In financial markets, information constitutes a crucial factor contributing to the evolution of the system, while the presence of heterogeneous investors ensures its flow among financial products. When nonlinear trading strategies prevail, the diffusion mechanism reacts accordingly. Under these conditions, information englobes behavioral traces of traders’ decisions and represents their actions. The resulting effect of information endogenization leads to the revision of traders’ positions and affects connectivity among assets. In an effort to investigate the computational dimensions of this effect, we first simulate multivariate systems including several scenarios of noise terms, and then we apply direct causality tests to analyze the information flow among their variables. Finally, empirical evidence is provided in real financial data. During the last few decades, research in finance has pointed to biases and sub-optimal decision-making processes as the drivers of investors’ behavior. Many alternatives have been proposed so as to rethink the strong assumptions of rationality and perfect information. According to Lo and Mackinlay  and Lo , economiAlthough information constitutes a crucial factor contributing to the evolution of financial systems, heterogeneous investors ensure the flow of information. The trading decision, strongly affected by the linear or nonlinear underlying trading strategies and filtering of news, can give birth to new information that enters the diffusion mechanism of the market and spreads rapidly. In this process, information englobes behavioral traces of traders’ decisions and represents their actions. The endogenization of information creates risk that significantly affects traders’ positions, capital flows, connectivity among assets, and finally asset allocation. The observed non-normal distribution of asset returns is often attributed to the dominance of irrational investors following active trading strategies. Investors whose actions are affected by anchoring and disposition effects participate in the buildup of a trend-forcing price evolution. Thurner et al.  show thaDespite the simplistic character of the assumption of normality, the conventional approach to build a diversified portfolio has appeal due to the ease of implementation. In this framework, practitioners need only to consider for each asset class its mean, variance, and covariances, with the latter introducing the additional restrictive hypothesis of linear relationship for each pair of asset classes. Nevertheless, in dynamically unstable markets where information canalizes traders’ characteristics into prices, diversifying asset portfolios can become a complicated procedure. To this end, research on nonlinear analysis suggests alternative techniques to the standard mean-variance framework of Markowitz . BoginskAs follows, the trading-based non-normality is linked to high skewness and kurtosis in asset returns and can modify drastically the portfolio (basket of variables) structure. With the aim to investigate the computational dimensions of this effect, we use multivariate systems of variables where several scenarios of disturbances are considered. Then, a set of direct causality measures is employed to analyze the information flow among the variables. In this simulation exercise, the goal is to demonstrate that non-Gaussianity in a system is able to destabilize the fundamentally defined linkages. The impact becomes more pronounced when the initial connectiveness is nonlinear, which technically may be interpreted in terms of trading activity. The empirical validation of the effect of trading information on the variables’ connectiveness is provided through an application to a concentrated and mixed five-stock portfolio.In an attempt to concretize the effect of informational signals in the sense of random disturbances on the connectivity, we use three stochastic systems. Their residual terms are defined in different ways, so that the simulated time series obey irregular characteristics. The two main stochastic systems, often used in the literature for the evaluation of causality measures, with Gaussian noise terms are (i) a linear vector autoregressive (VAR) of order 4 in five variables  and  . To cont. Lagged information can also be transferred non-propositionally (nonlinear lagged X terms) into Xt, revealing that each variable at time t either over- or under-reacts to the past. Both conditions determine the spreading of information flow and feedback within the system.The presence of several lags in the simulated systems as well as the diversity in the nature of the relationships help establish a direct connection with the trading practice in financial markets. The systems evolve because of the combination of value signals ), since the current state of each variable depends on past information of the same or other variable. The delay in the system equations measures the speed at which imperfectly reflected information is incorporated into XThe system S1 is represented by the following set of equations.Based on S1, and by changing the distribution of noise terms S1t: S1 with noise terms t-Student distribution, with df = 2 degrees of freedom. The generated series exhibit leptokurtic behavior that increases with the sample. Among all the variables, X5, participating in more couplings than the rest, seems to be more sensitive to residual irregularity, having the highest kurtosis and varying skewness values.S1n: S1 with noise terms S1b: S1 with noise terms S1g: S1 with noise terms S1f: S1 with noise terms The system S2 is represented by the following set of equations.With the aim to complicate the structure of the driving variable in S3, X1 is modelled as a noisy Mackey Glass with c = 10 and τ = 2. The remaining equations are identical to those of S2.Although the system S2 is disturbed by normally distributed errors, as Based on S2 and S3, and by modifying the distribution of noise terms S2t and S3t: S2 and S3 with noise terms S2n and S3n: S2 and S3 with noise terms S2b and S3b: S2 and S3 with noise terms S2g and S3g: S2 and S3 with noise terms S2f and S3f: S2 and S3 with noise terms To provide a schematic description of the methodological part, we present the simulation experiment in four steps. First, we simulate the systems S1, S2, and S3. In the second step, we introduce irregularity in the noise terms, as described above. Then, we identify couplings using direct causality methods to capture the information flow. In the last step, performance metrics are applied to verify the consistency of the obtained results.After describing the systems, including both linear and nonlinear couplings, together with the irregular characteristics of the residual terms able to give rise to abnormal values for skewness and kurtosis, we apply three multivariate (direct) measures of causality, instead of bivariate ones, to better apprehend the information flow. More specifically, we intend to identify the impact of introducing non-Gaussianity in the stochastic systems S1, S2, and S3 into the connectivity among their variables. Let us consider a multivariate system with The Restricted Conditional Granger Causality Index (RCGCI) is an extension of the standard Conditional Granger Causality Index (Geweke ), includThe statistical significance of the RCGCI is assessed by a parametric significance test (F-statistic) on the coefficients of the lagged terms of the driving variable in the unrestricted model:In the Partial Mutual Information on Mixed Embedding (PMIME), the dimension reduction is effectuated via a non-uniform embedding scheme (Kugiumtzis ). The miTo obtain the probability densities in the estimation of the  mutual information terms, the nearest neighbors’ estimator  is emplRespectively, the Partial Transfer Entropy on Non-Uniform Embedding (PTENUE) is introduced using the non-uniform embedding scheme . AlthouThe measure equals zero if causality does not exist; otherwise, it is positive. The nonlinear causality measures PMIME and PTENUE do not require a significance test. Surrogates, though, are incorporated within the estimation algorithm of the measures to form the stopping criterion regarding the mixed embedding vector. Papana et al. , Papana In the fourth step, binary classification metrics such as the sensitivity, specificity, and Matthews correlation coefficient (Tharwat ) are empThe sensitivity metric—i.e., the true positive rate (TPR)—quantifies the true positives (TP) against the number of real positives (P) in the data.The term true or false refers to the correct or incorrect (spurious) coupling, while positive or negative means the acceptance or rejection of couplings, respectively. If the sensitivity approaches 100%, then more correct causal links are detected over the total link detections.The specificity metric—i.e., the true negative rate (TNR)—checks the true negatives (TN) against the number of real negatives (N) in the data.Thus, the specificity provides the percentage of rejection of spurious links over the total number of detected uncoupled cases. The percentage at which the specificity value deviates from 100% denotes the accepted spurious couplings.considering all the possible correct and spurious couplings, either causal or no causal. If it equals 100%, there is a perfect identification of the pairs of true and no causality.Finally, the Mathews’ correlation coefficient (MCC) (Matthews ) is a meAs reported in t-distribution is added to S1 (S1t), the performance of RCGCI is similar to that of S1. It finds the causal links perfectly well, while the percentages of significant detections for the uncoupled pairs of variables does not exceed 5%. The performance of PMIME does not deteriorate for S1t compared to S1. The true links are detected. However, the percentage of significant detections for the uncoupled pairs of variables varies from 9.85% to 12.85%. The PTENUE performs for S1t as for S1. The true links are found, and a few spurious acceptances arise. In total, the PTENUE has the best mean performance for S1t. RCGCI comes second, with an MCC value very close to that of PTENUE.When a noise term from the Including the GARCH residuals in S1 (S1n) worsens the metrics of the RCGCI. The measure captures the true causal linkages, but for the uncoupled cases the percentage of significant RCGCI values varies from 8.36% to 10.38%. The PMIME gives less acceptances of spurious causalities for S1n. The best performance for the system S1n is achieved by PTENUE.n (100%). Similar to S1, also a few spurious links are obtained. The PMIME identifies perfectly the true connections (100%) for all n. The uncoupled links are falsely indicated, with percentages that vary from 9.85% to 12.85%. The PTENUE indicates again the true connections, while a small number of spurious cases appear.When beta-distributed errors are considered for S1b, the RCGCI finds the true connectivity network for all In the case of system S1g, the RCGCI captures the true causalities, but for the uncoupled cases the percentage of significant RCGCI values remains high (from 7.77% to 9.23%). The PMIME performs almost similarly to the RCGCI. The PTENUE continues to be the best measure. When the FIGARCH errors are considered, the PMINE puts forward more false couplings.In the case of the t-distributed errors in S2, the RCGI correctly detects the true couplings. This percentage increases to 90% for the large sample. Spurious links are also revealed. This effect is more significant in the sample of 4096 observations. The PTENUE shows more true causal links, while fewer spurious relationships than the RCGCI and PMIME detect are found. In terms of performance, the PTENUE overcomes the PMIME. In the third system, the PMINE seems to be more sensitive to the nonlinearity, suggesting an increasing number of spurious couplings.For the systems S2n, S3n, and S2g, S3g considering the GARCH residual terms, the measures produce almost identical results. The RCGI indicates the true couplings, giving comparable percentages of acceptance with S2t. The spurious detections are high, approaching 