The physicochemical and transport properties  of a series of perfluorinated membranes with an inert fluoropolymer content from 0 to 40%, obtained by polymer solution casting, were studied. Based on the analysis of the parameters of the extended three-wire model, the effect of an inert component on the path of electric current flow in the membrane and its selectivity were estimated. The mechanical characteristics of the membranes, such as Young’s modulus, yield strength, tensile strength, and relative elongation, were determined from the dynamometric curves. The optimal amount of the inert polymer in the perfluorinated membrane was found to be 20%, which does not significantly affect its structure and electrotransport properties but increases the elasticity of the obtained samples. Therefore, the perfluorinated membrane with 20% of inert fluoropolymer is promising for its application in redox flow batteries and direct methanol fuel cells. The expansion of alternative energy applications is closely related to the development of redox flow batteries (RFB) with aqueous and non-aqueous electrolytes, which are widely recognized as being easy for scale up and suitable for large-scale energy storage applications 10 kW–10 MW). The total energy output depends on the volume of the reservoirs ,3. There kW–10 MWAnother method is combining the charged polymer solution with a modifying agent and subsequent membrane casting ,9,11,14.The membrane modifying permits researchers to solve the problem of improving some characteristics of the material while slightly reducing others. The introduction of any modifier affects the key characteristics of membranes for redox flow batteries, such as ion-exchange capacity, water content, osmotic permeability, conductivity, diffusion permeability to electrolytes and non-electrolytes and mechanical strength, as well as electroosmotic permeability, since according to , non-eleDuring RFB operation, the membrane is subjected to various influences, for example, leading to the degradation of polymer chains and the appearance of cavities and cracks in their structure. So, during operation of both batteries and hydrogen fuel cells, the membrane is subjected to highly active oxidants . In thisLF-4SK solution of perfluorosulfonic acid (9 wt.%) in DMF , poly(vinylidene fluoride co-hexafluoropropylene) F-26.The membrane samples with different content of inert polymer were obtained by mixing solutions of F-26 in DMF and LF-4SK with different ratios. A solution of F-26 in DMF was prepared by ultrasonic dissolution of F-26 powder in DMF. Mixed LF-4SK and F-26 solution was also mixed under the action of ultrasound. Afterwards, the mixed solution was placed into the mold and dried at 60 °C for 2 h and at room temperature for 24 h. The membrane was then extracted from the mold and immersed in 2 M solution of NaCl. The series of the membranes with varied content (ω) of F-26 in the range 0–40 wt.% was prepared. The obtained membranes were marked MX, where X was the weight fraction of F-26 in dry membrane. The main physicochemical characteristics  Q), water content W, specific water content nm) were obtained according to standard methods described in detail in  and are m was calculated by the formulaRm was membrane resistance, and l and S were the thickness and working area of the membrane, correspondingly.The resistance of the samples was determined by the two-electrode mercury-contact method as an active part of the impedance of the cell with membrane. The conductivity of the membrane κThe diffusion permeability was measured in a two-compartment cell by diffusion of electrolyte solution through the membrane to distilled water. The solution in both chambers was intensively stirred to eliminate the influence of diffusion boundary layers. The changes in solution resistance in the chamber filled with distilled water was used to determine the kinetic increase in solution concentration.jm) and integral coefficient of membrane diffusion permeability (Pm) were calculated by the formulasC was the electrolyte concentration, V was the chamber volume, K was the cell constant, R was solution resistance in the chamber filled with distilled water, and t was the time. The diffusion flux through the membrane  were measured in cylindrical four-chamber cells, which contained two electrode and two membrane chambers. The electrode chambers were supplied with platinum polarizing electrodes with 7.1 cmThe dynamic mechanical analysis of air-dry samples was performed by a tension testing machine with an extension rate of 5 mm/min at room temperature and humidity. The working width and length of the sample were 4.2 and 10 mm.The equilibrium  and transport  properties for experimental membrane samples are investigated in the present work. As can be seen from the data presented in The concentration dependencies of membrane diffusion permeability in NaCl solutions are presented in f1, f2 are volume fractions of gel and intergel phase, correspondingly, f1 + f2 = 1; α is the structural parameter reflecting the spatial orientation of conducting phases inside the material: α = 1 corresponds to the parallel orientation of conducting phases towards the transport direction, α = −1–to the serial one; and κm, κiso, κsol are