Coefficients of cross-correlations (p < 0.05) between the monthly (01.01.1985–31.12.1989) values of the morbidity.
\r\n\tWithin this scenario, special attention needs to be devoted to financial implications, due to their pervasiveness. Nobody would question the key role that finance plays to complement the real sphere of the economy and that has increasingly attracted both academics and practitioners. As a result, traditional pillars – such as financial markets, products, and institutions – have evolved significantly, with financial innovation fueling further progress over time. The global side of the coin features – among others – financially connected markets, international financial exchanges, and financial conglomerates that provide valuable opportunities in terms of international corporate finance. On the other side, recent advances have involved a wider recourse to ESG factors, allowed forward steps towards a more inclusive financial system, and have made digital finance a must, rather than an option, even though much remains to be accomplished, for instance, to facilitate access to formal financial channels in many underdeveloped regions.
\r\n\r\n\t
\r\n\tThis book aims to examine emerging trends, new perspectives, and empirical applications that deal with globalization and sustainability. The goal is to provide a comprehensive overview of these important concepts as valuable support to successfully meet the challenges and take on the opportunities ahead. At the same time, drawing upon empirical evidence can contribute to bridging the gap between theory and practice, which also fits within the scope of this book.
The Spitsbergen archipelago is located in the Arctic Ocean, between 76° 26‘ and 80° 50’ north latitude and 10 and 32° east longitude. A geophysical feature of the arch. Spitsbergen is its location in the cusp region [1]—a kind of funnel on the dayside of the magnetosphere with near zero magnetic field magnitude, where, under certain conditions, the solar wind (CW) can burst through powerful plasma jets (Figure 1, [2]). The open field lines of the cusp is connected with those of the interplanetary magnetic field (IMF), which allows the shocked solar wind plasma of the magnetosheath to enter the magnetosphere and to penetrate the ionosphere [3].
\nEarth’s protective shield:
The Earth’s magnetosphere is a highly dynamic structure that responds dramatically to solar variations [4, 5], especially in the cusp region [6]. The upper atmosphere at high latitudes, associated with cusp, is also called the “Earth’s window to outer space.” Through various electrodynamic coupling processes as well as through direct transfer of particles, many geophysical effects displayed that there are direct manifestations of phenomena occurring in the deep space. In the polar cusps, the solar wind plasma has also direct access to the upper atmosphere. The polar regions are thus of extreme importance when it comes to understanding the physical processes in the near space and their effect on our environment” [6].
\nIn the cusp areas, the impacts of the solar wind (SW) on the Earth’s magnetosphere manifest most strongly, and multiple phenomena originating as consequences of such interactions are referred to as space weather. It can be truly said that space weather affects everybody, either directly or indirectly. Space weather is defined by the U.S. National Space Weather Program (NSWP) as “conditions on the Sun and in the solar wind, magnetosphere, ionosphere, and thermosphere that can influence the performance and reliability of space-borne and ground-based technological systems and can endanger human life or health” [7, 8].
\nSpace weather begins at the sun. The sun exhibits an 11-year cycle of sunspots that are visible manifestations of increased solar magnetic field. Certain larger flares produce solar radio bursts of broadband noise from 10 MHz to 10 GHz that may directly affect GPS receivers on the dayside of the earth. Terrestrial effects are the result of three general types of conditions on the Sun: eruptive flares, disappearing filaments, and coronal holes facing Earth [9], on which the nature of magnetosphere-ionosphere interactions depends. The magnetosphere and the ionosphere of the Earth are sources of electromagnetic oscillations and waves, many of which are detected in the form of radiation outside the region of generation, in particular, on the surface of the Earth. The electromagnetic radiation range of the magnetosphere and ionosphere overlaps in frequency by many orders of magnitude—from the lowest frequencies of magnetohydrodynamic (MHD) waves (f ~ 5–10−3 Hz) to X-rays of energetic electrons in the upper atmosphere (f ~ 1018 Hz) [10]. The complexity and diversity of physical phenomena associated with solar activity and transmitted to earth through solar-terrestrial connections make the issue of identifying bioeffective agents in the space weather phenomenon nontrivial and rather complicated. Some of the cosmophysical phenomena, as attributes of space weather, are most pronounced and specific for high latitudes and for the polar cusp [11, 12, 13, 14].
\nThe unique data characterizing morbidity of the residents in the Russian settlements of the Barentsburg (1985–1993), including the females, were used in the study. The statistics on the complications about pregnancy and the postpartum period in women, who lived in the archipelago during the time of the former USSR, provide invaluable information that allows assessing the effect of space weather associated with the polar cap and the polar cusp on pregnant women. Today, such research is extremely difficult, because the residence of pregnant women in the Spitsbergen archipelago is undesirable.
\nThe monthly statistical reports on the morbidity structure in the Barentsburg mine hospital (1985–1993) were basis for analysis [15]. All data of morbidity were normalized on 1000 people of residents in the Barentsburg. The average number of inhabitants in each Russian settlement (Barentsburg and Pyramid) was about 1000, where one third were women. The average monthly data characterizing the CA were selected in the National Geophysical Data Center (NGDC): Solar Data Services (http://www.ngdc.noaa.gov/stp/SOLAR/ftp: sunspotnumber.html); intensity of the secondary cosmic rays (CR) was estimated by neutron count rate (ground station of the neutron monitor of the PGI KSC Russian Academy of Sciences in the Apatity and in the Barentsburg). Statistical data analysis was performed using the software Statistica 10.0 and the graphing was carried out using the software package ORIGIN50.
\nThe bioefficiency of geocosmic agents is manifested in synchronous dynamics of the functional state of resident’s organism in the high latitudes [16, 17, 18, 19, 20, 21] or in the coherency of morbidity dynamics of the population in the Arctic territories [22] with variations of the geocosmic agents on the time scales with different resolution (day, month, and year).
\nThe coefficients of cross-correlations between the monthly (01.01.1985–31.12.1989) values on the curves, smoothed by 5 points, of the morbidity and the solar radio flux f10.7_index are demonstrated inTable 1.
\nCoefficients of cross-correlations (p < 0.05) between the monthly (01.01.1985–31.12.1989) values of the morbidity.
The incidence of the mental disorders (MD); the diseases of the eye and its appendages (DEA); the diseases of arteries, arterioles and veins (DAAV); the incidence of the inflammatory processes of the female pelvic organs and other diseases of the female genital organs (IFGO); the infections of the skin and subcutaneous tissue (ISST); diseases of the musculoskeletal system and connective tissue (DMSSCT); the injuries and poisoning on the way to and from work (IP) and the solar radio flux with wavelength 10.7 cm (f10.7-index). Significant correlations are marked by red color.
The synchronism of the incidence diseases follows from the cross-correlation coefficients shown in Table 1, where one can see that the monthly values of the incidence of the mental disorders (MD) have significant correlation coefficients with injuries and poisonings (IP) and with f10.7-index. However, IP correlates with other diseases (Table 1): with DEA), with DAAV, with IFGO, with ISST, and with the fluxes of solar radio emission (f10.7-index).