30%. The PMIME captures effectively the true connections, but at the same time it shows spurious ones. According to the sensitivity metric, the PTENUE performs as well as the PMIME, but gives fewer wrong causalities. This performance is concretized through the highest mean MCC.Regarding S2b, the RCGCI captures efficiently both the linear and nonlinear linkages, and the percentage of spurious cases is lower than in the previously analyzed versions of the system S2. Both the PMIME and the PTENUE find the true connectivity. Nevertheless, the latter indicates a smaller number of spurious couplings. The high MCC values show that the PTENUE stands out. The significant difference between the system S3b refers to the poor performance of the RCGCI, since the number of false detections rises to 37.73%.Finally, when the FIGARCH error term is considered in S2 and S3, the linear measure fails significantly to indicate the correct causal relationships and suggests equally high false ones. The nonlinear tools perform better, giving a lower rate of false acceptances than RCGCI for S2, except PMINE for S3, which reaches 23.93% spurious couplings.Comparing the rate of acceptance of false causalities (specificity) between the systems S1, S2, and S3, we can conclude that more spurious couplings emerge in the nonlinear systems S2 and S3 due to the common source of driving  and the transitive indirect paths (X1→X4↔X5). It is also worth emphasizing that, among the spurious causalities, the bidirectional coupling between X2 and X4 (by definition uncoupled) is steadily detected by all measures in all samples. Therefore, we believe that the high rate of acceptance of couplings which are not derived from the initial formulation of the systems S2 and S3 indicates the creation of new structures because of the propagation.To further deploy our rationale, we calculate the mutual information among the variables of all versions of S2 and S3. As can be seen in On the contrary, in Regardless of the sample size of the nonlinear systems under study, we observe that a significant divergence in performance between the linear and the nonlinear causality measures is associated with nonlinear relationships among the variables. In this line, for all the causality forms and disturbance terms the simulation results show that the PTENUE decodes correctly the true linkages and gives lower rates of false couplings.With the aim to provide empirical evidence about the impact of heterogeneity in information on the connectivity of variables, we built two different stock portfolios. Following the simulated systems’ construction, the first portfolio A is composed of five stocks from the French stock exchange index CAC40 to reproduce the properties of a concentrated structure. The respective listed companies are Total (FP) from the energy sector, Sanofi (SAN) from the healthcare sector, L’Oreal (OR) and Danone (BN) from the consumer defensive sector, and BNP Paribas (BNP) from the financial services sector. The required heterogeneity is achieved by considering three big capitalization stocks  together with two lower capitalization stocks . In the second portfolio named B, the goal is to combine two different (preferably independent) concentrated structures so as to emphasize the contribution of their dynamics to the overall behavior of the portfolio. For this reason, we replace the lower capitalization stocks BN and BPN with Cipla limited (Cipla) from the healthcare sector and Britannia industries limited from the consumer defensive sector of the national stock exchange index of India NIFTY. The absence of lead-lag relationship between NIFTY and European stock indexes is pointed out by Choudhary and Singhal .With a focus on considering heterogeneous investment time horizons, we select four distinct samples for each portfolio—i.e., 500, 1000, 2000, and 4000 data points—starting from the most recent observation of the dataset . The The application of the causality measures to the portfolios A and B helps to further illustrate the above heterogeneity. The resulting path diagrams per measure and sample are presented in Although the stock systems are composed of only five variables, the intensity of spreading for the small and large samples among the variables of portfolio A slightly differs between PMIME and PTENUE. It turns out that the combination of domestic stocks generates a nonlinearly interconnected portfolio. Of course, introducing more variables would allow richer dynamics to unfold. Therefore, in the small sample (500 obs.) of the mixed portfolio B, mixing two different sets of stocks affects the consistency of couplings and interrupts the complex pattern of portfolio A. The decrease in the standard deviation from 2.6% (portfolio A) to 1.6% (portfolio B) along with the increase in mean return from −0.106% to 0.033% is an appealing effect of the changes in connectivity over the short-term period, including the first months of the coronavirus pandemic. Respectively, the slump in the skewness value from −1.73 (portfolio A) to −0.10 (portfolio B) illustrates the tight link between the nature of stock connectivity and portfolio asymmetry that reflects the trading activity (Horwitz ).Additionally, the fact that the visual representation of both portfolios is time-varying brings out the role of heterogeneity in financial markets in terms of trading horizons and the subsequent complexity of information signals. Evidence about horizon-dependent behavior has been provided by Prat  for the Looking closer at the evolution of dependence in portfolios A and B as far as information accumulates and sample size increases will shed light on the beneficial side of the resulting dynamics. To do so, we calculate the mutual information coefficient among the stock returns. The results for the concentrated portfolio A, reported in The endogenization of information and the subsequent amplification within the system has a significant effect on variables’ connectiveness. The increasing nonlinearity and the presence of endogenous heteroskedasticity in the simulated systems S2 and S3, together with the appearance of new couplings detected as spurious by the specific statistical measures, can have several exploitable implications for the trading practice and portfolio construction. The property of nonlinearly self-exciting process applies in the set of real variables as well, where their connectivity relies upon the underlying dynamics of the data time horizon. Although, in the long run, the concentrated portfolio A and the mixed portfolio B are characterized by the rich nonlinear association of their components, in the short run mixing two different pools of stocks in portfolio B affects the connectivity and risk measurement. This finding emphasizes the importance of investors’ risk profile and time horizon in the asset allocation process.From a broader perspective, the presence of escalating nonlinear dependences among stocks justifies the need to deal with the curse of dimensionality in financial portfolios. In complex financial markets, where the number of variables influencing the asset prices can be huge, selecting a subset able to capture market risk is an appalling challenge. Green and Hollifield  show thaUnder conditions of strong nonlinear association, a diffusion of biased information among assets can modify a portfolio’s characteristics and impact its performance. Remedies for this effect include the dynamical revising of the portfolio structure or updating asset allocation. On the other hand, exploiting informational evolution through investing in concentrated portfolios, possibly combined with style investing, can be an additional alternative. Although risky, a steady preference of individual investors for concentrated portfolios and active trading has been recorded in the literature. This is likely attributed to the existence of behavioral biases. Individual investors overestimate either the quality of their private information or their ability to interpret it (Odean ; Barber Future research on the impact of connectivity among financial assets will include high-dimensional simulated systems, as well as the evaluation of real stock portfolios built under different statistical scenarios.


Diseases of the gastrointestinal tract of horses are caused by many factors and have a complex pathogenesis. Developing effective methods of differential diagnostics is of high fundamental and applied importance. The pathogenesis of diseases of the digestive tract of horses accompanied by the development of inflammation and oxidative stress, can be associated with a lack of the nitrogen monoxide which controls many signaling pathways in the body. The level of the nitric oxide (NO) is involved in the regulation of the immune and nervous systems, the tone of all the blood vessels, and the courses of many pathological processes. The nitric oxide activates guanylate cyclase (sGC) and leads to vascular relaxation. The aim of this investigation was to study the metabolites of nitric oxide in horses suffered from intestinal diseases. The levels of nitric oxide in the blood serum of horses depending on their age and health state was studied. The concentration of nitrites in the blood serum of horses aged 6–25 years was 3.4 ± 4.2 μM, and in the young horses (1–5 years) the level of this indicator was 8.2 ± 5.4 μM. A sharp decrease in nitrite was observed in all the horses with intestinal diseases of 2 ± 0.9 μM, especially with tympanitic caecun of 0.6 ± 0.4 μM and with spasmodic colic of 1.8 ± 0.5 μM. The level of nitrosylhemoglobin HbNO in the blood of the diseased animals was higher than that in clinically healthy horses, regardless of age. The prevalence of diseases of the stomach and intestines in horses ,2, the mOne of the main characteristics of gastrointestinal diseases in horses is the frequent development of endotoxic shock. Almost all diseases of the intestine of a horse cause a change in the composition and the quantitative ratios of its microflora, a decrease in the pH of the intestinal contents. The destruction of Gram-negative bacteria leads to the release of endotoxins. The endotoxins absorbed by the intestinal wall bind to specific CD14 receptors on the endothelial cells of blood vessels, dendrite cells, monocytes and macrophages. Through CD14 receptors, the endotoxins bind to toll-like receptors  and mitogen-activated kinases ,4. In otIn this respect, the role of microflora in the metabolism of nitro compounds  is profound in the development of equine gastrointestinal diseases ,9. NumerNitrogen monoxide (NO) takes part in the work of the immune and nervous systems, its level directly determines the tone of all vessels and the course of many pathological processes. NO is synthesized from L-arginine by the action of NO synthase (NOS). One of the most important functions of NO is vascular relaxation via activation of the heme-containing enzyme soluble guanylate cyclase (sGC). Specific physicochemical properties of this molecule determine the method of its transportation: in the form of nitrosyl complexes with heme iron, nitrosothiols nitrosocysteine (RSNO), and also high- and low-molecular weight complexes of non-heme iron with thiol ligands —so-called “2.03 complexes” ,17. The Measurement of the concentration of nitrates and nitrites using the Griess reagent is widely practiced to quantify the severity of the inflammatory process . This apTherefore, the aim of our work—based on the analysis of literature data on the possible association of nitric oxide levels in the blood serum of horses with symptoms of colic, and gastrointestinal tract pathology in general—was the measurement of the NO-nitrite metabolite level by electron spin resonance spectroscopy using spin traps ,27.The protocols of experiments were determined by the Committee for Animal Care and Use of the Moscow State Academy of Veterinary Medicine and Biotechnology in accordance with Decision No. 80 of the Council of the Eurasian Economic Commission of 10 November 2017 “On Approval of the Rules for Organization of Laboratory Testing during