conductivities of membrane, its gel phase and equilibrium solution, respectively. According to ETWM, the current is transferred by tree parallel channels: successively through gel phase and solution , parameters d and e correspond to fractions of solution and gel in channel a (d + e = 1).The concentration conductivity curves are used for calculation of the transport-structural parameters of the extended three-wire model (ETWM) for obtained membranes. This model is based on the microheterogeneous structure of the ion-exchange material and the theory of generalized conductivity of structurally heterogeneous media. According to the model, the ion-exchange material consists of a gel phase, which includes all polymer components and bond water, and an intergel phase that is an inner equilibrium solution. In the gel phase, the current is transferred by counter-ions, but in the intergel phase it is transferred by both counter- and co-ions. The resulting conductivity of such a two-phase system could be calculated by the formula (4)κm=[fion a in , only thiso), structural  and geometrical parameters Km and Kd are conductivities of membrane and its gel phase divided to solution conductivity, correspondingly, t– is co-ion transport number in electrolyte solution. This assumption is adequate in diluted solutions with concentration C < 1 mol/L, where the Donnan electrolyte sorption could be neglected. The ETWM permits the determination of structural  and geometrical  parameters simultaneously, which discovers a wide opportunity in discussion of ion-exchange membranes conductivity. Thus, this model is used for the simplified parametrization of the ion-exchange membrane.The main equations of ETWM combines the transport (κf1 = 1 for the M40 sample. This correlates with a decrease in the fraction of inner free solution due to lower swelling of the membranes. The conductivities of the membrane phase (κiso) also decreases with IEC, but other α, a, b, c, d and e parameters vary stepwise. These parameters are almost constant in the F-26 content range from 0 to 30%, but for the M40 sample they change considerably due to the reorganization of its structure. Thus, the current transfer through mixed and solution channels (a and c parameters) decreases. The value of α parameter is 2 times greater for the M40 membrane than for other samples that point to the domination of parallel orientation of conducting phases, while for other samples the orientation of conducting phases is more chaotic. The similar effect of stepwise change in model parameters was observed earlier for heterogeneous cation exchange membranes with variable content of inert polyethylene binders when the binder content was 40% [ was 40% .The membrane in RFB plays an important role in transporting the counter-ions from one chamber to another, and to retard the co-ions flux. In contrast to the PEMFC, where there is only one type of cations and water, in RFB the membrane contacts the solutions, containing both counter- and co-ions, so the selectivity of the membrane is important. The calculated transport-structural parameters are used to estimate the counter-ion transport numbers Thus, on the base of model parameter analysis it could be concluded that the introduction of 20% inert polymer into perfluorinated membrane does not affect the structure and selectivity of the membrane.f1, f2 and κiso, which could be calculated from the bi-logarithmic presentation of membrane conductivity dependence on solution conductivity according to formulaThe concentration dependencies of conductivity and diffusion permeability  are procβ and G, characterizing the shape of concentration profile inside the membrane and the diffusion transport of co-ions in the membrane gel phase. These parameters β, α and G could be calculated according to the formulasD is the diffusion coefficient of the electrolyte in solution.The MM also permits to calculate parameters f2, κiso and α are close to the calculated ones according to ETWM.β and G, which characterize the diffusion transfer in membranes. Thus, the value of the β parameter, corresponding to the shape of concentration profile inside the membrane, is almost independent on the F-26 portion for samples M0, M10, and M20, while the inert polymer portion exceeds 20% of the decrease in β value,, and it equals 1 for the M40 membrane due to the significant decrease in the IEC of the sample. The G parameter includes the Donnan constant, the IEC of the gel phase and the diffusion coefficient of co-ions in the gel phase [G parameter magnitude also decreases with the growth of F-26 content inside the M0–M30 membranes. The manifestation of the effect of obstacles due to the introduction of an inert polymer is also not excluded. For the M40 sample this parameter could not be calculated due to the model restrictions.This fact points to the agreement of the two models’ results. It is worth noting the parameters el phase  and descDuring the employment the membranes in effective current generation regimes the diffusion limited current on the membrane could be achieved. Therefore, the main purpose of CV-curve measurement was to control the changes in limiting the current density and Ohmic slope after modification. The CV-curves for samples with different