\nOne can see certain concordance between the curves of the average monthly angular parameters of the solar wind (sigma-phi-V, deg.,), the monthly dynamics of incidence of the mental disorders (MD), the injuries, and poisoning (IP) in Figure 2A. Coefficient correlations between sigma-phi-V and the MD, sigma-phi-V, and IP are r = 0.32, r = 0.44, and p < 0.05, respectively. In this case, the MD and the IP diseases are not only interconnected by connection with the solar radio emission (Table 1, f10.7-index), but also with the parameters of the solar wind (sigma-phi-V, deg). This suggests that the solar wind could generate such conditions in the cusp area, when the physical agents might affect the mental state, and through it, the predisposition to the appearance of the injury.
\nCoherent dynamics of the average monthly values of the parameters of geocosmic agents and the monthly values of morbidity. A. Parameter of solar wind “sigma-phi-V, deg” (1, graph area—cyan), the incidence of the mental MD (2), incidence of the injuries and poisoning on the way to and from work, IP (3). B. Solar radio emission with wavelength 10.7 cm (1, graph area—cyan), incidence of the diseases of arteries, arterioles, and veins (2), sigma-theta-V, deg. (3), pc (N) index (4).
In Figure 2B, one can see concordance between curves of average monthly variations of the solar radio flux at 10.7 cm, dynamics of monthly diseases of arteries, arterioles, and veins and (DAAV), average monthly values of the sigma-theta-V, deg. and average monthly values of the Pc (N)-index. Correlation coefficients between f-10.7-index, sigma-theta-V, deg., Pc (N), and DAAV are r = 0.40; r = 0.29; r = 0.27; and p < 0.05, respectively. The positive relationship between the incidence of DAAV, the f-10.7-index, and PC(N) means that with increasing solar activity and associated geomagnetic disturbances, the morbidity of DAAV also increases. The connection between PC and the DAAV demonstrates the effect of the space weather on the vascular system of human organism.
\nFigure 3A shows the connection between the dynamics of monthly pregnancy complications (IFGO), the parameter of space weather (hydrodynamic pressure of the solar wind), and the ap-index reflecting the local geomagnetic activity. The connection between the dynamics of monthly inflammatory processes of the female pelvic organs and other diseases of the female genital organs (IFGO), the F10.7-index, and PC(N) are shown in Figure 3B. Correlation coefficients between IFGO, flow pressure, and ap-index are r = 0.34; r = 0.29, respectively, p < 0.05. Correlation coefficients between IFGO, F10.7-index, and PC(N) are −r = 0.34; r = 0.29, respectively, p < 0.05.
\nCoherency dynamics of the monthly values of morbidity and the monthly average values of the parameters of geocosmic agents. A. Incidences of the complications of pregnancy and the postpartum period, CPP (1); flow pressure of the solar wind, (nPa) (2); and ap-index (3). B. Incidences of the inflammatory processes of the female pelvic organs and other diseases of the female genital organs, IFGO(1), the solar radio emission with wavelength 10.7 cm (2), PC(N)-index.
One can again remark that morbidity, even specific such as diseases of the female genital system, is associated with solar and geomagnetic activity, expressed by the ground indicators of local geomagnetic storm conditions PC(N), ap-index), and the agents in the near Earth space (F10.7-index, variations of the angle velocity of solar wind—sigma-phi-V, sigma-teta-V, deg., flow pressure). The fluctuations of the monthly values of morbidity of the somatic diseases, the mental disorders, and the frequency of injuries and poisoning, as well as the coherency of the diseases among themselves and with the space weather indicators suggest that space weather controls the state of the human body in Svalbard.
\nThe source of physical phenomena, some of them could have a pronounced bioefficiency, is the magnetosphere-ionosphere interaction, reflecting the interaction of the solar plasma with the earth’s magnetosphere in the polar cusp region. Since the properties of the ionosphere are largely determined by Solar X-
Ionospheric differences during the polar day and the polar night are also confirmed by differences in the electrical current systems in the summer season and in the winter due to current vortex, which is most noticeable in the summer season [23]. The total electron content (TEC) exhibits significant spatial and temporal variations, when the minimum level of TEC observed in the high latitude of the northern hemisphere in the mid polar night (December) and the maximum level—in the mid polar day [24]. A characteristic feature of geomagnetic disturbances in all hours is the presence of pulsations with large amplitudes and periods of several minutes. And some of them practically disappear during the polar night [14, 25, 26, 27, 28, 29].
\nTo appreciate the significance of the space weather agents (geocosmic agents) affecting the human health in the polar days and in the polar night, the monthly data sets of the morbidity in the settlement of Barentsburg were sorted in two groups. In the first group was included the monthly values of morbidity in the polar day (from March to September, n = 35) and in the second group—the monthly values of morbidity in the polar night (from October to February, n = 25). This sorting was performed due to the duration of the dark time (122 days) from 21 October to 20 February at 80 degrees north latitude [30]. Significant differences between the incidence of the population during the polar day and the polar night, as well as differences in the values of geophysical indicators, have been estimated by using the nonparametric (the Mann-Whitney U test, Kolmogorov-Smirnov criterion) and the parametric T-criterion.
\nIt turned out that the monthly values of incidence during the polar day and night significantly differ only in cases of intestinal infections (yersiniosis) and the inflammatory processes of the female pelvic organs and other diseases of the female genital organs (IFGO). During the polar day and the polar night, incidences of intestinal infections were 0.05 ± 0.21 and 0.25 ± 0.49, respectively, p < 0.05; incidences of IFGO were 1.89 ± 2.58 and 3.70 ± 3.62 in the polar day and in the polar night (according to the Mann-Whitney U-test T-criterion). The geophysical indices differed only in the monthly average values of atmospheric pressure (992.36 ± 4.01 and 987.58 ± 7.70, p < 0.005, mb), in the Bulk flow latitude (2, 24 ± 0.67 and 1.16 ± 0.93, p < 0.001, degrees), in the DST index (−16.07 ± 12.79 and − 22.16 ± 8.91, nT, p < 0.025), and in the PC (N) index (0.96 ± 0.35 and 1.14 ± 0.24, p < 0.005), respectively, in the polar day and in the polar night. That is, in fact, the incidence rate on the polar day and on the polar night, with a few exceptions, just as the monthly average of geophysical agents, with the exception of 2 indices characterizing geomagnetic activity, does not differ.
\nHowever, when correlations between the monthly values of morbidity and the monthly average values of geophysical agents corresponding to the polar day and to the polar night periods were compared, it turned out that there are large differences between them. These differences indicate that during the polar day and during the polar night, the roles of similar geophysical agents are different.
\nOne can see above (Table 1) that the monthly values of the incidences of the MD, DEA, DAAV, IFGO, ISST, DMSSCT, and IP are associated with solar radio flux with a wavelength of 10.7 cm (f10.7_index), characterizing the solar activity (SA). This means that the Sun is the source of causal relationships, starting with SA and ending with the morbidity of the population on the Earth. But at the same time, the cause of the morbidity can be other bioeffective agents associated with SA, whose contribution to the morbidity can depend on multiple reasons, including the properties of the ionosphere during periods of the polar day and the polar night.