Veterinary Control (Supervision)”. The protocols of experiments .The object of the investigation was horses, of which 38 animals were studied altogether. In the clinical study, 8 were diagnosed with: spasmodic colic, impaction of the left ventral colon, large colon displacement, seasonal colic, tympanitic caecum or colitis. NO forms stable complexes with iron of hemoglobin that can be detected directly in blood samples by the EPR method. To determine the NO in serum, we used the spin trap method based on the use of dithiocarbamate and iron (II).For the study, blood was taken from the jugular vein into heparin tubes and centrifuged for 10 min in a centrifuge—model CH80-2S “Armed” at the speed of 3000 rpm to precipitate erythrocytes. After that, serum samples were stored in liquid nitrogen (−196 °C). To measure the concentration of nitrosylhemoglobin, whole blood (without heparin) was frozen in plastic containers of cylindrical shape and with a length of 35 mm and a diameter of 4 mm. EPR signals were recorded on a spectrometer Bruker ECS-106 (Germany).The method of determining NO used in this work is based on the reaction of nitrosothiol formation—nitrosocysteine (RSNO) formation in an acidic medium (pH = 3.5) from the nitrite anion NO2- and cysteine hydrochloride. Then, nitrosocysteine, in the presence of iron (2+) N-methyl-D, L-glucamine dithiocarbamate (MGD), forms a water-soluble paramagnetic mononitrosyl iron complex MNIC MGD-Fe-NO. The determination of the nitrite anion NO2- was carried out as follows: serum proteins of a weight greater than 30 kDa after defrosting were removed by filtration through a Microcon 30 kD filter  for 20 min at 14,500 rpm in a Mini Spin plus centrifuge, Eppendorf. The developed method makes it possible, first, to control the effect of serum proteins on the reaction of the MNIC MGD formation and secondly, to take into account the contribution of high molecular weight RSNO and DNIC as sources of NO. To 50 μL of cysteine at a concentration of 400 mM, 10–120 μL of serum was added after filtration. The pH of the solution was adjusted to 3.5 by adding 0.01 mM HCl. After 5 min, 50 μL of 40 mM iron (II) sulfate, 200 μL of 400 mM buffer (Tris or Hepes) and 200 μL of MGD with a concentration of 250 mM were added. Then the pH of the solution was raised to 7.6 with a 0.06% NaOH solution. Under these conditions, the MNIC MGD-Fe-NO is formed.To construct a calibration curve, a sodium nitrite solution of a concentration of 480 μM in the various volumes (2–40 μL) was added to 50 μL of cysteine hydrochloride with a concentration of 400 mM, the pH of the solution was adjusted to 3.5 by adding 0.01 mm HCl. After 5 min, 50 μL of 40 mM ferrous sulfate was added, 200 μL of 200 mM (Tris or Hepes), 200 μL of 250 mM MGD, the pH was adjusted to 7.6 with 0.06% NaOH solution. After 10 min, the ESR spectrum of the MNIC MGD-Fe-NO was recorded. The concentration of nitrite in the sample was estimated by the method of double integration and construction of a calibration curve . The dept-test), respectively.To estimate the degree of difference, analysis of the distribution form was used (Kolmogorov–Smirnov test) and comparison of medians and means  solution or the medium. In our studies, the reduction of nitrite and its inclusion in the MNIC MGD-Fe-NO is carried out at pH = 3.5. In case of the formation of RSNO from nitrite and glutathione or serum cysteine, the latter can be in present in low concentrations, then, after addition of a trap MGD-Fe form, the paramagnetic MNIC MGD-Fe-NO with the participation of the nitrosyl group of the newly RSNO is formed. The addition of a trap at pH = 7.4 to the serum of diseased and healthy animals did not cause the formation of the paramagnetic complex MNIC MGD-Fe-NO , which may indicate the absence of significant amounts of RSNO and DNIC in the serum. According to our estimates, this level in total did not to exceed 30 nM.Using the constructed calibration curve, we measured the nitrite content in the serum samples of animals, which is shown in p = 0.0484 and 0.0108; 0.0074 and 0.0391, respectively), whereas the differences between aged and diseased samples were not significant (p = 0.6547 and 0.2500).The results of the statistical analysis of the obtained data are shown in Nitric oxide (NO) should be considered as one of the most important factors for protection of the gastrointestinal mucosa. The blockade of NO formation affects the secretory and regenerative functions of the stomach. The role of NO as a neurotransmitter in non-adrenergic/non-cholinergic neurons (NANC neurons), which, along with the choline- and noradrenergic conductors of the autonomic nervous system, constitute a third of the autonomic nervous system ,29. The To explain the reduced level of NO in the development of oxidative stress, the concept of bioavailability of NO has been introduced. This means that NO production is high, but its action as a messenger is small. At certain stages of the disease there is a deficit of NO and thus, the reduced bioavailability of NO causes a violation of the functions of the gastrointestinal tract in horses. Metronidazole has been used to treat horses and people with gastrointestinal diseases. Why is this so? Mason et al.  demonstrThe data obtained in this study demonstrate that the concentration of nitrite  in the sThese facts are important for further studies of the diagnostic and prognostic value of nitric oxide as a biomarker for the regulation of intestinal peristalsis in normal and pathological conditions. It begs the question as to why serum nitrite levels are lower in aged horses and those with gastrointestinal diseases compared to healthy ones, even though NO is synthesized in greater quantities? The following is proposed as an explanation. Both in physiological and pathological states, the main mechanism for the removal of excess NO is its transformation into nitrate by the oxygenated heme of hemoglobin A.At the same time, part of NO manages to react with oxygen to form nitrite. The transformation of NO can also occur through interaction with the superoxide anion radical formed during inflammation, with the formation of peroxynitrite. At physiological pH 7.4, it is unstable, easily protonated and decomposed leading to the formation of both nitrates 70% and 30% nitrites. Thus, in a state of inflammation and oxidative stress, pathways for metabolizing NO are shifted towards the formation of nitrate B. It is There is reason to believe that in the serum of aged and diseased horses, beside nitrites, there is also a pool of low molecular weight RSNO with a mass below 30 kD and a pool of DNIC (or binuclear DNIC: B-DNIC) with thiyl ligands. Normally, both serum RSNO and DNIC (or B-DNIC binuclear DNIC), are present in a higher concentration as donors and transporters of NO. According to Tsikas et al. , in normThe results of the study show that in the state of inflammation and oxidative stress, the pathways of NO metabolism shift towards the formation of nitrates. It is also possible that NO is partially consumed for the formation of DNIC, the uncontrolled nitrosylation of protein thiols, and the formation of nitrothyrosine in proteins. Studies on the assessment of NO and its metabolites in the blood should continue to distinguish a more complete picture of the processes occurring in the complex symptoms of colic in horses. The spin trap technique and EPR spectroscopy, applied by us, allows one to take into account NO in both RSNO and DNIC. For a more correct assessment of the inflammatory process, it is necessary to study the NO-producing activity in the tissues and fluids of the zone of inflammation.


Pipa is the only South American pipid, while all the other genera are found in Africa. The divergence of the South American lineages from the African ones occurred at least 136 million years ago and is thought to have had a strong biogeographic component. Here, we tested the potential of the repetitive DNA to enable a better understanding of the differentiation of the karyotype among the family Pipidae and to expand our capacity to interpret the chromosomal evolution in this frog family. Our results indicate a long history of conservation in the chromosome bearing the H3 histone locus, corroborating inferences on the chromosomal homologies between the species in pairs 6, 8, and 9. The chromosomal distribution of the microsatellite motifs also provides useful markers for comparative genomics at the chromosome level between Pipa carvalhoi and Xenopus tropicalis, contributing new insights into the evolution of the karyotypes of these species. We detected similar patterns in the distribution and abundance of the microsatellite arrangements, which reflect the shared organization in the terminal/subterminal region of the chromosomes between these two species. By contrast, the microsatellite probes detected a differential arrangement of the repetitive DNA among the chromosomes of the two species, allowing longitudinal differentiation of pairs that are identical in size and morphology, such as pairs 1, 2, 4, and 5. We also found evidence of the distinctive composition of the repetitive motifs of the centromeric region between the species analyzed in the present study, with a clear enrichment of the (CA) and (GA) microsatellite motifs in P. carvalhoi. Finally, microsatellite enrichment in the pericentromeric region of chromosome pairs 6, 8, and 9 in the P. carvalhoi karyotype, together with interstitial telomeric sequences (ITS), validate the hypothesis that pericentromeric inversions occurred during the chromosomal evolution of P. carvalhoi and reinforce the role of the repetitive DNA in the remodeling of the karyotype architecture of the Pipidae.The large amphibian genomes contain numerous repetitive DNA components that have played an important role in the karyotypic diversification of this vertebrate group. Hypotheses based on the presumable primitive karyotype (2n = 20) of the anurans of the family Pipidae suggest that they have evolved principally through intrachromosomal rearrangements.  Amphibians are a diverse and abundant class of vertebrates that provide an important model for studies in evolutionary genetics . AmphibiIn addition to its structural role in the eukaryote chromosome, the repetitive fraction of the genome also plays a central role in the stability of the chromosome, the cell cycle, and the regulation of gene expression and is an important substrate for genome evolution . Macrostin tandem, such as microsatellite, mini-satellite, and satellite DNA  and DNA sequences arranged lite DNA . Satellinversion . Microsanversion . These snversion  or the enversion .By contrast, the multigene families that encode fundamental molecules  are subject to strong selective pressures and often maintain a conserved nucleotide sequence and chromosome position over the evolutionary history of a lineage . This maThere is considerable evidence that the chromosomal organization of different repetitive DNA classes is conserved during the karyotype evolution of closely related species  and thatHymenochirus, Pseudhymenochirus, Pipa, and Xenopus  + (Hymenochirus + Pseudhymenochirus)] . Thus, the biogeographic scenario that accounts for the diversification of these lineages remains unsolved.The family Pipidae includes 41 species distributed in four genera,  Xenopus . Pipa isn Africa . A recenchirus)] . Despitechirus)] . This han = 20 karyotype is a putative plesiomorphic condition in the family Pipidae , the use of the repetitive DNA as a probe in FISH assays should enable the identification of karyological differences imperceptible by classical chromosome markers. The presumable primitive karyotype of the Pipidae, for example , indicates the occurrence of five pericentromeric inversions  in the P. carvalhoi karyotype  in the homologs of pairs 6, 8, and 9.If this hypothesis is true, the diversification of the pipid karyotype would be due primarily to intrachromosomal rearrangements see . As the aryotype  and threP. merli . RecentlP. carvalhoi and X. tropicalis share the same diploid number (20 