portions of inert polymers are experimentally investigated. The investigated membranes are prepared by the casting method, so the membrane surfaces are faced to glass and air during drying. In  the authilim) and potential of transition to limiting state (ΔElim), as well as slopes of Ohmic (tgohm) and overlimiting (tgover) sections are almost equal within the experimental error. Some change in slope of limiting plateau indicates the uneven achievement of limiting state on the membrane surface.The CV-curves for investigated membranes are presented in lim) and lowest length of limiting plateau  are also observed for M20 membrane. This could indicate the highest development of electroconvection in this sample due to the optimal ratio of conductive and non-conductive areas on the membrane surface among the studied samples.The analysis of limiting plateau length (Δ) has shown that its dependence on inert polymer portion inside the membrane has minimum for the M20 sample. The introduction of inert polymer into the homogeneous one leads to an increase in electric inhomogeneity of the membrane surface and the intensification of electroconvection, so the overlimiting state is achieved for lower value of potential drop on the membrane . The higE), yield strength, tensile strength and relative elongation (ε). The dynamometric parameters are presented in The dynamometric curves for membranes are shown in One can see, that the increase in F-26 content in the membrane is accompanied by the reduction of Young’s modulus and tensile strength values by 30% and yield strength by 50%. At the same time, the relative elongation increases by a factor of three times. It should be noted that elongation dependence on the fraction of inert polymer is linear for M0–M40 membranes, so F-26 acts as plasticizer in the obtained polymer mixture and facilitates the mutual slide of polymer chains. Young’s modulus, yield strength, tensile strength and relative elongation values dramatically decrease with the growth of membrane water uptake ,35 due tThe remaining characteristics change nonmonotonically: they decrease in the range of ω from 0 to 20%, but for samples M20 and M40 they have close values. The growth of membrane elasticity is a positive effect for their application in electricity chemical sources, since formation of the membrane electrode assembly for both fuel cells and redox flow batteries is performed by the pressing of electrodes to the membrane surface. Thus, higher elasticity of the membrane could provide the better contact between electrodes and membranes in the stack. According to , the mecA series of composite membranes with varying content of inert component from 0 to 40 wt.% on a dry membrane was obtained by casting from solutions of perfluorosulfonic acid and poly(vinylidene fluoride co-hexafluoropropylene) in dimethylformamide. It is shown that with an increase in the mass fraction of an inert fluoropolymer, the ion-exchange capacity and hydration characteristics of membranes naturally decrease. A simultaneous decrease in the diffusion permeability of membranes for a sodium chloride solution indicates a possible decrease in the crossover of charged and uncharged electrochemical particles through the membrane during its operation in a fuel cell or redox flow battery.The concentration dependence of the specific electrical conductivity of membranes in sodium chloride solutions was studied by the mercury-contact method, and a decrease in electrical conductivity by a factor of six with an increase in the proportion of the inert component from 0 to 40% was shown. In this case, the introduction of an inert fluoropolymer into the membrane has practically no effect on the limiting diffusion current and other parameters of the current-voltage curve.Within the framework of the extended three-wire model of membrane conductivity, the parameters characterizing the current paths in the membrane were determined which permitted the estimation of the sodium counter-ion transport numbers in the membranes. It was is shown that a significant change in the structure of membranes is observed when an inert polymer exceeds 30%. In this case, the paths of current flow through the membrane are reorganized, and its selectivity decreases.Based on the dynamometric analysis, the effect of an inert fluoropolymer on the mechanical characteristics of perfluorinated membranes was determined. With an increase in the proportion of the inert component from 0 to 40%, a decrease in the value of Young’s modulus and tensile strength by about 30% and yield strength by 50% was found. In this case, the relative elongation linearly increases by a factor of three, which indicates the plasticizing effect of the inert fluoropolymer.Summarizing the information on the effect of the proportion of the inert component on the physicochemical and electrotransport properties of perfluorinated membranes, it can be concluded that the optimal amount of the inert polymer F-26 in the perfluorinated membrane is 20%. This does not significantly affect the structure and electrotransport properties of the membrane, but increases its elasticity and reduces diffusion permeability, which is important when such materials are used in electric current generation devices.