\nA comparative analysis of the correlations of the same classes of morbidity with geophysical indices, separately for the polar day and for the polar night, showed that there are both general and particular trends in the nature of the relationship between the morbidity and geocosmic agents. There are correlations, which appear only during the polar day: mental disorder (MD), diseases of the arteries, arterioles, and veins (DAAV), the gastritis, the kidney and urinary tract diseases, the complications of pregnancy and the postpartum period, and other diseases. Diseases such as the pneumonia, the ischemic heart disease, and other forms of heart disease without hypertension are correlated with geocosmic agents only during the polar night. There are diseases with a mixed nature of the connections with geocosmic agents during the polar day and the polar night.
\nFigure 4 shows that during the period of the polar day, dynamics of the monthly values of incidences of the mental disorders, MD, and dynamics of the monthly values of incidences of the diseases of arteries, arterioles, and veins (DAAV) are associated with variations of solar wind parameters such as “sigma-phi-V” and the solar radio emission with wavelength 10.7 cm. Along with these parameters of geocosmic agents, other parameters of IMF and SW, as well as, possibly, their combination and interaction, can make a certain contribution to the modulation of cases of mental disorders (Table 2).
\nThe relationship between morbidity and geocosmic agents, which appears only in the period of the polar day. A. Dynamics of the monthly values of incidences of the mental disorders, MD (1). B. Dynamics of the monthly values of incidences of the diseases of arteries, arterioles, and veins, DAAV (1); A, B. Dynamics of the monthly average magnitudes of the solar wind parameters “sigma-phi-V” (2) and the solar radio emission with wavelength 10.7 cm (3).
Period | \nNM | \nPres | \nBz | \nPr-Den | \nδ phi | \nR | \nf10.7 | \nPC(N) | \nMakh | \n|
---|---|---|---|---|---|---|---|---|---|---|
PD | \n1.21 ± 1.20 | \n|||||||||
PN | \n1.08 ± 1.08 | \n−0.04 | \n−0.06 | \n0.02 | \n0.07 | \n0.13 | \n0.13 | \n0.16 | \n−0.06 | \n−0.26 | \n
Correlation coefficients between monthly values of incidents of the mental disorders (MD) and monthly average magnitudes of the parameters of geocosmic agents during the polar day (PD) and polar night (PN).
NM—count rate of ground based on neutron monitor (counts/s); Pres—atmospheric pressure (mb); Bz-BzGSE—Bz component of interplanetary magnetic field (IMF) in the geocentric solar-ecliptic coordinate systems, nT; Pr-Den—proton density in the solar wind, N/cm3; δ ph—sigma-phi-V—solar wind angle parameter, deg.; R—sunspot number; f10.7-index of the solar radio flux with wavelength 10.7 cm in solar flux units (s.f.u.), (10−22), Watts/meter sq/hertz; PC(N)—Index of geomagnetic activity in the high latitude; Makh—Magnetosonic mach number = V/Magnetosonic speed. Coefficient values marked in red color correspond to the level of significance p < 0.05.
The same can be seen in Table 3, which shows the links of the diseases of arteries, arterioles, and veins (DAAV) with variations of geocosmic agents, reflecting the complex nature of the effects of physical agents on the diseases of blood vessels.
\nPeriod | \nM ± δ | \nNM | \nδ-By | \nδ-Bz | \nNa/Np | \nδ-phi | \nδ-theta | \nR | \nf10.7 | \n
---|---|---|---|---|---|---|---|---|---|
PD | \n0.70 ± 0.79 | \n||||||||
PN | \n0.61 ± 0.81 | \n−0.20 | \n−0.22 | \n−0.06 | \n−0.06 | \n0.04 | \n0.24 | \n0.26 | \n0.31 | \n
Correlation coefficients between monthly values of incidents of the diseases of arteries, arterioles, and veins (DAAV) and the monthly average magnitudes of the parameters of geocosmic agents during the polar day (PD) and polar night (PN).
NM—count rate of ground based on neutron monitor (counts/s); δ-By—sigma By—variability of By-component of IMF, nT; δ-Bz—sigma Bz—variability of Bz-component of IMF, nT; Na/Np—alpha/proton ratio in the solar wind; δ phi, δ-theta—sigma-phi-V, sigma-theta-V—solar wind angle parameters, deg.; R—sunspot number; f10.7-index of the solar radio flux with wavelength 10.7 cm in solar flux units (s.f.u.), (10−22), Watts/meter sq/hertz. Coefficient values marked in red color correspond to the level of significance p < 0.05.
In general, it can be seen that cases of mental disorders and vascular morbidity are associated with SA, manifested by variability of the solar wind (SW) and IMF during the polar day period. This may mean that, as a result of the interaction of the SW and IMP with the Earth’s magnetosphere, physical phenomena generated in the polar cusp region during polar day could contribute to an unstable mental state and vascular disorders.
\nOne can assume that these phenomena have an electromagnetic and wave nature, which determines their bioefficiency. One of the most likely candidates in a wide range of physical phenomena detected in the cusp region is low-frequency pulsations [14].
\nThe pulsations in the spectral range (1–5 mHz) with different morphological properties and, accordingly, with different physical nature are observed at high latitudes (Φ > 70°). It is established that the long-period (T ~ 4–60 min) geomagnetic pulsations observed both in daytime and nighttime hours are typical phenomena on the polar cusp latitudes. The most typical fluctuations of the daytime cusp observed on the earth’s surface are specific broadband irregular pulsations of the Pc5 range (f ~ 1.5–5.0 mHz) with an amplitude of the order of 15–60 nT, named by V.А. Troitskaya
The daytime geomagnetic pulsations
In the higher frequency range, the broadband noises from Pc3–4 (10–40 mHz) to ELF choirs (0.3–3.0 kHz) are often observed in the high latitude. The intensity of the Pc3–4 waves in the polar cusp depends on the ionospheric conductivity, which causes a sharp weakening of the waves during the polar night [28, 31, 32].
\nSummarizing the descriptions of physical phenomena associated with the processes of the interaction of the solar wind and IMF with the earth’s magnetosphere in the polar cusp region, one can see that the polar day differs from the polar night by more diverse geocosmic events. These events are dependent on ionospheric conductivity, which determines diverse phenomena, including amplitude and frequency characteristics of high latitude pulsations.
\nIt has now been established [33, 34, 35, 36] that brain rhythms include ultra-slow frequency oscillations (USFO), which are usually not detected by standard electroencephalogram measurements. The frequency range of these oscillations corresponds to very low-frequency pulsations Pc3–4 characteristic of a polar cusp. Among the ultra-slow fluctuations, the rhythm with a period of 15–40 s is remarkable in that the human brain is accompanied by transitions of levels of consciousness, for example, transitions to the hypnotic state. The fluctuations in the decasecond range correspond to the period of fluctuations of the pulsations Pc3, the amplitude and intensity of which are significantly higher during the polar day than in the polar night. It is not excluded that Pc3–4 pulsations can contribute, along with other factors, to the unstable mental state of the residents of arch. Spitsbergen.
\nSignificance (p < 0.05) of correlation coefficients between MD and DAAV (r = 0.40), between MD and DAAV and solar activity (Tables 2 and 3) in the polar day and the absence of significance of correlations between these morbidity and SA indices (R, F10.7) during the polar night indicate common causes, which determine the relationship between the morbidity and geophysical agents in the polar day. We assume that such common causes may be geophysical agents associated with the illuminated ionosphere during the polar day. It is possible that geomagnetic pulsations, in the ultralow frequency range, most pronounced during the polar day, could modulate brain and vascular functional activity and, accordingly, certain mental states. In particular, they might suppress the cognitive processing and promote switching of the brain to its noncognitive “idling” state or activation of default cortical networks whose activity is suppressed during cognitive processing [37, 38].