chromosomes) but diverged at least 136 million years ago  in the karyotypes of P. carvalhoi analyzed in the present study were collected in Buíque, Pernambuco, Brazil , and in all experiments at least ten metaphases were analyzed in each slide. Specimen collection was authorized by SISBIO/Chico Mendes Institute for Biodiversity Conservation (protocol 55481-1), and the specimens were deposited in the Natural History Museum of the Federal University of Alagoas (MHN-UFAL) in Maceió, Brazil. The P. carvalhoi specimens were injected intraperitoneally with 2% colchicine  for 4 h to obtain the chromosome preparations, with the suspensions of the intestine and testicles being obtained following the protocol of X. tropicalis were obtained through the culture of the fibroblast cells in the laboratory of Dr. Malcolm A. Ferguson-Smith of the Department of Veterinary Medicine at the University of Cambridge  and H3-R (5′ ATATCCTTRGGCATRATRGTGAC 3′) following Escherichia coli TOP10 cells. The recombinant clones were grown for plasmid DNA mini-prep extraction as described by P. carvalhoi histone H3 (GenBank access number MT508594) was 99% similar to that of X. tropicalis available in GenBank (DQ28350).A 400 base pair (bp) fragment of the histone H3 gene was synthesized from P. carvalhoi as a query in genomic searches for LocalBlast in X. tropicalis genome (GenBank assembly accession – GCA000004195.4). The results were manually filtered using as criteria an e-value threshold of >10–4, 80% of the identify, and alignment of at least 70% of the query.We used the sequence of the histone H3 clone from P. carvalhoi karyotype was performed by FISH experiments. The plasmid mini-preparations were used as a template for probe production using PCR labeling with 11-digoxigenin-dUTP and detected with anti-DIG-Rhodamine. The hybridization experiments were conducted following the protocol of The chromosome mapping of the H3 probe in 15, (GA)15, (GATA)8, (CGC)10, (GAA)10, (CAG)10, and (GACA)4 were mapped to chromosome spreads of P. carvalhoi and X. tropicais using oligonucleotide probes marked directly with Cy5-fluorochrome at the 5′ end during synthesis (Sigma-Aldrich). Fluorescent In Situ Hybridization (FISH) assays were run following the protocol of The microsatellites (CA)The FISH experiments were run in duplicate, with the metaphases of both species being included in each run. Images of the hybridized metaphase chromosomes were captured with an Olympus BX51 Fluorescence Microscope, and the acquisitions of the images were made considering the same exposure time for the two species.P. carvalhoi histone H3 probe detected hybridization signals primarily in the pericentromeric region of the short arms of pair 6 and in the long arms of the homologs of pairs 1, 5, and 8, in addition to the subterminal region of the long arms of pair 9  and coverage with the histone H3 of P. carvalhoi, with copies being mapped in pairs 3, 6, 8, and 9 but only a few hits in pairs 2 and 5  and S (short chromosome). Our genomic searches in X. laevis recovered only a few hits in chromosomes 5S and 5L and one hit in 6S 15 probe detected strong hybridization signals in the centromeric region of homolog pairs 1, 2, 3, 5, 6, 7, and 8, as well as in the subterminal region of pairs 2 and 9 15 probe detected signals in the subterminal regions in all the chromosomes of the complement 10, and (GATA)8 in the subterminal/terminal regions of all the chromosomes of P. carvalhoi and X. tropicalis opicalis . The 10 probes only detected hybridization signals in the P. carvalhoi chromosomes. The (GAA)10 probe detected signals in the subterminal regions of chromosome pairs 4 and 9, in the pericentromeric region of the homologs of pair 5, and interstitially in the long arms of pair 7 f pair 7 . The  during its genomic reorganization and the allopolyploidy process, which resulted in the conservation only of the copies in chromosome pairs 5 (in both subgenomes – 5S and 5L) and 6S.Another interesting finding of the present study was the presence of histone H3 copies in the homologs of pairs 8 and 9 in the  Pipidae , X. laev extinct . The com pairing . The 2n evis see . Thus, clarities  but may X. tropicalis and P. carvalhoi, the physical mapping of this sequence in other pipid species should provide more evidence to support this evolutionary hypothesis.The conservation of chromosomes that bear histone clusters has already been reported in other groups  and reflP. carvalhoi and X. tropicalis. This offers new insights into the evolution of the karyotypes of these species. Our findings reveal shared patterns in the distribution and abundance of the repetitive DNA shared by these two species, such as the arrangement of the repetitive tri- and tetra-nucleotide [(GATA)8] motifs mapped in the terminal/subterminal regions of the chromosomes, which reflects the conservation of the organization of these chromosomal regions. The enrichment of the microsatellite motifs in the terminal/subterminal region is a common phenomenon in the karyotypes of birds 15 motif in the pericentromeric region in the short arm in X. tropicalis. Chromosome pair 4 presents an exclusive accumulation signal of repeat motifs (GATA)8 and (GA)15 in the X. tropicalis karyotype, whereas the homologs of pair 5 exhibit exclusive hybridization signals of the (GAA)10 and (GATA)8 repeat motifs in P. carvalhoi. These findings highlight the unique patterns in the chromosomal organization of the repetitive DNAs in these karyotypes and their contribution to the chromosomal diversification of the two study species.By contrast, the microsatellite probes detected distinct arrangements of the repetitive DNA in the chromosomes of the two species, enabling the longitudinal differentiation of pairs that are identical in their size and morphology, such as pairs 1, 2, 4, and 5. For example, the metacentric pair 1 presents pericentromeric accumulation of the (GATA)P. carvalhoi and X. tropicalis, with clear evidence for the species-specific accumulation and distribution of some of these markers. The microsatellite repeats appear to have an intragenomic “life cycle” that includes (i) their birth in the respective genome, (ii) the subsequent increase in the number of repeats (by polymerase slippage), representing “adulthood,” and (iii) death, when the locus degrades the number of repeats by substitutions or inserts/deletions, causing the interruption of the repetitive units 15 motifs, which revealed distinct centromeric arrangements in the two study species. For example, these two repeat motifs marked all the centromeric/pericentromeric portions of the karyotype of P. carvalhoi, whereas in X. tropicalis, only pairs 1 and 3 presented clusters of (CA)15. The composition of the repetitive DNA that compiles the centromeric region of the chromosomes may vary considerably between closely related species  species . Melters species . When pr species .P. carvalhoi karyotype, which may have played a role in the rearrangements that occurred in these pairs during the diversification of the karyotype of this species. Pericentromeric inversions are the most parsimonious hypothesis to account for the chromosomal changes between the inferred primitive pipid karyotype  and P. carvalhoi 4, (CAG)10, and (GA)15 motifs] reinforce these hypotheses.We also found evidence of microsatellite enrichment in the pericentromeric region of chromosome pairs 6, 8, and 9 in the arvalhoi . The preX. tropicalis genome is composed of transposable elements . Future studies that evaluate the interaction between microsatellites and transposable elements will be important for the more systematic understanding of the origins of the chromosome variation in these species.While recombination and amplification events are a major source of chromosomal variation in the density and composition of microsatellites in the genome, we cannot overlook the role of the transposable elements in the accumulation and spread of the microsatellite motifs in the genomes of these two species during the diversification of their chromosomes . In factelements , and a rP. carvalhoi and X. tropicalis. This contributes to a better understanding of the chromosome changes that have occurred during the karyotypic diversification of these species. We have shown the evolutionary conservation of the chromosomes that bear the histone H3 copies and the distribution of the microsatellite motifs for at least 136 million years, the estimated time to the TMRCA of the two study species. Our data also provide clear evidence of the distinct profiles of the distribution and density of microsatellite motifs between these species, which reveals the fundamental role of these repetitive DNAs in the shaping of chromosome structure in these species. Our findings reinforce the role of the repetitive DNA in the remodeling of the karyotype architecture and indicate that the understanding of the evolutionary dynamics of these sequences in the chromosomes can increase our capacity to discriminate chromosome pairs that are identical in classical cytogenetic analyses, and consequently, our capacity to understand the mechanisms operating during chromosomal evolution of the Pipidae, in particular, P. carvalhoi and X. tropicalis.Our results have added important chromosome markers to the evolutionary comparisons of the pipid karyotypes and have corroborated inferences on the interspecific chromosomal homeologies between All datasets presented in this study are included in the article/The animal study was reviewed and approved by SISBIO/Chico Mendes Institute for Biodiversity Conservation (protocol 55481-1). Written informed consent for participation was not obtained from the owners because SISBIO/Chico Mendes Institute for Biodiversity Conservation (protocol 55481-1).MZ conducted the experiments, analyzed the data, and wrote the manuscript. CG and AS assisted in the execution and analysis of FISH experiments. TG, NP, and SR-P helped to draft the manuscript. DB designed and coordinated the study and wrote the manuscript. All authors read and approved the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The fear of hypoglycemia leads to psychological symptoms in patients with diabetes type 2. In this research, the effects of EDMR on the fear of hypoglycemia in patients with diabetes type 2 were examined.A clinical trial study was carried out with participation of 72 patients who had diabetes type 2 in Velayat Hospital. The participants were randomly assigned into control and intervention groups. The intervention group received EMDR. The required information was gleaned using a questionnaire of fear of hypoglycemia, intensity of hypoglycemia, and demographics filled out before the intervention, and 1 month and 3 months after it. The data were analyzed using descriptive statistics on SPSS Version 23. For comparison of fear of hypoglycemia in intervention and control groups, repeated measure ANOVA and Cohen d test were used.The mean age of the participants in the intervention group was 43.17 ± 10.55 and in the control group was 45.86 ± 13.6. In this study, without considering the potential disruptors in the incorrect model, the intervention caused a reduction of 15 points 1 month after the completion of the intervention and a reduction of 17 points 3 month after the completion of the intervention on the scale of fear of hypoglycemia; but post-correction of potential disruptors, intervention caused a reduction of 19.5 scores 1 month after the completion and a reduction of 20.3 scores 3 months after the intervention .The EMDR can be used as a non-pharmaceutical treatment method to treat and alleviate the fear of hypoglycemia in type 2 diabetes patients.IRCT20181201041813N1, 2019/11/13.Iranian Registry of Clinical Trials:  Diabet Mellitus is a chronic and complicated disease that requires permanent medical cares and strategies to attenuate the risk of recurrence of many side-effects . The priFear of hypoglycemia promotes dysfunctional behaviors in order to prevent hypoglycemia such as maintaining high blood glucose levels by limiting physical activity, reducing the required insulin dose, and increasing carbohydrate intake. These maladaptive behaviors may lead to permanent hyperglycemia and increase the risk of vascular complications . StudiesEye Movement Desensitization and Reprocessing (EMDR) is a short-term psychotherapy for anxiety, especially in the case of traumatic events . It was Given the fact that the fear of hypoglycemia is one of the main side effects of diabetes that leads to problems in controlling blood sugar level, taking into account that health interventions have been limited to patients with diabetes type 1 so far, and taking into account the fact that EMDR, as a non-invasive and cognitive-behavioral treatment, has been very successful, the present study, as the first attempt in the world, examines the effect of EMDR on the fear of hypoglycemia in patients with diabetes type 2. The main hypothesis of this study was to reduce the fear of hypoglycemia in type 2 diabetic patients following treatment with EMDR.The study was carried out as a single blind parallel clinical trial. The study population consisted of patients with diabetes type 2 visiting the Clinic of Velayat Educational Hospital, Ghazvin-Iran from 2018 to 12 to 2020–01.The number of people participating in this study was determined using the first-type error with 0.05 and the second-type error 0.8; and using the amount of fear of hypoglycemia in the study of Walker et al. , 36 patiFor evaluation of fear of hypoglycemia in patients, questions such as »Are you afraid of recurrence of low blood sugar?