Reducing or eliminating cracks caused by shrinkage of cementitious materials remains a daunting challenge for construction engineers. Drying shrinkage and autogenous shrinkage are the main shrinkage types in the service process of cement-based materials, which have a great impact on engineering applications. If cracks in concrete generate by drying or autogenous shrinkage, the mechanical properties, water resistance and durability of concrete will be also affected. It is an effective method to use chemical admixtures to inhibit the shrinkage of cement-based materials. Polycarboxylate plasticizer (PCE) is an important chemical admixture in cement-based materials and is widely used in practical engineering. It can bring great value by reducing the shrinkage effect through molecular design. Through our innovative design, a series of shrinkage-reducing polycarboxylate superplasticizers (SRPs) were synthesized, their molecular structures were confirmed by Fourier transform infrared spectroscopy (FTIR) and their molecular properties were determined by gel permeation chromatography (GPC). Furthermore, the shrinkage performances at different ages of the mortars containing the synthesized SRPs with different structures were systematically evaluated. The results showed that compared with the blank sample, the dry shrinkage rate and free shrinkage rate of the mortars containing SRP decreased by over 20% and 15%, respectively. Additionally, the shrinkage rates of the mortars containing SRP were significantly lower than that of the mortar containing conventional PCE, and moreover, the water-reducing performance was improved compared to conventional PCE. Based on the experimental results of surface tension and evaporation rate of different SRP solutions, the mechanism of the shrinkage-reducing effect was probed, as expected to provide guidance for the design and development of new shrinkage-reducing admixtures. The shrinkage of cement-based materials has been the focus of engineers and academics. Shrinkage is an inherent characteristic of cement-based materials, taking place both in the plastic and hardened stages. When concrete is used in building structures, it will be restricted by many factors, such as steel or adjacent structures. Under both the influence of the shrinkage process and external constraints, the concrete matrix usually develops harmful cracking ,2,3. DueChemical admixture, as an indispensable component of modern concrete, is widely used in construction engineering. Superplasticizer is a widely used and studied type of chemical admixture and it is usually used to reduce water consumption and adjust the workability of concrete ,9,10. ThIn the preparation process of concrete, if shrinking-reducing agents (SRAs) are added to the slurry according to the actual requirements to inhibit the shrinkage of concrete, it will increase the complexity of the preparation process and the problem of compatibility between other admixtures and SRAs, which may be detrimental to the mechanical properties of the concrete ,18,19. IIn this study, a series of SRPs with different molecular structures were successfully designed and synthesized by using new monomers and then their molecular structures and molecular weights were characterized. Additionally, these synthesized SRPs with different molecular structures were applied to test the fluidity properties of cement pastes and the shrinkage properties of mortars at different ages. The mechanism of the shrinkage reduction was probed by the evaluations of surface tension and evaporation rate of solution with SRPs. The aim of this study is to clarify the influence of molecular structures of SRP on shrinkage performances of cement-based materials, providing a direction for designing the molecular structure of SRP to further improve shrinkage-reducing effectiveness.3 and a bulk density of 1460 kg/m3.Acrylic acid (AA), maleic anhydride (MA), mercaptoacetic acid (TGA), ammonium persulfate (APS), p-toluenesulfonic acid (p-TsOH) and toluene  were obtained from Tianjin Guangfu Fine Chemical Research Institute . Isopentenol polyoxyethylene ether macromonomer (TPEG) was provided by Liaoning Oxiranchem, Inc . Reference cement P·I·42.5 was supplied by China Building Materials Research Institute , and its chemical and mineral compositions are given in Shrinkage-reducing monomers were synthesized by the esterification of carboxylic acid and functional alcohols under the condition of catalyst and heating. The difference between functional alcohols is that the structural content of EO and PO in the molecule is different. The synthesized shrinkage-reducing monomers were identified as SRM-1, SRM-2 and SRM-3. Both SRM-1 and SRM-3 contained the structure of acrylic acid but the functional alcohol structure was different, while SRM-2 contained the structure of maleic acid and the functional alcohol structure, which was the same as that of SRM-3.0 is the initial acid value; Ht is the acid value of the system at t min; and Htheo is the theoretical acid value in which an alcohol-based