\nThe different significance of physical agents for different systems of the body can be seen on the basis of the mutually exclusive nature of the connections with similar geocosmic agents in the polar day and in the polar night (Figure 5, Table 4).
\nThe mutually exclusive nature of the connections with similar geocosmic agents of the various diseases in the polar day (A) and in the polar night (B). A. Dynamics of the monthly values of incidences of diseases of the musculoskeletal system and connective tissue (DMSSCT) (1), the monthly average magnitudes of interplanetary magnetic field (IMF), nT (2), the monthly average magnitudes of the alpha/proton ratio in the solar wind (Na/Np), (3); B. Dynamics of the monthly values of incidence of the infections of the skin and subcutaneous tissue (ISST) (1), the monthly average magnitudes of IMF (2), the monthly average magnitudes of the Na/Np (3).
Period | \nM ± δ | \nNM | \nIMF | \nFV|<B>| | \nBz,GSM | \nδ-B | \nδ-By | \nδ-Bz | \nNa/Np | \n
---|---|---|---|---|---|---|---|---|---|
Diseases of the musculoskeletal system and connective tissue (DMSSCT) | \n|||||||||
PD | \n3.66 ± 2.48 | \n||||||||
PN | \n3.42 ± 2.95 | \n−0.25 | \n0.26 | \n0.31 | \n−0.21 | \n0.15 | \n0.17 | \n0.21 | \n0.16 | \n
The infections of the skin and subcutaneous tissue (ISST) | \n|||||||||
PD | \n1.83 ± 1.61 | \n−0.20 | \n0.23 | \n0.25 | \n−0.12 | \n0.16 | \n0.13 | \n0.12 | \n0.15 | \n
PN | \n1.76 ± 1.27 | \n
Correlation coefficients between monthly values of incidents of the diseases of the musculoskeletal system and connective tissue (DMSSCT), the infections of the skin and subcutaneous tissue (ISST), and the monthly average magnitudes of the parameters of geocosmic agents during the polar day (PD) and polar night (PN).
NM—count rate of ground based on neutron monitor (counts/s); IMF—field magnitude Avg, <F>, nT; FV|<B>|—magnitude of average, field vector, |<B>|, nT; Bz, GSM-Bz—component INF in the geocentric solar-magnetospheric coordinate systems; δ-B—variability of the magnetic field strength; δ-By—Sigma By—variability of By—component of IMF, nT; δ-Bz—Sigma Bz—variability of Bz—component of IMF, nT; Na/Np—alpha/proton ratio in the solar wind. Coefficient values marked in red color correspond to the level of significance p < 0.05.
The only difference in the nature of the connections between these diseases and geocosmic agents is the connection with the Bz-component of IMF. This connection has a negative sign with the incidence of DMSSCT and positive sign with the incidence of ISST in the polar day. Since the negative value of the Bz-component characterizes a high GMA, it can be assumed that GMA, along with other factors, including ultraviolet irradiation, contributes to the incidence of the DMSSCT in the polar day.
\nOn the other hand, excessive irradiation of ultraviolet light during the polar day can inhibit the growth of pathogenic microflora, which causes skin diseases (ISST). But in the polar night, in the absence of ultraviolet light, the growth of pathogenic microflora can increase under the influence of factors associated with the variability of the SW and IMF [39].
\nThe importance of SA for human behavior manifests in the correlations with the cases of injury and poisoning on the way to and from work of the residents of arch. Spitsbergen (Figure 6, Table 5). Most likely, this definition hides injuries caused by the state of altered consciousness under the influence of alcohol.
\nThe stable links between injury rates and poisoning on the way to work and from work with similar geocosmic agents during the polar day (A) and the polar night (B). A, B. Dynamics of the monthly values of incidence of the injury and poisoning on the way to work and from work (1), the monthly average magnitudes of the solar radio emission with wavelength 10.7 cm (2), and the solar wind parameter “sigma-phi-V”(3).
Period | \nM ± δ | \nNM | \nδ phi- | \nδ theta | \nPl beta | \nAMN | \nKp * 10 | \nR | \nf10.7 | \nPC(N) | \nMMN | \n
---|---|---|---|---|---|---|---|---|---|---|---|
PD | \n1.75 ± 1.97 | \n0.26 | \n|||||||||
PN | \n1.26 ± 1.07 | \n
Correlation coefficients between monthly values of incidents of the injury and poisoning on the way to work and from work and the monthly average magnitudes of the geocosmic agents during the polar day (PD) and polar night (PN).
NM—count rate of ground based on neutron monitor (counts/s); δ phi, δ-theta—sigma-phi-V, sigma-theta-V—solar wind angle parameters, deg.; Pl beta—plasma beta (Beta = [(T * 4.16/105) + 5.34] * Np/B2; AMN—Alfven mach number (Ma = (V * Np0.5)/20 * B); Kp * 10-index of geomagnetic activity (GMA); R—sunspot number; f10.7-index of the solar radio flux with wavelength 10.7 cm in solar flux units (s.f.u.), (10−22), Watts/meter sq/hertz; PC(N)—high latitude index of GMA; MMN—magnetosonic mach number = V/Magnetosonic_speed. Coefficient values marked in red color correspond to the level of significance p < 0.05.
It can be assumed that an increase in SA is accompanied by the neuropsychic arousal, the anxiety, the decrease in health, and the mood, which can be causes provoking the need for alcohol in a certain category of persons. Since the level of SA in the summer and winter periods does not differ significantly, the connection of the frequency of injuries and poisonings on the way to work and from working with SA appears equally on a polar day and on a polar night according to the level of the solar activity.
\nMonitoring of the daily psycho-emotional state of the healthy volunteers in the settlement Barentsburg (arch. Spitsbergen) during polar day revealed correlations between situational anxiety, mood, activity, and indices of SA of proton fluxes with energy >10 MeV [40, 41]. It was also found that health, the activity, and the mood decreased and the situational anxiety increased under increase of SA and GMA [40, 41]. Thus, one of the causes for the increase in injuries and poisoning could be an arising of the psycho-emotional instability associated with increase in SA.
\nThe revealed differences in the nature of the links between the morbidity of the population in the Barentsburg during the polar day and the polar night show that the diverse diseases are associated with a combination of separated characteristics of the SV, MMP, GMA, and SA, the significance of which for the morbidity varies with the season.
\nA geophysical feature of the arch. Spitsbergen is its location in the cusp region—a kind of funnel on the dayside of the magnetosphere with near zero magnetic field magnitude. The open field lines of the cusp are connected with those of the interplanetary magnetic field (IMF), which allows the shocked solar wind plasma of the magnetosheath to enter the magnetosphere and to penetrate the ionosphere.
\nIn the cusp areas, the impacts of the solar wind (SW) on the Earth’s magnetosphere manifest most strongly, and multiple phenomena originating as consequences of such interactions are referred to as the space weather. The magnetosphere and the ionosphere of the Earth are sources of electromagnetic oscillations and waves, many of which are detected in the form of radiation outside the region of generation, in particular, on the surface of the Earth.