“ “Have you reduced your activities following the fear of low blood sugar?” “Does the fear of blood sugar drop result in a reduction in insulin dose or consumption of oral anti-diabetes tablets?” “Have you consumed a lot of carbohydrates for fear of low blood sugar?” “Do you fear the blood sugar drop in your sleep?” were used. The exclusion criteria were failure to continue EMDR (impaired concentration during treatment and the occurrence of conditions such as dizziness) and reluctance to continue the study.In this study, the sampling was made available. The researchers examined 520 diabetic type 2 patients and finally selected 72 patients who were eligible for the study Fig. . PatientThe required data were collected using a demographic questionnaire, Hypoglycemia fear survey, and Hypoglycemia severity inventory. The demographic questionnaire covered age, gender, job, education, marital status, history of diabetes, blood sugar test, awareness of insulin function, and number of hypoglycemia over the past 6 months. Fear of hypoglycemia was measured using the worry scale of fear of hypoglycemia questionnaire (HYPOGLYCEMIA FEAR SURVEY –II). This questionnaire, designed by Cox et al., included two sub-scales “worry” and “behavior.” The” worry” sub-scale is covered by 18 questions that measure fear of hypoglycemia over the past 6 months with score range from 0 to 72. The questions in this sub-scale are designed based on a five-point scale (0-- 4) and validity and reliability of the questionnaire have been supported by many studies. Momeni et al.  studied The intervention group received the EMDR intervention through two 45 min individual sessions in the internal ward examination room of the hospital. In each intervention session, the intervention was performed by the researcher in several stages. Each step consisted of 24 two-way cycles using finger movements , each cySampling was started after securing a medical ethics license under No.: IR.QUMS.REC.1397.230  and an informed letter of consent from the participants. In addition, all the items in the Declaration of Helsinki were observed . All patP = 0.05). To compare the fear of hypoglycemia between the two groups in before intervention, after intervention and follow up, repeated measure ANOVA was used. The effect of the intervention was also measured by Cohen’s d test.The collected data were analyzed using descriptive statistics  on SPSS (version.23)  were male; and in the control group, most participants (58.3%) were female. The mean Diabetes Duration in the intervention group was 10.5 and in the control group was 10.4 (Table The results of the study showed that without considering the potential disruptors in the incorrect model, the intervention caused a reduction of 15 points 1 month after completion of intervention and a reduction of 17 points 3 months after the completion of the intervention on the scale of fear of hypoglycemia Fig. . Due to This study was conducted for the first time with the aim of investigating the effect of EMDR treatment method on fear of hypoglycemia in type 2 diabetic patients, as a randomized clinical trial. The results of the study showed that treatment with EMDR reduces the fear of hypoglycemia in type 2 diabetic patients, and the therapeutic benefits of this method are achieved in a short time and with a significant effect size. There are no studies on the treatment of fear of hypoglycemia with EMDR method, but cognitive-behavioral interventions have been used to treat the fear of hypoglycemia. It should be noted that these interventions have been performed only in type 1 diabetic patients, and no intervention has been performed in type 2 patients, Boyle et al., in a case study, performed a cognitive-behavioral intervention in a 37-year-old woman with type 1 diabetes, who had panic attacks and fear of hypoglycemia. The implementation of this intervention led to the improvement of the idea that hypoglycemia leads to loss of control and leads to increased self-confidence in recognizing and managing blood sugar . AsmebarThe study also found that the effect of treatment with EMDR in management of fear of hypoglycemia in type 2 diabetic patients is permanent because after a three-month follow-up period, it was found that the fear of hypoglycemia scores in type 2 diabetic patients was reduced. In previous study of EMDR on various diseases, it was discovered that the effect of this intervention is stable over time. In a systematic review study by Tezars et al., the effect of EMDR on chronic pain was determined. The results of the study showed permanent improvement in chronic pain without any side effects , consistThe fear of hypoglycemia is one of the main obstacles of glycemic control in patients with diabetes type 2. The results showed that the EMDR was a successful way to treat the fear of hypoglycemia in patients with diabetes type 2. It can be considered an economic, non-invasive, and fast-rewarding method to treat the fear of hypoglycemia in patients with diabetes type 2. Further studies with longer follow-up terms are recommended to ensure the success of the treatment. Among the limitations of the study, short-term follow-up period and the self-report nature of the fear of hypoglycemia survey are notable.


In particular, understanding the effect of nano-alumina with different morphologies on the dielectric performance of composites is of great significance. In this study, Al2O3-NWs with lengths of approximately 100 nm and diameters of approximately 5 nm were prepared and blended with anepoxy resin to form composites, and the effect of the mass fraction of fillers on the thermal conductivity of the composites was investigated. Specifically, the effect of alumina fillers with ananowire structure on the insulating and dielectric performance and breakdown strength of the epoxy composites were analyzed. The influence principle of the interfacial effect and heat accumulation on the dielectric and insulating properties of the composites were described. The results demonstrated that the thermal conductivity of Al2O3-NWs/epoxy resin composites was higher than that of the bare epoxy resin. The thermal conductivity of Al2O3-NWs/epoxy resin composites increased with increasing mass fraction of fillers. When the mass fraction of fillers was 10%, the thermal conductivity of the composite was 134% higher than that of the epoxy resin matrix. The volume resistivity of the composites first increased and then decreased as the mass fraction of fillers increased, while the dielectric constant of the composites increased with increasing mass fraction of fillers and decreasing frequency. The dielectric loss of the composites decreased and then increased as the mass fraction of fillers increased, and it increased with increasing frequency. Additionally, the alternating current breakdown strength of the composites first increased and then decreased withincreasingmass fraction of fillers.Alumina nanowires (Al The breakdown strength of epoxy resins is as high as 28 KV/mm, the dielectric constant at power frequency is approximately 3.5, and the mechanical elasticity can reach 53 GPa ,2. The iy resins ,12. The y resins . Howevery resins . At pres2O3-NWs/epoxy composites went up with increasing mass fraction of alumina. Additionally, two- and three-phase Lewis–Nielsen models were proposed to specifically solve the effect of interfacial characteristics between the polymer matrix and the filler nanoparticles on the thermal conductivity of the composites [2O3-NWs) wereinvestigated. Firstly, Al2O3-NWs were prepared by heating in a water bath, and Al2O3-NWs/epoxy resin composites with different doping ratios were prepared by blending the above-mentioned Al2O3-NWs with epoxy resins. The relationship between the thermal conductivity of the composites and the mass fraction of the fillers was analyzed. Overall, the effect of Al2O3-NWs on the electrical performance of composites, namely volume resistivity, dielectric performance, and breakdown performance, with different mass ratios was extensively investigated.Kochetovet al. studied the thermal conductivity of epoxy composites with inorganic nanoparticles such as alumina. The results showed that the thermal conductivity of Almposites . Moreiramposites . Anithammposites . Eker etmposites . Tomaskomposites . Paul etmposites . Gong etmposites . Moreovemposites . Nascimemposites . In summBisphenol epoxy resin (Eponex 1513) E-51, anhydride curing agent, and methyltetrahydrophthalic anhydride were purchased from Changzhou Chongkai Chemical Co., LTD., China. 2,4,6-tri(dimethylamino) phenol (DMP-30) was purchased from Shanghai Aladdin Bio-Chem Technology Co., LTD., China. Aluminium sulfate octadecahydrate was purchased from Tianjin Damao Chemical Reagent Factory, China. Ammonium hydroxide was purchased from Chengdu Kelong Chemicals Co., Ltd., China.2O3-NWs and epoxy composites. First, aluminum sulfate octadate was dissolved in deionized water, and the pH value of the solution was adjusted to 5 by adding ammonia to generate the precipitation of aluminum hydroxide. The mixed solution was placed in a high-pressure reaction kettle and heated in a water bath at 180 °C for 10 h. The formed solution was centrifuged to separate the aluminum hydroxide, which was cleaned with deionized water and alcohol, and then dried to obtain aluminum hydroxide powder. Finally, the powder was calcined at 600 °C for 2 h to obtain alumina nanowire powder.A hydrothermal synthesis method was used for the preparation of Al2O3-NWs were prepared by a blending technique. Firstly, the epoxy resin was heated at 60 °C in an oil bath until the viscous mixture became liquid. Then, different mass fractions of Al2O3-NWs  were added to the epoxy resin matrix, and the mixture was stirred until evenly mixed. Methyl tetrahydrophthalic anhydride was added as the curing agent, which was dispersed and emulsified for 30 min. Lastly, 2,4,6-tri(dimethylamino) was added as an accelerant and stirred electrically for 30 min. The above mixture was placed in a vacuum box for degassing, then poured into a mold and heated for curing as follows: 80 °C for 1 h, 120 °C for 2 h, and 150 °C for 2 h [Epoxy resin composites with different mass fractions of Al for 2 h .2O3-NWs, as well as the dispersion of theAl2O3-NWs in the epoxy resin matrix. The samples were cooled with liquid nitrogen to make them brittle. The nanowires and the cross-section of the samples were sprayed with conductive gold, and the microscopic morphology was observed using an extremely narrow electron beam emitted from an SEM