compound is completely esterified.During the synthesis of shrinkage-reducing monomers, unsaturated carboxylic acids and alcohol-based compounds were added to the flask equipped with the condenser. In this study, p-TsOH was used as catalyst in the esterification reaction to reduce the by-products. Since the esterification reaction is reversible, it was also necessary to add toluene, a low boiling solvent, and form an azeotrope to remove the water, so that the esterification reaction was carried out in a positive direction. In order to ensure the esterification rate was as high as possible, the reaction temperature was controlled at 100–130 °C, and the reaction time was about 4–6 h. During the reaction, some solution was taken from the flask and the esterification rate of the reactants in the system was determined by titration. When the esterification rate was stable to equilibrium, the reaction was terminated. The calculation of the esterification rate is as shown in Formula (1). After the reaction, the product was cooled and transferred to the storage container:The shrinkage-reducing monomer obtained from −1.The samples of SRP were placed in a drying oven (at 105 °C for over 2 h) to remove the free water to prevent hydroxyl groups in water molecules from affecting the sample spectrum, and the solid SRPs were then mixed with potassium bromide and pressed as mixed powder into a transparent disk by tablet press. FTIR spectra of samples were conducted on VERTEX 70 Fourier transform infrared spectrometer . The measurement range of wavenumber was from 500 to 4000 cmn), weight average molecular weight (Mw) and polydispersity index (PDI) of specimens were determined on a Waters PL-GPC50 . Completely dried solid samples of PCE or SRP were dissolved in sodium nitrate aqueous solution whose concentration was 0.1 mol/L, and the concentration of polymer in the solution was controlled within the range of 1–3 g/L. GPC equipped with a PL aquaqel-OH MIXED 8 μm chromatographic column, laser light scattering (LS) and refractive index (RI) detectors, was used to characterize the molecular weight of polymers. The eluent was also aqueous solution of sodium nitrate with an injection volume of 100 μL and a flow rate of 1.0 mL/min, and the elution time of the sample was 30 min.The number average molecular weight (MThe experiments on the fluidity of cement pastes containing polymer were carried out according to the Chinese standard GB/T 8077-2012. The dosage of admixture was 0.2% by weight of cement and the water–cement ratio (W/C) was 0.29, and the water in the admixture solution should be deducted when the mixing water was weighed. The detailed mix proportion of cement pastes is shown in Shrinkage tests of mortars containing PCE or SRP were conducted according to Chinese standard JC/T 603-2004.The ratio of cement to standard sand in the mortar was 1:2 (mass ratio) and the water consumption was determined by the fluidity of the mortar reaching 130–140 mm. The fluidities of mortars were tested according to Chinese standard GB/T 2419-2016, with the admixture dosage of 0.15% (by weight of cement).The specimens used in the shrinkage tests were demolded (the mold size was 25 mm × 25 mm × 280 mm) after being cured for 24 ± 2 h at a temperature of 20 ± 1 °C and relative humidity of more than 90%. The specimens were cured or dried as required after demolding, and the specimens for dry shrinkage should be cured for 7 d, while the specimens for free shrinkage should be dried directly. Three samples are tested for each composition.s0 and Lsn are the initial length and the n day length of the mortar with PCE or SRP, respectively (mm); LB0 and LBn are the initial length and the n day length of the blank mortar (mm).In the drying shrinkage test, the initial lengths of specimens were measured by comparator and transferred specimens to the drying chamber with a temperature of 20 °C ± 3 °C and relative humidity of 50% ± 4%. Then, the lengths for 7 d, 14 d, 21 d and 28 d were measured and the relative shrinkage reduction rates of mortars with PCE or SRP were calculated according to Formula (2):s0 and lsn are the initial length and the n day length of the mortar with PCE or SRP; respectively (mm); and lB0 and lBn are the initial length and the n day length of the blank mortar (mm).In the free shrinkage test, the initial lengths of specimens were measured by comparator and transferred specimens to the drying chamber immediately after being demolded. Then, the lengths for 2 d, 3 d, 7 d, 14 d, 21 d and 28 d were measured and the relative shrinkage reduction rates of mortars with PCE or SRP were calculated according to Formula (3):The surface tension values of polymer aqueous solution were measured by a BZY-1 Surface or interface tension apparatus produced by Shanghai Hengping Instrument Factory . The concentration of aqueous solution of PCE or SRP was 10 wt%. The platinum plate method was used for the test. When the platinum plate was immersed in the sample solution, it was affected by the surface tension. Until the surface tension of the solution was balanced with the external force provided by the instrument, the platinum plate stopped immersion into the liquid. Then, the balance sensor of the instrument measured the immersion depth and converted it into the surface tension value of the solution.