\nThe feature of the cusp is the existence of the geomagnetic pulsations not only in the period of geomagnetic disturbances but also during the quiet period. One can see that narrow band waves at frequencies 0.2 to 3 Hz are a permanent feature in the vicinity of the polar cusp. The waves have been found in the magnetosphere adjacent to the cusp (both poleward and equatorward of the cusp) and in the cusp itself. It is an established fact that the daytime polar cusp latitudes are typically characterized by long-period (T ~4–60 min) geomagnetic pulsations observed both in daytime and nighttime hours. Diverse radiation (from ULF to VHF) and waves in the field of polar cusp, covering the entire range of the body rhythms, give credit for studying the effects of space weather in the field of polar cusp. The study of the dependence cases of diseases on effects of space weather has shown that diverse forms of morbidity varied synchronously and they are associated with variations of space weather agents. Assessment of the relationship between the dynamics of the monthly morbidity in Russian settlements and indicators of space weather revealed that, practically, all forms of morbidity are associated with solar activity: with F10.7 index, with variations of solar wind parameters, and with indices characterizing the local geomagnetic activity in the polar cusp.
\nIt has been found that mental disorders are associated with the variability of the solar wind and the radio emission of the Sun, as well as the frequency of injuries and poisoning at the work and at the home. A high degree of association of the diseases of arteries, arterioles, and veins with the parameters of the solar wind and the geomagnetic indices, characterizing the local geomagnetic activity in the polar cusp, was shown.
\nA high sensitivity of the female organism to variations of space weather in the polar cusp was revealed. This phenomenon is manifested in the increase of pregnancy complications, cases of inflammation of the genital organs, etc., according to the increase in geomagnetic activity in the polar cusp.
\nThe revealed differences in the nature of the links between the morbidity of the population in the Barentsburg during the polar day and the polar night show that the diverse diseases are associated with a combination of separated characteristics of the SV, MMP, GMA, and SA, the significance of which for the morbidity varies with the season.
\nHowever, it has been found that certain diseases are associated only with the polar day or with the polar night. This allows selecting the physical agents that could modulate morbidity rate in the alternative season. In particular, agents such as long-period oscillations, with the frequency range that coincides with the range of the ultraslow fluctuations of the constant potential (USFCP) in the brain, could modulate the morbidity of the MD and DAAV in the polar day.
\nThe absence of solar radiation during the polar night, such as UV radiation, and the association of the incidence of the inflectional diseases of skin with GMA only during the polar night indicate the role of UV in suppressing the growth of pathogenic microflora. Correlations of the inflectional diseases of skin with GMA in the absence of UV radiation demonstrate the significance of GMA for the microorganism growth.
\nIn general, it should be noted that, probably, many of the bioeffective agents associated with CA were left out of consideration. The health of the population most likely depends on a combination of geophysical agents, some of which are simply not registered and are not reflected in the indicators of the database (OMNI). On the other hand, the state of the human body during the periods of the polar day and the polar night may also differ in sensitivity to the effects of geophysical agents. In general, the polar day is characterized by a larger number of influencing physical agents on the human body, than the polar night.
\nThe found links between the morbidity of the population and the peculiarities of space weather will make it possible to develop prognoses of the morbidity for preventive measures aimed at reducing the morbidity in high latitudes.
\nThe task of studying the labor activity in the difficult arctic conditions demands the need to develop criteria for determining the mental state of a person and his working capacity, as well as predicting a shift in the functional state of the CNS. The solution of such a problem should take into account the possibility of modulation of the mental and of the physiological state of people of the dangerous professions by the high latitude geocosmic agents, the effects of which might also express in the seasonal manifestation of morbidity.
\nArtificial intelligence (AI) technique is the most effective technology used in the modern healthcare area. The rapidly growing accessibility of healthcare medical data and also the advances of big data diagnostic techniques has completed the potential of the current successful uses of artificial intelligence (AI) in healthcare system. With the help of important medical questions, potential artificial intelligence (AI) techniques can disengage healthcare-appropriate information secreted in the huge quantity of data, which can maintain healthcare decision-making. Modern healthcare technology in various medical areas has spread to the several pioneering startups in the world, which helps people in healthier and longer lives. The advances have initially been determined by the beginning of mobility and software, permitting the health sector to digitize several of the pen- and paper-based processes and operations that are presently held up service release. Nowadays, computer software has become far more intelligent and autonomous. These new abilities are discussed under the same cover of machine learning (ML) and artificial intelligence (AI), which are accelerating the tempo of improvement in healthcare. The applications of machine learning (ML) and artificial intelligence (AI) in healthcare region have allowed the area to employ some of its major challenges in particular domains like drug discovery, personal genetics, and disease identification and management. Every time an innovative technical tool comes into the healthcare system, it also faces several challenges. Most of the common issues of artificial intelligence (AI) technique in healthcare system are regulatory compliance requirements, patient and provider adoption, and also lack of data exchange. The Artificial intelligence (AI) has moved from all of these concerns, reducing the areas in which it can accomplish something. The purpose of artificial intelligence (AI) and machine learning (ML) in healthcare system is redesigning the industry and creating what was once impracticable into a real truth. For artificial intelligence (AI)/machine learning (ML) to take its place in the healthcare system, sustained access to appropriate data is necessary to succeed. Artificial intelligence (AI) can be used to analyze and identify patterns in large and complex datasets faster and more precisely than has previously been possible. It can also be used to search the scientific literature for relevant studies and to combine different kinds of data, for example, to aid drug discovery. Artificial intelligence (AI) health apps have the potential to empower people to evaluate their own symptoms and care for themselves when possible. Artificial intelligence (AI) systems that aim to support people with chronic health conditions or disabilities could increase people’s sense of dignity, independence, and quality of life, and enable people who may otherwise have been admitted to care institutions to stay at home for longer. Artificial intelligence (AI) depends on digital data, so inconsistencies in the availability and quality of data restrict the potential of artificial intelligence (AI). Also, significant computing power is required for the analysis of large and complex datasets. Clinical practice often involves complex judgments and abilities that artificial intelligence (AI) currently is unable to replicate, such as appropriate knowledge and the ability to read social cues. With the help of machine learning process, structured data like genetic data, electro physical data (EP), and imaging data are properly investigated. Machine learning makes the information analytical algorithms to extract characteristics from the input data. Input data generally in machine learning algorithms involve with patient’s natures as well as the intermittently apprehension healing effects. A patient’s nature generally includes bottom line data, such as gender, disease history, age, gene expressions, electrophysiological data (EP) test, analytical imaging, idea test results, and medicinal symptoms. Support vector machine was also applied in cancer diagnosis. Even supposing complicated data, machine learning represents the support for artificial intelligence (AI). At this moment in time, an innovative advancement is happening in the subfield of neural networks. This has created notable interest in various domains of healthcare science, in addition to drug analysis and also the area of public health. Deep neural networks can implement in addition to the most exceptional human clinicians in specific diagnostic tasks. Also, artificial intelligence techniques are already promising in healthcare-based apps, which can be performed by any network machine like modern smart mobile phone. Artificial intelligence has the ability to address imperative health challenges, but it is limited due to the unavailability of good health data. Employing artificial intelligence (AI) involves some ethical issues including the probable for artificial intelligence (AI) to make mistaken assessments and then the question of responsibility occurs.