gun.SEM was used to observe the microscopic morphology of Al2O3-NWs. As observed, the alumina nano-fillers prepared via the hydrothermal method were linear, with a length of 100 nm and a diameter of 5 nm. The nanowires were well distributed with a clear interface between particles, the size was homogeneous, and no agglomeration was observed.2O3-NWs/epoxy resin composites with different mass ratios. It can be observed that at low mass ratios, dot-shaped alumina fillers were coated and uniformly distributed throughout the epoxy resin matrix. The cross-section was shell-shaped with obvious characteristics of ductile fractures and good matrix/filler interfacial properties. As the mass fraction of fillers increased, the distance between alumina filler particles decreased and the matrix surface became rough with a large number of bumps, revealing that agglomeration occurred in the epoxy resin matrix and the alumina fillers overlapped each other to form a filler network.A multifunctional thermal conductivity meter  was used to measure the thermal conductivity of the samples in a direction perpendicular to the composite plane by the Hot Disk Method. The transient plane heat source method was used for testing. The sample was heated by the Hot Disk probe, and the temperature change was calculated by the change of resistance inside the probe, so as to complete the measurement and transmission of temperature data.All the measured samples had a smooth and flat surface with a thickness of approximately 1 mm. The samples were stacked tightly and neatly and clamped to prevent the influence of air on the measurement of thermal conductivity. Graphite thin layers were sprayed on the surface of the measurement.We read and recorded the thermal conductivity obtained by the measuring instrument .Thermogravimetric Analysis-Differential Scanning Calorimetry(TG-DSC) was used to study the thermal stability of the composite materials. The weight of the sample was measured in a controlled heating environment as a function of temperature. In the experiment, 5 mg of the sample was added into a crucible, which was then placed in the apparatus. And nitrogen gas was introduced at a flow rate of 50 mL/min. The temperature was increased from 30 °C to 900 °C with a heating rate of 7 °C/min.ρν is the volume resistivity of the sample, Rν is the volumeresistance of the sample, Ae is the effective area of the protected electrode, and t is the thickness of the sample.The volume resistivity of the composite was measured using a high insulation resistance tester . The shield electrode, the protected electrode, and the measuring electrode were connected via a three-electrode connection method. A voltage of 1000 V was applied to both ends of the composite sample and the volume resistivity was recorded. In order to avoid interference of the polarized current and environmental factors on the experimental data, the volume resistivity was calculated by taking the average value of the resistivity with the voltage applied for 20 min. Finally, the volume resistivity of the composite sample was calculated using Equation (1):C is the capacitance of the sample, h is the thickness, d1 is the diameter of the protected electrode, g is the electrode gap, and ε0 is the dielectric constant of vacuum. The frequency range of permittivity was 10–100,000 Hz, the test voltage was 0.5 V, and the number of scanning points was 800. The experiment was carried out at room temperature.Data such as the dielectric loss of the composite and its capacitance were measured by a high-frequency LCRdigital bridge . Before the measurement, the shield electrode, the protected electrode, and the measuring electrode were connected by a three-electrode connection method, where the shield and the protected electrodes were connected with the composite samples. Finally, data such as the gap and diameter of the measured electrode were recorded to calculate the dielectric constant. The dielectric constant was obtained from Equation (2):The breakdown strength of the composite was measured by breakdown voltage testing equipment controlled by a computer . The composite sample was placed between spherical electrodes with a diameter of 25 mm. Transformer oil was used as the insulating dielectric. In the experiment, an AC voltage of 50 Hz was applied between the electrodes, and the voltage between the electrodesevenly increased at a rate of 1 V/s. Finally, the breakdown strength of the composite sample was calculated as the ratio of the breakdown voltage to the sample thickness.2O3-NWs/epoxy resin composites with different mass ratios are shown in 2O3-NWs. Additionally, the thermal conductivity of the composites was better than that of the pure epoxy resin. When the mass fraction of Al2O3-NWs reached 10%, the maximum thermal conductivity was obtained, increasing by 34% compared with the pure epoxy resin, due to the better thermal conductivity of alumina compared with epoxy resin.The results of the thermal conductivity tests of the pure epoxy resin and the Al2O3-NWs fillers in the composite samples resulted in a low Al2O3-NWs density, leading to a low probability of contact between the particles. Therefore, the composite formed a dispersed heat-conducting chain structure inside. Compared with the pure epoxy resin, the thermal conductivity of the composite increased, but to a small extent. As the mass fraction of Al2O3-NWs increased, the density of the Al2O3-NWs also increased, leading to a high probability of contact between the wires. The increased density resulted in the nano-wires forming a series of thermally-conductive chains, thus creating a complex parallel thermal conductive network. At this time, the thermal conductivity of the composite material increased significantly, and it was significantly improved compared with that of the packing material with a lower mass fraction. When the mass fraction of the filler reached 10%, the thermal conductivity of the composite was 134% of that of the pure epoxy resin [Low mass fractions of Alxy resin ,28,29.2O3-NWS/epoxy resin composite with different mass fractions.It can be seen from 2O3-NWS does not change the decomposition mechanism of the epoxy resin at high temperatures. The DSC curves of the samples have two obvious heat absorption peaks, but the TG curve only shows a significant change near the first heat absorption peak, indicating that at the temperature corresponding to the first heat absorption peak, the samples underwent thermal decomposition and the mass loss of the sample was accelerated and stabilized as the temperature increased. As can be seen from 2O3-NWS, the corresponding temperature (represented by T10 and T50 respectively) when the mass loss of the sample reached 10% and 50% also increases, reaching the temperature corresponding to the maximum thermal decomposition rate of the composite material. In addition, with the increase of the mass fraction of Al2O3-NWS, the temperature corresponding to the first heat absorption peak of the sample (represented by Tm) increased, aswell as the thermal decomposition temperature. In summary, Al2O3-NWS was uniformly dispersed in epoxy resin and formed a thermal conductivity chain or network with the increase of mass fractions, which improved the thermal conductivity and enhanced the thermal stability of the composite material [material .2O3-NWs/epoxy resin composites with different mass ratios were measured, and the results are shown in The volume resistivity of the epoxy resin and the Al2O3-NWs, reaching a maximum value at approximately 3%. This may be due to the fact that when a small amount of Al2O3-NWs was added to the epoxy resin matrix, the wires bonded with the epoxy resin segments and the filler and matrix formed an interaction zone. The free volume of the molecules inside the composite decreased, which decreased the mobility of the carriers, and the volume resistivity of the composite increased. Additionally, when the mass fraction of the Al2O3-NWs was low, the addition of fillers introduced deep traps in the composite. These deep traps captured charges inside the composite and decreased the average displacement of the charged carriers, thereby inhibiting its movement. When the mass fraction of Al2O3-NWs increased, the space between nanowires decreased. Due to the high specific surface area of the incorporated Al2O3-NWs, agglomeration of Al2O3-NWs appeared in the composite, which reduced the effective interaction range and weakened the restraining effect on the carriers. At the same time, interaction regions overlapped with each other, providing a low-resistance path for charge carriers, which was beneficial for the transmission of carriers within the composite. Therefore, the volume resistivity of the composite increased [It can be observed from the figure that the volume resistivity of the composites first increased and then decreased with increasing mass fraction of Alncreased .2O3-NWs/epoxy resin composites with different mass ratios were measured as a function of frequency, and the results are shown in The dielectric constant and dielectric loss of the epoxy resin and the Al2O3-NWs, which was greater than the dielectric constant of the pure epoxy resin. This was likely because the dielectric constant of the Al2O3-NWs filler was approximately 10 [pf is the mass fraction, pk is the dielectric constant, and ρp is the density of the epoxy resin; ff is the mass fraction, fk is the dielectric constant, and ρf is the density of the Al2O3-NWs fillers; and η is a constant related to the shape and orientation of the fillers in the composite.The results reveal that the dielectric constant of the composites increased with increasing mass fraction of Alately 10 , which wately 10 :(3)kc=kp2O3-NWs fillers, which was similar to the measureddielectric constant of the composite. The density of the polar groups in the composite increased with increasing mass fraction of nano-alumina fillers. This resulted in a higher number of introduced interfaces between the filler and the matrix, and the effect of interface polarization generated by the external electric field was enhanced. Therefore, the dielectric constant of the composite increased. With the enhancement of interface polarization, the charge distribution in the interface became denser due to the action of the interaction zone and the external electric field, which further increased the dielectric constant of the composite.According to Equation (3), the dielectric constant of the composites is related to the matrix to filler doping ratio. The dielectric constant of the composite was calculated according to the formula, and it was observed that it increased with increasing mass fraction of AlAs observed, the dielectric constant of the composite decreased with increasing frequency, especially in the high-frequency region. This may be due to the heterogeneity of the composite. It was proposed that under an external electric field, free carriers in the composite were trapped by dielectric or interfacial traps during macroscopic movement, leading to a space charge accumulation in the interface region. Therefore, an electric dipole moment was formed in the region with uneven charge distribution. At low frequencies, dipole polarization was completed within the frequency variation cycle, with a stronger composite polarization performance and a larger dielectric constant. As frequency increased, the cycle shortened, and it was difficult to establish dipole orientation polarization. Therefore, the polarization performance of the composite decreased, as well as the dielectric constant.2O3-NWs fillers increased. This may be due to the fact that at low Al2O3-NWs mass fractions, the matrix and nano-fillers were bonded tightly to form an interaction zone. Due to the special morphology of the nanowires, this interaction zone was interspersed in the epoxy resin matrix to form a network which restricted the movement of the polar groups and the epoxy resin segments within the composite, inhibiting the orientation polarization in the external electric field and reducing the dielectric