step 4. Modify the connections,
\nStep 5. For the next time step \n
As same as the training process proposed in [10], the training process of DBN is performed by two steps. The first one, pretraining, utilizes the learning rules of RBM, i.e., Eqs. (4–6), for each RBM independently. The second step is a fine-tuning process using the pretrained parameters of RBMs and BP algorithm. These processes are shown in Figure 4 and Eqs. (11)–(13).
\nThe training of DBN by BP method.
In the case of reinforcement learning (RL), the output is decided by a probability distribution, e.g., the Gaussian distribution \n
The learning algorithm of stochastic gradient ascent (SGA) [7] is as follows.
\nStep 1. Observe an input \n
Step 2. Predict a future data \n
Step 3. Receive a scalar reward/punishment \n
where \n
Step 4. Calculate characteristic eligibility \n
where \n
Step 5. Calculate the modification \n
where \n
Step 6. Improve the policy Eq. (16) by renewing its internal variable \n
where \n
Step 7. For the next time step \n
Characteristic eligibility \n
The calculation of \n
The \n
The learning rate \n
where is \n
The learning errors given by different learning rates.
The number of RBM that constitute the DBN and the number of neurons of each layer affects prediction performance seriously. In [9], particle swarm optimization (PSO) method is used to decide the structure of DBN, and in [13] it is suggested that random search method [16] is more efficient. In the experiment of time series forecasting by DBN and SGA shown in this chapter, these meta-parameters were decided by the random search, and the exploration limits are shown as the following.
The number of RBMs: [0–3]
The number of units in each layer of DBN: [2–20]
Fixed learning rate of SGA in Eq. (21): [10−5–10−1]
Discount factor in Eq. (19): [10−5–10−1]
Coefficient in Eq. (27) [0.5–2.0]
The optimization algorithm of these meta-parameters by the random search method is as follows:
\nStep 1. Set random values of meta-parameters beyond the exploration limitations.
\nStep 2. Predict a future data \n
Step 3. If the error between \n
or else if the error is not changed,
\nstop the exploration,
\nelse return to step 1.
\nCATS time series data is the artificial benchmark data for forecasting competition with ANN methods [3, 4].This artificial time series is given with 5000 data, among which 100 are missed (hidden by competition the organizers). The missed data exist in five blocks:
Elements 981 to 1000
Elements 1981 to 2000
Elements 2981 to 3000
Elements 3981 to 4000
Elements 4981 to 5000
The mean square error \n
where \n
CATS benchmark data.
The prediction results of different blocks of CATS data are shown in Figure 7. Comparing to the conventional learning method of DBN, i.e., using Hinton’s RBM unsupervised learning method [6, 8] and back-propagation (BP), the proposed method which used the reinforcement learning method SGA instead of BP showed its superiority in the sense of the average prediction precision E1 (see Figure 7f). In addition, the proposed method, DBN with SGA, yielded the highest prediction (E1 measurement) comparing to all previous studies such as MLP with BP, the best prediction of CATS competition IJCNN’04 [4], the conventional DBNs with BP [9, 11], and hybrid models [13]. The details are shown in Table 1.
\nThe prediction results of different methods for CATS data: (a) block 1; (b) block 2; (c) block 3; (d) block 4; (e) block 5; and (f) results of the long-term forecasting.
Method | \nE1 | \n
---|---|
DBN(SGA) [18] | \n170 | \n
DBN(BP) + ARIMA [14] | \n244 | \n
DBN [11] (BP) | \n257 | \n
Kalman Smoother (the best of IJCNN ‘04) [4] | \n408 | \n
DBN [9] (2 RBMs) | \n1215 | \n
MLP [9] | \n1245 | \n
A hierarchical Bayesian learning (the worst of IJCNN ‘04) [4] | \n1247 | \n
ARIMA [1] | \n1715 | \n
ARIMA+MLP(BP) [12] | \n2153 | \n
ARIMA+DBN(BP) [14] | \n2266 | \n
The long-term forecasting error comparison of different methods using CATS data.