Basically, artificial intelligence (AI) devices are categorized by two main types: the first one is machine learning (ML) category [1], which generally analyses the structured data, for example, electrophysiological data (EP), genetic data, and imaging data. For healthcare applications, the machine learning (ML) processes try to gather patients’ individuality or understand the possibility of the disease effects [2]. The second type of artificial intelligence (AI) device is the natural language processing (NLP) technique [3], which can take out the information from free or unstructured data such as medical observations or health journals to enhance structured health check data. The natural language processing (NLP) processes objects at revolving contents toward the machine-understandable structured records and can then be considered by machine learning (ML) procedures [4]. Figure 1 explains the road plan from medical data making, during natural language processing (NLP) data improvement and machine learning (ML) data investigation, to medical judgment creating. In this figure, the road plan starts and ends with medical activities. As dominant as artificial intelligence (AI) procedures, they can be inspired by medical/healthcare troubles and also be practical to help out the medical performance at the end.
The road plan from generation of medical data, during natural language processing (NLP) data improvement and machine learning (ML) data investigation.
Machine learning (ML) builds the data investigative algorithms to extort characteristics from the data. Inputs to machine learning (ML) algorithms consist of patient ‘characters’ and occasionally therapeutic effects of concern. A patient’s characters generally contain bottom line data, for example, gender, age, disease history, and also disease explicit data, for instance, gene expressions, analytical imaging, electrophysiological data (EP) test, objective test results, medication, and medical symptoms. In addition to the attributes of the patients medical results are frequently composed for medical investigation. These contain syndrome pointers, patients’ endurance periods, and quantitative syndrome stages such as the size of tumor. Here
Representation of (A) unsupervised learning, (B) supervised learning, and (C) semisupervised learning.
There are two major unsupervised learning techniques available such as (i) principal component analysis (PCA) technique and (ii) clustering technique. Principal component analysis is basically for element reduction, mainly while the characteristic is documented in a huge number of elements, such as the number of genes in a genome-mixt connection revise. Principal component analyses (PCA) project the data on a small number of principal component (PC) guidelines, without trailing in excess of information regarding the issues. Occasionally, PCA is used to decrease the element of the data, after which clustering technique is used to fraction the issues. All these fraction issues with related characteristics are gathered together, without applying any result information. This algorithm’s result output helps the cluster tags for the patients throughout maximizing as well as minimizing the parallel of the patients and also involving the clusters. These accepted clustering algorithms contain (i) Gaussian mixture clustering, (ii) K-means clustering, and (iii) hierarchical clustering. Alternatively, supervised learning reflects on the topics’ outcomes in cooperation with their characteristics and goes via a definite training procedure to find out the finest outputs connected through the inputs, which are nearby the standard outcomes. Generally, the formulations of output contrast through the concern outcomes. Such that, the outcome can be the possibility of receiving an exact clinical result, the projected value of a disease stage or the projected endurance time. Evaluated by unsupervised learning and supervised learning, which offers extra clinically applicable results; therefore Artificial Intelligence (AI) relevance in healthcare system most regularly apply supervised learning. Unsupervised learning may be applied as a component of the preprocessing stage to or find out subgroups or decrease dimensionality, which consecutively makes summarizing supervised learning stage more capable. Appropriate methods contain logistic regression, linear regression, decision tree, naïve Bayes, random forest, discriminate analysis, nearest neighbor, neural network, and support vector machine (SVM). Neural network and SVM are the most accepted supervised learning methods in healthcare applications [5]. The mechanisms of neural networks and support vector machine (SVM) techniques process together with relevant examples in the cardiovascular disease, neurological disease, and cancer.
Neural network is basically known as the expansion of linear regression for confining the difficult nonlinear relationships dividing the input parameters and outcome data. In this neural network, the relations involving the input parameters and the outcome are represented throughout the multiple unknown layer grouping of preindividual functional. The aim is to calculate approximately the weights via input data and also the outcome data so that the average error involving the outcome and their calculation is reduced. Here, this technique is described via following some examples. Neural network was used in stroke diagnosis [6], where the input parameters were given as
In this equation,
Neural network diagram.
The instruction’s aim is to find out the weight of
The supporting vector machine is mostly applied for categorizing the topics into two different clusters, where the result
where
Deep learning method is a contemporary expansion of the traditional neural network method. Figure 4 represents deep learning like a neural network with multicovers.
Multilayer neural network.
Rapid growth of current computing allowed deep learning for constructing the neural networks along with huge amount of covers, which is impossible for traditional neural networks. Basically, this technique helps to investigate many critical nonlinear models in the information. One more cause for the recent acceptance of deep learning techniques is owing to the enhancement of the critical and volume of data [15]. Dissimilar to the traditional neural network, this process generally applies more hidden levels in order that the algorithms can handle critical data with different structures [5]. In the healthcare applications, the generally applied deep learning algorithms consist of recurrent neural network, convolution neural network technique, deep neural network, and deep belief network. Convolution neural network is the most accepted one in 2016. The convolution neural network is extended to analyzing the ineptitude of the traditional machine learning algorithms when conducting high dimensional data, that is, data with a huge number of characteristics. Conventionally, the machine learning algorithms are considered to examine data when the number of characteristics is little. The image data are physically high dimensional because each image generally includes thousands of pixels as characteristics. One explanation is to present dimension decrease: primarily preselect an object of pixels as elements and then complete the machine learning algorithms on the ensuing lower dimensional traits. However, heuristic feature selection events may drop the information in the images. Unsupervised learning methods such as clustering or PCA can be applied for data-determined dimension decrease. The convolution neural network was first projected the high-dimensional image investigation [16], where the inputs for convolution neural network are the accurately regulated pixel values on the images. The convolution neural network then transmitted the pixel values in the image throughout weighting in the difficulty layers and variety in the subsampling layers instead. The ultimate output is a recursive purpose of the weighted input values. The weights are skilled to reduce the average error involving the predictions and the outcomes. The performance of convolution neural network has been incorporated in trendy software packages such as Caffe from Berkeley AI Research [17] and Tensor Flow from Google [18]. Recently, the convolution neural network has been effectively executed in the healthcare area to help disease identification. It is used to identify the congenital cataract disease throughout learning the ocular images [19], though it has over 90% accuracy on identification and treatment implication. Convolution neural network was performed to identify skin cancer from clinical images [20]. Convolution neural network is applied to identify referable diabetic retinopathy via the retinal fundus photographs [21]. The specificity and sensitivity of the algorithm are both over 90%, which expressed the usefulness of using the method in the analysis of diabetes. It is importance to declare that in all this type of applications, the presentation of the convolution neural network is competitive beside an experienced physician in the truthfulness for categorizes both usual and disease stages.