−3 g/cm2·h); m0 and mt are the initial mass of the solution and the mass of the solution for t h, respectively (g); S is the area of the evaporating dish (cm2); and t is the test time of the solution (h).The evaporation rate of polymer solution was calculated based on the mass loss of solution. The aqueous solution of PCE or SRP with a concentration of 10 wt% was placed in an evaporating dish and evaporated in a constant-temperature oven with a constant wind speed of 30 °C. The mass was recorded at 24 h and 48 h, respectively, and the evaporation rate was calculated according to Formula (4):The final esterification rates of the three types of shrinkage-reducing monomers are given in The FTIR spectrum of the SRP is shown in −1, indicating that the sample contains a large number of intramolecular hydrogen bonds, which are formed by the hydroxyls at the end of TPEG side chains. There are two stretching peaks at around 2920 cm−1 and 1465 cm−1, which confirms the existence of -CH and -CH2, respectively. Distinguishingly, a peak appears at around 1131cm−1 corresponding to the ester group, which is the characteristic functional group of SRPs [From the FTIR spectrum, a wide and strong peak appears at around 3430 cm of SRPs ,28,29. Aw of SRP1-3 should be about 4%, 11% and 5% higher than that of PCE, respectively, and thus, from the Mw results of SRP1-3 in w of PCE should be in the range of 53,000–55,500 g/mol. However, it can be seen in The improvement of the fluidity retention ability of cement paste with SRP can be expected and easily understood. In the environment of the high pH of a cement solution, some ester groups of SRPs were hydrolyzed gradually, which produced more anionic charges in the molecules, and these new anionic charges continued to adsorb on the surface of cement particles to retain the dispersion of cement paste. This had been confirmed many times in related studies ,31,32,33The drying shrinkage rates of mortars containing PCE or SRP at different ages are shown in The free shrinkage rates of mortars containing PCE or SRP at different ages are shown in pure water = 72.9 mN/m). This shows that PCE and SRP can significantly reduce the surface tension of the aqueous solution, which is beneficial to the wetting, osmotic adsorption of the surface of cement particles and foaming on the gas–liquid interface [cap is the capillary pressure; γ is the surface tension of solution (mN/m); θ is the contact angle between the solution and the cement matrix; and R is the diameter of the capillary pore.The surface tension values of the PCE and SRP solution are shown in nterface ,36. The nterface ,36, as snterface ,37,38.The molecular structure and molecular weight of the synthesized SRPs were characterized by FTIR and GPC analysis, which confirmed the achievement of the synthesis target and the rationality of the synthesis process;(2)Compared with conventional PCE, SRP showed the improved fluidity of cement paste, and the fluidity retention ability was increased by 37.2%, indicating that partially replacing AA with the shrinkage-reducing monomer could enhance the dispersing capacity;(3)Compared with pure water, the surface tension and evaporation rate (within 48 h) of an SRP aqueous solution can be reduced by 50.5% and 57.4%, respectively. SRP effectively reduced the shrinkage of mortar through the combination of two mechanisms, i.e., reducing the surface tension of cement solution in capillary pores and inhibiting the evaporation of water in the solution. This reduction was very significant, especially for drying shrinkage which decreased by over 20%;(4)The diester structure in SRP molecules showed greater advantages in improving the fluidity of cement paste and reducing the shrinkage of cement-based materials, which was related to the hydrolysis process of ester groups in the molecules and the non-adsorbing small molecule alcohol-based compounds produced by hydrolysis;(5)The new type of SRP developed in this study can be used as a functional material in cement-based materials and has a potential application in concrete engineering requiring low shrinkage, high workability and long fluidity retention.The above research results fully demonstrated the great advantages of SRP and verified the influence of different molecular structures on the shrinkage-reducing effect. These findings are expected to contribute to future research in this field and promote the application of SRP, which also helps to improve the durability and safety of building structures and reduce unnecessary government expenditure and carbon emissions. Future research should concentrate on the adsorption process, hydrolysis kinetics and effects on hydration and pore structure of SRP, which will help to further understand the effects of SRP on various shrinkage processes.