loss of the composite. Moreover, when the mass fraction of Al2O3-NWs fillers increased, the filler density increased. Since the specific surface area of the nanowires was significantly larger than that of the nanoparticles, agglomeration in the composite increased, and the restriction effect on the polymer chain segment decreased. Additionally, the defects introduced by the agglomeration increased the conductive loss of the composite, so the dielectric loss of the composite increased accordingly [As it can be observed from ordingly .fP is the accumulated breakdown probability of the sample; E is the breakdown strength; 0E is the scaleparameter, indicating the breakdown strength when the probability of failure is 0.632; and β is the shapeparameter (positive), which reflects the breakdown voltage distribution range, meaning that the larger the β, the smaller the dispersion of breakdown voltage. In breakdown tests, according to the IEC/TC56 international standard, when the sample size is less than 25, fP can be expressed as:i is the ith measured breakdown strength in ascending order, and n is the number of the measured breakdown strength [E0 in the Weibull distribution. Substituting E0 into Equation (4) with the measured breakdown strength, E, the β in the Weibull distribution was obtained.In this study, the Weibulldistribution was applied to analyze the data, which was embodied in the breakdown probability at a specific fieldstrength of the composite sample. For AC breakdown strength, the two-parameter Weibull distribution can be expressed as:strength . From Eq2O3-NWs was low, Al2O3-NWs bondedtightly with the epoxy resin segments after being evenly dispersed in the resin matrix to form an interaction zone due to their large specific surface area. Therefore, the filler/matrix interface effect was enhanced, and deep traps were introduced to inhibit the movement of carriers in the composite. Additionally, the movement of polymer chains and space charges was restricted, which increased the breakdown strength of the composite. On the other hand, the composite generated significant heat accumulation under the external electric field during breakdown, which destroyed the insulation structure of the composite, resulting in breakdown of the composite. When the breakdown of the composite is under an AC electric field, the power loss per unit volume, P, can be expressed as:εr refers to the dielectric constant of the material, tan δ0 is the tangent dielectric loss angle at 0 °C, α refers to the temperature coefficient of the tangent dielectric loss angle, mt is the maximum temperature of the dielectric, and t0 is the environmental temperature.It was hypothesized that when the mass fraction of AlQ, which can be calculated as follows:Additionally, most of the energy loss in dielectrics is converted to heat. For example, the dielectric loss will be converted into 2O3-NWs, the dielectric loss of the composite at high frequencies decreased and the heat generated inside the composite was reduced. The thermal conductivity of the composite was largeand the heat accumulation in the dielectric decreased, which improved the breakdown strength of the composite.According to Equation (7), as the composite dielectric constant and dielectric loss of the compositeincreased, the heat generated inside the composite would also increase. However, at lowmass fractions of Al2O3-NWs were prone to agglomeration due to their large specific surface area, and the interaction zones overlapped with each other. This reduced the restriction on carriers, which was favorable for the formation of a low-resistance conductive pathway that resulted in the reduced breakdown strength of the composite. On the other hand, as the mass fraction of fillers increased, the dielectric constant and dielectric loss of the composite also increased. Moreover, the heat generated in the composite increased and it was more likely to accumulate inside the dielectric, which reduced the breakdown strength of the composite.When the mass fraction of the fillers increased, Al2O3-NWs/epoxy resin composites with different mass ratios were prepared. The effect of the addition of Al2O3-NWs on the thermal conductivity of the composites was investigated. The conclusions were as follows:In this study, Al2O3-NWs formed heat-conducting chains and networks inside the composite.The thermal conductivity of the composites was enhanced compared with the bare epoxy resin. The thermal conductivity increased with increasing mass fraction of fillers. When the mass fraction of the fillers was 10%, the thermal conductivity of the composite was 34% higher than that of the epoxy resin. This was probably due to the fact thatAl2O3-NWs can improve the insulating performance of the composite. The volume resistivity of the composite increased and then decreased as the mass fraction of Al2O3-NWs increased. When the mass fraction of the packing reached 3%, the internal agglomeration of the composite material was insignificant and the introduction of deep traps restricted the motion of carriers. Consequently, the volume resistivity reached a maximum value 38% higher than that of the epoxy resin.Additionally, it was determined that the presence of Al2O3-NWs enhances the dielectric performance of composites. It was observed that the dielectric constant of the composite increased with increasing mass fraction of Al2O3-NWs, while its dielectric loss decreased and then increased as the mass fraction of Al2O3-NWs increased. As the frequency increased, the dielectric constant of the composites decreased, while the dielectric loss increased. This might be due to the interactions between the Al2O3-NWs and the epoxy resin matrix as well as the interface effect, both of which affects the polarization performance of the composite.The presence of Al2O3-NWs can also improve the breakdown performance of the composite. The breakdown strength increased and then decreased as the mass fraction of Al2O3-NWs increased. When the mass fraction of the filler reached 3%, the volume resistivity of the composite material reached the maximum, and the dielectric loss was low. The heat accumulation inside the composite material was lower than that of samples with different mass fractions, and the AC breakdown strength reached the maximum.The presence of Al


The piezoelectric actuator is indispensable for driving the micro-manipulator. In this paper, a simplified interval type-2 (IT2) fuzzy system is proposed for hysteresis modelling and feedforward control of a piezoelectric actuator. The partial derivative of the output of IT2 fuzzy system with respect to the modelling parameters can be analytically computed with the antecedent part of IT2 fuzzy rule specifically designed. In the experiments, gradient based optimization was used to identify the IT2 fuzzy hysteresis model. Results showed that the maximum error of model identification is 0.42% with only 3 developed IT2 fuzzy rules. Moreover, the model validation was conducted to demonstrate the generalization performance of the identified model. Based on the analytic inverse of the developed model, feedforward control experiment for tracking sinusoidal trajectory of 20 Hz was carried out. As a result, the hysteresis effect of the piezoelectric actuator was reduced with the maximum tracking error being 4.6%. Experimental results indicated an improved performance of the proposed IT2 fuzzy system for hysteresis modelling and feedforward control of the piezoelectric actuator. Smart material based actuators are new types of actuators different from the traditional electromagnetic actuators. Among these smart actuators, the piezoelectric actuator is widely used in the field of micro-/nano- positioning and manipulation ,3,4,5,6,However, the nonlinear hysteresis of the piezoelectric actuator has an influence on the positioning or manipulating accuracy of these applications. Under the hysteresis effect, the displacement of the piezoelectric actuator is a function of not only the current input voltage but also the previous displacement or input voltage.Various modelling methods and control strategies have been proposed to tackle the hysteresis and its effect. The Prandtl-Ishlinskii model ,15,16,17Extensive research works into interval type-2 (IT2) fuzzy system were carried out during the last decades . In factIn this paper, an IT2 fuzzy system with an analytic inverse was designed, and applied to hysteresis modelling and feedforward control of the stacked piezoelectric actuator with practical experiments. The remaining organization of this paper is as follows: in A is a set function on the universe X into   , and a tA is the crisp set of all the following pointsThe support of a type-1 fuzzy set X and U are the universes for the primary variable x and the secondary variable u, respectively.A type-2 fuzzy set The footprint of uncertainty (FOU) of the type-2 fuzzy set The type-2 fuzzy set L is the number of fuzzy rules.The simplified IT2 fuzzy system has the following no (T-S)  fuzzy ruThe final defuzzified output of the IT2 fuzzy system can be determined byThe fuzzy system uses singleton fuzzifier and direct defuzzifier. Without type-reduction, the output of the fuzzy system is analytically computed via the Nie-Tan method . It can There are 2 variables,  rule in  is the sSome remarks regarding the proposed simplified design of the developed IT2 fuzzy system are as follows:The analytic partial derivative of the output function in  with resThe partial derivative of the output function in  with resThe derivation of  and 15)15) are sTherefore, the gradient of y^k in  with resBased on the gradient in , many grThe experimental platform is mainly comprised of 4 parts: (1) piezoelectric actuator; (2) power amplifier; (3) strain gauge sensor (SGS) signal conditioner, and (4) real-time control platform (RTCP) AD5436A, as shown in As shown in N is the number of the sampled data. The input The cost function for optimization is defined asChoose the value fminunc can also be used.Use gradient based method to optimize the antecedent and consequent parameters of IT2 fuzzy system based on −18. GradThe IT2 fuzzy system of  is identUse  to compuHysteresis model identification of the proposed IT2 fuzzy system can be executed as follows:The identified parameters of IT2 fuzzy system are listed in To evaluate the modelling performance, 2 types of error index are defined as: To validate the identified model, the following input voltage signals of different profiles are used: In the model validation experiments, totally 4 different input signals were used to excite the piezoelectric actuator and the corresponding displacement was measured. These input signals consist of two sinusoidal signals of  with fmsignal of  with fmtThe hysteresis model is firstly identified based on the measured data of the piezoelectric actuator under the excitatory input voltage for identification of . Then, tCompared with feedback control, feedforward control does not indispensably need the expensive sensor for its practical implementation. It is suitable for the applications where the sensor is not feasible or easy to be deployed for directly monitoring the plant or where the cost is a top priority and strictly limited. The plant’s model and especially its inverse are generally needed for the feedforward control.Based on , the equThen, it can be transposed asHence, the analytic inverse, An open-loop feedforward controller is designed for the piezoelectric actuator based on the inverse model of  as shownTs of .The desired displacement was chosen as With analytic gradient and inverse, a simplified IT2 fuzzy system was developed for hysteresis modelling and feedforward control of the piezoelectric actuator. Experimental results demonstrated excellent performance of the proposed IT2 fuzzy system with only 3 fuzzy rules. Future work will involve: (1) other optimization methods such as evolutionary computation and neural networks for identifying the parameters of IT2 fuzzy system, and (2) feedback control algorithm incorporating the inverse model of IT2 fuzzy system.