Genetic data and EP plus image are all machine-comprehensible, that is why the machine learning (ML) algorithms can be straightly presented after quality control processes or appropriate preprocessing. Though huge extents of medical data are like descriptive content, like a substantial examination, operative notes, and an experimental laboratory reports and release abstracts, these are formless and inconceivable for computer programming. Below this background, natural language processing (NLP) targets removing helpful data from the descriptive text to support the medical conclusion making [3]. A natural language processing (NLP) pipeline includes two main components: (i) classification and (ii) text processing. During text processing, the natural language processing (NLP) recognizes a sequence of disease-appropriate keywords at clinical remarks related to the past records [22]. After that, keyword subsets are preferred during analyzing their achievements in the arrangement in the normal abnormal cases. The authorized keywords then enter and enhance the controlled information to support medical choice making. The natural language processing pipelines have been developed to help the medical choice making on attentive treatment preparations and monitoring critical effects. For instance, it was showed that establishment of natural language processing, for analyzing the chest X-ray reports would help the antibiotic assistant system to aware physicians for the probable necessitate for anti-infective therapy [23]. Natural language processing was used to mechanically monitor laboratory-based difficult effects. Moreover, the natural language processing pipelines can also assist with disease analysis [24]. A recognized of 14 cerebral aneurysm disease-associated changeable during executing natural language processing (NLP), based on the clinical remarks [25]. Resulting variables are effectively applied for categorizing the common patients and the patients with cerebral problems, with 86% to 95% accuracy rates on the validation and training trials correspondingly. A natural language processing was implemented to extort the peripheral arterial disease-allied keywords from description clinical remarks. The keywords are then applied to categorize the common patients and the patients who have peripheral arterial disease, which reaches over 90% accurate [22].
In spite of few limitations, artificial intelligence (AI) are applied in healthcare system. Researchers mainly focus on the region of major three diseases: cardiovascular disease, nervous system disease, and life-threatening cancer also. In cardiology, [26] explained the prospective uses of the AI system for making a diagnosis of the cardiac diseases with the help of cardiac images. Cardiac stroke is a natural and commonly stirring disease that has an effect on more than 500 million people all around the world. It is the most leading cause of death in world. It has also high medical expenses across the world nearly about US$ 689 billion, which causes serious trouble to patient families [27, 28]. For that reason, research on anticipation and medical treatment for stroke has a great impact. Recently, artificial intelligence (AI) processes have been used in additional and supplementary stroke-connected studies. In stroke-concerned cases, AI procedures help in the three main areas: before time for disease calculation and analysis, healing, and in addition to conclusion forecast and diagnosis assessment. About 85% of the time, stroke is caused by cerebral infarction, that is, thrombus in the vessel. For require of finding pre stroke indication, only some patients could obtain appropriate treatment. A movement-detecting device was developed for predicting early stroke [29]. For model structure resolution, two machine learning algorithms like PCA and genetic fuzzy finite state machine are mainly used. The revealing method is attached with a patient human action detection phase and the starting of the stroke detection phase. Ideally, the typical model is remarkably different from the patient movement, and an attentive model that can detect stroke can stimulate and assess medical action and make it immediately feasible. Correspondingly, a device that is wearable was proposed for gathering data for regular and pathological steps for calculation of stroke [30]. The data can be removed and copied by SVM and unseen Markov models, and this algorithm could suitably organize 91% of information to the exact group. For some identification of the stroke, neuro-imaging processes like CT scan and MRI are also essential for disease estimation. Several studies have attempted to concern machine learning techniques to neuro-imaging data to support with stroke analysis. SVM was used in resting-state functional MRI data, where endophenotypes of motor disability behind stroke were classified and recognized [31]. This algorithm can precisely distinguish patients with a precision of 87.6%. T1-weighted MRI, [32] helps to rearrange the stroke injury. This effect is similar for human-proficient physical injury explanation. Kamnitsas et al. [33] attempted 3D CNN aimed at injury fragmentation in multisculpt brain MRI. It likewise used fully associated provisional casual field representation for ultimate postprocessing of the CNN’s soft segmentation plots. With the help of Gaussian process regression method, stroke anatomical MRI images were analyzed,and also establish the vortex pattern performed well than injury load/area like the expecting elements [34]. Machine learning (ML) techniques are also useful to examine stroke patients with CT scans. A free-floating intraluminal thrombus can be created like injury post stroke, and this is complicated to discriminate by carotid sign in CT imaging. Three machine learning (ML) algorithms were used to categorize two quantitative types: shape analysis with linear classification analysis, SVM, and artificial neural network [35]. Machine learning is also used in expecting and evaluating the presentation for stroke cure. In a critical emergency phase determination, the result of intravenous thrombolysis (tPA) has a sturdy link for the diagnosis per durance rate. With CT scan, SVM can be used for expecting whether the patients by thrombolysis (tPA) cure can build up suggestive intracranial hemorrhage [36]. In SVM, complete brain images were used as input, which acted healthier than traditional radiology-based procedures. For improving the medical result making procedure of thrombolysis (tPA) healing, a stroke treatment model was proposed for investigating perform guiding principle, clinical trials and meta-analysis with Bayesian principle network [37]. The model consisted of 56 different types of variables and 3 decisions aimed at investigating the process for analysis, cure, and effective calculation. An interaction tree was used, where the subgroup investigated suitable thrombolysis (tPA) dosage as per patient individuality, taking into consideration the healing efficacy and the possibility of bleeding [38]. Several issues can influence stroke diagnosis and syndrome mortality. Evaluating with traditional methods, machine learning techniques have returns in progressing calculation activity. To enhance and maintain the medical assessment making procedure, a model was proposed for expecting a three-month healing outcome by examining the physiological considerations for the duration of 48 hours following stroke with logistic degeneration [39]. A database was observed with 107 patient’s medical information through acute anterior stroke and also posterior stroke via intra-arterial therapy [18]. Here, the data were examined through SVM and artificial neural network and achieved calculation accurateness of more than 70%. Machine learning procedures was used to recognize the control effect in brain arterio-venous abnormality satisfied with endo-vascular embolization. Though typical degeneration analysis representation could only reach a 43% precision rate, this technique’s exertion is much enhanced with 97.5% exactness. An optimal algorithm was analyzed to calculate 30 days mortality test and gained additional exact calculation than surviving techniques [40]. Likewise, SVM was used to calculate the stroke mortality via discharge. Additionally, the application of the synthetic alternative oversampling procedure was proposed to decrease the stroke effect calculation prejudice reasoned among class inequality between several datasets. Brain images were examined for calculating the effect of stroke cure. CT scan data were examined through machine learning procedure for estimating the cerebral edema through hemispheric infraction [41]. A random forest was constructed to involuntarily recognize the cerebrospinal fluid (CSF) and examined the changes in the CT scan, and this is more precise and capable compared to the traditional procedures. Functional connectivity was extracted from magnetic resonance imaging (MRI) and practical magnetic resonance imaging (MRI) data, and ridge degeneration and multitasking intellect were also applied for cognitive deficit calculation following stroke [42]. A relationship was examined, which involved injuries extorted from magnetic resonance imaging (MRI) and the cure effect through Gaussian method regression technique [43]. The model was used to calculate the difficulty of cognitive damages during stroke and the way of retrieval in due course. In Arterys Cardio DL process, where artificial intelligence (AI) is help to make available programmed and also changeable ventricle segmentations related on traditional MRI of cardiac images [44]. In nervous system disease, an artificial intelligence (AI) method was developed [45] for repairing the regulation of body movement in quadriplegia patients. Farina et al. experienced the control of the offline man–machine edge, which applies the release timings for the spinal motor neurons for controlling the prosthesis of the upper limb. IBM Watson for the oncology diagnosis can be a consistent AI for cancer diagnosis from start to the end, which was explained by Somashekhar et al. [46] by a double-blinded validation study. A clinical image was examined for recognizing skin cancer subtypes [20]. The applications of these three types’ diseases are not absolutely unpredicted. These three diseases are principal death causes; for that reason, analyzing the stages of the disease before time is vital to avoid worsening of the patients’ health condition. Moreover, quick diagnoses can prospectively reach throughout recovering the analysis measures on electrophysiological (EP) or electronic medical record (EMR), imaging and genetic, and this is the major power of the artificial intelligence (AI) technique. Moreover, apart from the three main diseases, artificial intelligence (AI) system has been used in another disease too: to examine the ocular image data for diagnosing inherited cataract diseases [19]. A referable diabetic retinopathy was detected by the retinal fundus photographs [21].