The meta-parameters obtained by random search method are shown in Table 2. And we found that the MSE of learning, i.e., given by one-ahead prediction results, showed that the proposed method has worse convergence compared to the conventional BP training. In Figure 8, the case of the first block learning MSE of two methods is shown. The convergence of MSE given by BP converged in a long training process and SGA gave unstable MSE of prediction. However, as the basic consideration of a sparse model, the better results of long-term prediction of the proposed method may successfully avoid the over-fitting problem which is caused by the model that is built too strictly by the training sample and loses its robustness for unknown data.
\n\n | DBN with SGA | \nDBN with BP | \n
---|---|---|
The number of RBMs | \n3 | \n1 | \n
Learning rate of RBM | \n0.048-0.055-0.026 | \n0.042 | \n
Structure of DBN (the number of units and layers) | \n14-14-18-19-18-2 | \n5-11-2-1 | \n
Learning rate of SGA or BP | \n0.090 | \n0.090 | \n
Discount factor \n | \n0.082 | \n— | \n
Coefficient \n | \n1.320 | \n— | \n
Meta-parameters of DBN used for the CATS data (block 1).
Change of the learning error during fine-tuning (CATS data [1–980]).
Three types of natural phenomenon time series data provided by Aalto University [17] were used in the one-ahead forecasting experiments of real time series data.
CO2: Atmospheric CO2 from continuous air samples weekly averages atmospheric CO2 concentration derived from continuous air samples, Hawaii, 2225 data
Sea level pressures: Monthly values of the Darwin sea level pressure series, A.D. 1882–1998, 1300 data
Sunspot number: Monthly averages of sunspot numbers from A.D. 1749 to the present 3078 values
The prediction results of these three datasets are shown in Figure 9. Short-term prediction error is shown in Table 3. DBN with the SGA learning method showed its priority in all cases.
\nPrediction results by DBN with BP and SGA. (a) Prediction result of CO2 data. (b) Prediction result of Sea level pressure data. (c) Prediction result of Sun spot number data.
Data | \nDBN with BP | \nDBN with SGA | \n
---|---|---|
CO2 | \n0.2671 | \n0.2047 | \n
Sea level pressure | \n0.9902 | \n0.9003 | \n
Sun spot number | \n733.51 | \n364.05 | \n
Prediction MSE of real time series data [17].
The efficiency of random search to find the optimal meta-parameters, i.e., the structure of RBM and MLP, learning rates, discount factor, etc. which are explained in Section 2.5 is shown in Figure 10 in the case of DBN with SGA learning algorithm. The random search results are shown in Table 4.
\nChanges of learning error by random search for DBN with SGA.
Data series | \nTotal data | \nTesting data | \nDBN with BP (the number of units) | \nDBN with SGA (the number of units) | \n
---|---|---|---|---|
CO2 | \n2225 | \n225 | \n15-17-17-1 | \n20-18-7-2 | \n
Sea level pressure | \n1400 | \n400 | \n16-18-18-1 | \n16-20-8-7-2 | \n
Sun spot number | \n3078 | \n578 | \n20-20-17-18-1 | \n19-19-20-10-2 | \n
Meta-parameters of DBN used for real time series forecasting.
We also used seven types of natural phenomenon time series data of TSDL [18]. The data to be predicted was chosen based on [19] which are named as Lynx, Sunspots, River flow, Vehicles, RGNP, Wine, and Airline. The short-term (one-ahead) prediction results are shown in Figure 11 and Table 5.
\nPrediction results of natural phenomenon time series data of TSDL. (a) Prediction result of Lynx; (b) prediction result of sunspots; (c) prediction result of river flow; (d) prediction result of vehicles; (e) prediction result of RGNP; (f) prediction result of wine; and (g) prediction result of airline.