XFR showed that RCAs have lower levels of CaO and Al2O3 concerning NA. Potential reactions in asphalt mixtures by nitration, sulfonation, amination of organic compounds, and reactions by alkaline activation in the aggregates were discarded due to the minimum concentration of components such as NO2, (–SO3H), (–SO2Cl), and (Na) in the aggregates. Finally, this research concludes that studied RCAs might be used as replacements of coarse aggregate in asphalt mixtures since chemical properties do not affect the overall chemical stability of the asphalt mixture.The incorporation of a recycled concrete aggregate (RCA) as a replacement of natural aggregates (NA) in road construction has been the subject of recent research. This tendency promotes sustainability, but its use depends mainly on the final product’s properties, such as chemical stability. This study evaluates the physical and chemical properties of RCAs from two different sources in comparison with the performance of NA. One RCA was obtained from the demolition of a building (recycled concrete aggregate of a building—RCAB) and another RCA from the rehabilitation of a Portland cement concrete pavement (recycled concrete aggregate from a pavement—RCAP). Characterization techniques such as X-ray fluorescence (XRF), X-ray diffraction (XRD), UV spectroscopy, and atomic absorption spectrometry were used to evaluate the RCAs’ coarse fractions for chemical potential effects on asphalt mixtures. NA was replaced with RCA at 15%, 30%, and 45% for each size of the coarse fractions . The mineralogical characterization results indicated the presence of quartz (SiO Waste generation during the execution of construction activities (construction and demolition waste—CDW) has prompted researchers from several countries to develop projects aimed at making construction waste reuse viable ,2,3,4,5.Standard physical and mechanical laboratory tests for checking the quality of NA are used to evaluate RCAs and their potential performances in asphalt mixtures ,9. HowevThe chemical and mineralogical compositions of RCAs are varied and do not follow a general pattern in terms of its elements, compounds, and concentrations due to the various RCA sources and the different dosages of their original concrete components ,8,11. Fu3) and the rest (40%) include clay, silica, and dolomite. However, RCAs can contain salts formed from potassium (K) and cobalt (Co), and potassium (K) and iron (Fe), which can cause aging in asphalt pavements, as they can oxidize asphalt cement  (34.3%) compared to RCAP (27.3%). This higher content can be attributed to the nature of the aggregate and the cement composition used in the concrete manufacturing.RCAB had a higher content of dolomite [CaMg(CO2(AlSi3) O10(OH)2 and anorthoclase 2O), were present in proportions of less than 5%. Similarly, the presence of pyroxenes or steatite was demonstrated, which can be attributed to traces of pozzolans in the residues.In the case of RCAP, both muscovite KAl2, and Al2O3 in each of the mixtures produced with NA, RCAB, and RCAP. A more significant presence of silicon dioxide (SiO2) was observed when there was no RCA mixture (RCAP and RCAB). The SiO2 reduced from 24.3% to 22.7% such that the percentage of RCAB increased, and between 24.3% to 22.3% for the increase in RCAP content, because of the lower presence of mortar. Calcium oxide (CaO) increased with recycled aggregate content in the mixture, while the aluminum oxide (Al2O3) did not show any significant difference.− present. On the other hand, the sulfate content in the natural aggregate was higher than for RCAs for sizes 25.0–19.0, 19.0–12.5, and 12.5–9.5 mm, which can be associated with the aggregate source. The sulfate content in RCAB was higher for all the fractions compared to RCAP, which may cause susceptibility to degradation attributable to the expansion and cracking in the concrete of corresponding asphalt mixtures [3− of surface water in contact with the material in service.mixtures . The pre2. The heavy metal values were insignificant in the leaching process. For example, the lead content did not exceed 0.3 mg/L. This condition means that RCAs will not become sources of environmental pollution, a vital criterion to be subsequently used without toxic risk. The presence of mercury in one of the fractions (9.5–4.75 mm) of RCAB with a value of 11.56 mg/L is explained by the existence of mercury in soils. Reactions with some complex compounds in organic matter  and soil clays cause the mercury to remain for some time. These compounds can reach the RCAs by the exploitation of river sands or soils contaminated with mercury [To determine the environmental impact related to ionic leaching in the asphalt mixtures, an analysis was performed using atomic absorption spectrometry for the different fractions of the three aggregates. In  mercury . In the  mercury .Another important indicator is iron (Fe) found in natural aggregates, and its value in RCA decreases to approximately 50%. This can be associated with the nature of the area where the natural aggregates were exploited and now as part of the RCA.This section seeks to analyze the chemical stability and possible interactions between the aggregates and the asphalt cement, assuming that these aggregates are used in the manufacture of asphalt mixtures.It should be noted that the temperature at which the asphalt mixture works depends on environmental factors and the frequency and type of traffic once it begins its service. The only time where this mixture is exposed to high temperatures (above 150 °C) is during its manufacturing and storage. Likewise, the type of technology used in manufacturing the mixture  does not significantly affect the time because the system is subjected to high temperatures for a few hours . The rea2) to the hydrocarbon chain [3 in the aggregates could indicate the presence of sulfuric acid, its concentration and nitric acid together are not sufficient to induce these reactions. Therefore, the only fixed nitrogen in the aggregates is in the form of nitrates , or the corresponding salt or halogen group of sulfonic acid (–SO2Cl) in an organic compound [3 indicates that there could be sulfuric acid present in the aggregates  could occur. Besides, the content of SO3 could even act as an adhesion-improving agent.The second reaction is the sulfonation of the compounds present in asphalt. This possible reaction consists of the inclusion of the sulfonic acid group  with ammonia . AlthougBefore considering the possible oxidation reactions, it should be noted that the halogenation reactions of the asphalt binder are not spontaneous under the temperature and concentration conditions where the mixture is found. In these reactions, halogen atoms are introduced into the hydrocarbon chain . Althoug2 and Na2O [2 and CaO  can be approximated in a molar proportion as the substitution percentage of the RCA (RCAP and RCAB) in the mixture increases. The proportion of SiO2 decreases while that of CaO increases when more RCA is added, without reaching the required values of molar proportion. There are aggregates with significant silicon and calcium contents such as quartz (SiO2) and calcite (CaCO3) in addition to other silicates  such as albite (NaAlSi3O8), muscovite KAl2(AlSi3O10) (OH)2, and anorthoclase 2O). These aggregate types have proportions lower than 5% and require generating the necessary conditions by adding other solutions to increase the molar amount of sodium species (Na) and promote alkaline reactions.For the fourth reaction case, reactions could occur by alkali activation, which can improve the production of the binder and binder material. In and Na2O . In addiFinally, it is important to underline that even though in this work the evolution of the chemical composition of an asphalt mixture (using RCAs) was not monitored, some research allows us to affirm that the mixture is sufficiently stable over time. Asphalt binders have been used before as stabilization matrices for non-hazardous and hazardous wastes ,47,48. BThe physical characterization showed that the RCAs have lower density and higher absorption than NA. This result can be explained by the presence of mortar adhered in RCAs. As an RCA replaces the NA, the optimal asphalt content (OAC) increases due to higher absorption of the RCA, which ultimately increases the cost of the asphalt mixture.Coarser RCAs (25.0–19.0 to 9.5–4.75 mm) primarily contain quartz, calcite, and dolomite, which provide chemical and mineralogical characteristics suitable for use in asphalt mixtures.2 concerning the NA, and pH higher than 11.7, all promote the adhesion with the binder in the asphalt mixtures.The increase in the concentration of CaO in the RCA, the decrease in SiO2 to Na2O found in the RCAs, the reduction in the ratio between SiO2 and CaO  in the replacements  of RCAs, and the absence of NaOH in non-asphalt mixtures promote alkaline activation reactions, which favor the chemical stability of the mixture.The ratio of SiO− ions—up to 482 mg/L, some of Cl− ions came from stable salts that were not reactive with the organic compounds present in the asphalt binder.Although the exposure time of the RCAs to the coastal environment led to a high concentration of ClThe nitrate and sulfate contents in RCAs do not promote nitration and sulfonation reactions owing to the absence of catalytic agents such as nitric and sulfuric acids, which favor chemical stability in asphalt mixtures.Dissolved metals in RCAs support the use of RCAs in asphalt mixtures because the lead and mercury contents do not generate adverse environmental impacts.Based on the chemical, mineralogical, and physical characteristics of the RCAs, the replacement of NA with an RCA in the manufacture of hot mix asphalt is an alternative viable. According to the results of this study, it is suggested to improve regulations to promote the use of RCAs in the manufacture of asphalt mixtures with less environmental impact in relation to conventional aggregates.Based on the results of the study, it can be concluded that:Despite having very good and interesting results in this research, the authors think that more experiments are necessary to obtain a more robust average of the results. For example, more analysis with different sources is necessary to obtain the RCAs and to understand the performance in the mixture. Other important laboratory experiment should analyze the absorption of the asphalt with different RCAs in order to assess the asphalt consumption and the costs of implementation in projects of roads. Future studies should also include the formulation of analysis guides for RCAs, where the way to chemically study them is standardized, and thus obtain indices and potential reactions between aggregates and asphalt mixtures, which should give standard data and reactions.