Artificial intelligence in modern medicine and medial area has been a mostly upcoming hot topic in current years. Although there is wisdom of excessive prospective in the use of artificial intelligence in modern medicine, there are also worries about the defeat of the ‘human touch’ in such an important and person-motivated work. Artificial intelligence in modern medicine denotes to the practice of artificial intelligence tools and programmed procedures in the identification and cure of patients who need care. At the same time as analysis and cure may appear like modest phases, there are numerous other circumstantial procedures that come to pass in demand for a patient designate properly taken to attention, such as:
Collecting information data from patient discussions and checks
Treating and examining outcomes of result
Applying several causes of information data to derive an exact identification
Defining an applicable cure technique
Arranging and controlling the selected cure technique
Patient observing
Rehabilitation, continuation arrangements
Disagreement for enlarged use of artificial intelligence in modern medicine is that reasonably a various of the beyond could be programmed—computerization often means jobs are finished more swiftly, and it also help to frees up the time of a medical expert’s when they could be acting other responsibilities, which cannot be computerized, and hence are appreciated as a more cherished practice of human wealth. For instance, technology application has improved in all regions of daily life. Now, there are unbelievable volumes of tools and robotics in association with modern medicine; all medical information is digitized, online appointments can be arranged, and with the help of different healthcare apps in smartphone, it can be easy to find out nearest medical clinics or any health centers. Artificial intelligence is already being used in healthcare modern medicine nowadays. As a medical assessment support system, DXplain [47] is an artificial intelligence system that can help to perform on a set of medical outcomes like symptoms, marks, laboratory files, etc. to make a hierarchical list of identification that can describe the medical indices. Germwatcher [48] is another artificial intelligent system that is considered to notice, and examine taints in needy patients. In medical robotic surgical technology, the “da Vinci robotic surgical system” [49] with defined movement, robotic arms, and magnetized visualization permits surgeons to perform surgery that is not possible through an exclusively manual method. The probable for enlarged artificial intelligence practice in modern medicine is not objective in a decrease of physical jobs and reducing doctor’s time, growing proficiency and output-it also offers the prospect for healthcare system to change to further accuracy of modern medicine.
Smart biomedical and medical packaging with the application of polymers is a generally and rapidly growing area of interest for academia and industries. Among a variety of polymers such as alginate, many uses have been created such as in biomedical field, medicine, packaging, and food sector [50]. For example, in modern drug delivery systems, a mesh completed of nanofibers created by the electro-spinning process is highly desired. Electro-spinning for biomedicine is based on the application of natural substances and biopolymers, along with the mixture of drugs such as sulfisoxazole, naproxen, and essential oils with antibacterial properties such as eugenol and tocopherol. In recent times, there has been an enormous thrust in the usage of biopolymers for a number of applications, especially in the biomedical and also in pharmaceutical areas [51, 52]. The functional effectiveness of the biopolymer molecules depends on the physicochemical properties, structural features, and composition [53]. It is feasible to rationally design the structure and composition of the biopolymer to gain suitable useful features [54]. The internal structure of the polymer molecule determines many functional characteristics, for example permeability, integrity, and chargeability [55]. The strength of the biopolymer particles and their summative capability is influenced by the electrical characteristics. Molecules of biopolymers and their electrical properties influence the contact with other molecules present in the neighboring environment. Alginate is one of the most popular natural biopolymers and intensely studied [56, 57]. It is an anionic biopolymer consisting of units of guluronic acid and mannuronic acid in uneven blocks [58]. Guluronic acid and mannuronic acid are linked by glycosidic linkages [59, 60], whereas the guluronic acid forms α bonds (1 → 4) and β (1 → 4) bonds with mannuronic acid [61]. The stiffness of molecular chains is ensured by the rigid and bent conformations of guluronic acid [62]. Hecth et al. have recently discussed their study on the characterization of calcium alginate and sodium alginate with particular importance on their structure [63]. Different applications and properties of alginate have also been examined. Alginate characteristics used biomedical especially in biomedicine can be formed by adjusting the accessibility of their hydroxyl and carboxyl groups [64]. It influences the characteristics of alginates, such as hydrophobicity, solubility, and their biological activity [65]. Alginate hydrogels were formed by cross-linking polymer chains [66]. The chemical properties of alginate hydrogels were found to depend on the cross-linking density of the chain [67]. The cellular viability of MG-63 osteosarcoma cells was improved by blending alginate bioink solution with N-acetyl cysteine (NAC) [68]. One of the techniques used in the design of alginate hydrogels is intermolecular cross-linking, wherein only the alginate guluronan groups react with the divalent cation, most frequently the calcium used to gel the alginate [69].
Artificial intelligence (AI) in healthcare offered a variety of healthcare information results that artificial intelligence (AI) has examined and reviewed the most important types of diseases that artificial intelligence (AI) has arranged. Machine learning (ML) and natural language processing are two major groups of artificial intelligence (AI) devices. For machine learning (ML) process, two most accepted traditional methods are available, that is, neural network and SVM. A typical artificial intelligence (AI) system must have the machine learning (ML) component that can help for conducting the structured data such as EP data, images, and genetic data and another natural language processing (NLP) module for the deduction of unstructured works. The complicated algorithm requires to be taught during the healthcare results previous to the system which can support the physicians for the disease analysis and plans which should be required for treatment. This technique focuses on how computer-oriented assessment methods, within the same roof as artificial intelligence (AI), can help in improving health and clinical area. Even though sophisticated information and machine learning present the base for artificial intelligence (AI), at present, there are revolutionary progresses happening in the subfield of neural networks. This has produced remarkable enthusiasm in several fields of healthcare science, as well as drug analysis and public health. Deep neural networks can execute as well as the most excellent human clinicians in definite diagnostic responsibilities. Additionally, artificial intelligence (AI) tools are already emerging in health-based apps, which can be engaged in handheld, network machines such as smart mobile phones. The major obstructions to be defeated in building health and healthcare data information are the space between digital data and human cognition. Data information regarding an entity patient is mostly gained in forms designed to be available to healthcare personnel. Typical data may consist of MRI or X-ray or ultrasound pictures of the patient, visual records of lung or heart function differing with time, or verbal similes of the patient as seen by the medical personnel. Alternatively, when data are accumulated in data information process and applied, in health research or to expand treatment procedures, it is regularly concentrated to statistical information that is mainly digital. The transfer of analog input into digital output is an oppressive task and may result in a defeat of important information, which would have been cooperative to the consumer.
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