Data | \nDBN with BP | \nDBN with SGA | \n
---|---|---|
Lynx | \n0.6547 | \n0.3593 | \n
Sunspots | \n999.54 | \n904.35 | \n
River flow | \n24262.24 | \n16980.46 | \n
Vehicles | \n6.0670 | \n6.1919 | \n
RGNP | \n771.79 | \n469.72 | \n
Wine | \n138743.80 | \n224432.02 | \n
Airline | \n380.60 | \n375.25 | \n
Prediction MSE of time series data of TSDL.
From Table 5, it can be confirmed that SGA showed its priority to BP except the cases of Vehicles and Wine. From Table 6, an interesting result of random search for meta-parameter showed that the structures of DBN for different datasets were different, not only the number of units on each layer but also the number of RBMs. In the case of SGA learning method, the number of layer for Sunspots, River flow, and Wine were more than DBN using BP learning.
\nSeries | \nTotal data | \nTesting data | \nDBN with BP | \nDBN with SGA | \n
---|---|---|---|---|
Lynx | \n114 | \n14 | \n19-16-1 | \n7-14-2 | \n
Sunspots | \n288 | \n35 | \n20-18-11-1 | \n10-12-12-17-2 | \n
River flow | \n600 | \n100 | \n20-17-18-1 | \n19-20-5-18-5-2 | \n
Vehicles | \n252 | \n52 | \n20-13-20-1 | \n20-11-5-2 | \n
RGNP | \n85 | \n15 | \n18-20-1 | \n19-15-2 | \n
Wine | \n187 | \n55 | \n16-15-12-1 | \n18-12-13-11-2 | \n
Airline | \n144 | \n12 | \n15-4-1 | \n13-7-2 | \n
Size of time series data and structure of prediction network.
The experiment results showed the DBN composed by multiple RBMs and MLP is the state-of-the-art predictor comparing to all conventional methods in the case of CATS data. Furthermore, the training method for DBN may be more efficient by the RL method SGA for real time series data than using the conventional BP algorithm. Here let us glance back at the development of this useful deep learning method.
Why the DBN composed by multiple RBMs and MLP [11, 13] is better than the DBN with multiple RBMs only [9]?
The output of the last RBM of DBN, a hidden unit of the last RBM in DBN, has a binary value during pretraining process. So the weights of connections between the unit and units of the visible layer of the last RBM are affected and with lower complexity than using multiple units with continuous values, i.e., MLP, or so-called full connections in deep learning architecture.
How are RL methods active at ANN training?
In 1992, Williams proposed to adopt a RL method named REINFORCE to modify artificial neural networks [8]. In 2008, Kuremoto et al. showed the RL method SGA is more efficient than the conventional BP method in the case of time series forecasting [6]. Recently, researchers in DeepMind Ltd. adopted RL into deep neural networks and resulted a famous game software AlphaGo [20, 21, 22, 23].
Why SGA is more efficient than BP?
Generally, the training process for ANN by BP uses mean square error as loss function. So every sample data affects the learning process and results including noise data. Meanwhile, SGA uses reward which may be an error zone to modify the parameters of model. So it has higher robustness for the noisy data and unknown data for real problems.
\nA deep belief net (DBN) composed by multiple restricted Boltzmann machines (RBMs) and multilayer perceptron (MLP) for time series forecasting were introduced in this chapter. The training method of DBN is also discussed as well as a reinforcement learning (RL) method; stochastic gradient ascent (SGA) showed its priority to the conventional error back-propagation (BP) learning method. The robustness of SGA comes from the utilization of relaxed prediction error during the learning process, i.e., different from the BP method which adopts all errors of every sample to modify the model. Additionally, the optimization of the structure of DBN was realized by random search method. Time series forecasting experiments used benchmark CATS data, and real time series datasets showed the effectiveness of the DBN. As for the future work, there are still some problems that need to be solved such as how to design the variable learning rate and reward which influence the learning performance strongly and how to prevent the explosion of characteristic eligibility trace in SGA.
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\n\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\n\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\n\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
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\n\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\n\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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