The Middle Latency Auditory Evoked Response may be influenced by the post-auricular muscle reflex which occurs at the same latency of this potential.to evaluate the muscle reflex influence on the middle latency response, identifying the most appropriate place for response recording.Prospective study in which 40 normal hearing individuals, ranging in age from 18 to 40 years old, were assessed by Middle Latency Response with electrodes placed first on the mastoids, and then on the earlobes.significant statistical differences were seen between the values found with electrodes placed on the mastoids and on the earlobes concerning the Na-Pa amplitude in C3/A1, C3/A2 and C4/A2, concerning the Na wave latency in C3/A2 and C4/A2, and concerning the Pa wave latency in C3/A1 and C3/A2. We found a higher occurrence of the post-auricular reflex when the electrode was placed on the mastoids, in all studied modalities.there was post-auricular muscle reflex interference upon the Middle Latency Response obtained when the electrodes were placed on the mastoid, and the most efficient electrode disposition in order to capture and to register more accurately this potential was on the earlobe. The Middle Latency Evoked Auditory Potentials (MLEAP) were initially described in 1958MLEAPs have multiple generators, such as the reticular formation and multisensorial divisions in the thalamusBesides assessing the dysfunctions which compromise the Central Nervous System auditory pathway, this potential can be utilized to establish electrophysiological thresholds in lower frequencies, assess cochlear implants, in perioperative monitoringThe Central Auditory System evaluation, by means of recording middle latency evoked auditory potentials, requires placing the electrodes in pre-determined places. One example of electrode placement which allows for a good response recording and, consequently, a good diagnosis is associated to placing the electrodes in the C4 and C3 positions , Cz (vertex) and on the right and left side mastoids (M2 and M1) or right and left earlobes (A2 and A1)  - C4 and C3 are considered active electrodes and M2, M1, A2 and A1 are considered reference electrodes. This electrode placement allows MLEAP to be registered ipsi and contralaterally to the stimulated ear (in many derivations), allowing for the comparison of wave latency and amplitudes between each hemisphere and the middle line.Head and neck muscle activity impact on the recording of evoked potentials has been described in the literature, and such activity may cause doubts as to the neurogenic origin of these responses. This influence can be seen by means of the Vestibular Evoked Myogenic Potential - VEMP, which is captured with click or tone burst stimuli in intensities higher than 90 dBHL and reflects the activity of the vestibular system triggered by high intensity stimuliA common difficulty found in recording MLEAP is that, when there are high levels of stimulation, many reflexes from scalp muscles happen in the same latency of evoked potentials, between seven and 50 msAmong these reflexes, the post auricular muscle reflex is the one most frequently observed. This happens between 14 and 19 ms after the acoustic stimulus presentation, it is usually recorded by the ipsilateral electrode to the sound and it is created similarly to the stapes muscle reflex, which afferent pathway is the VIII cranial nerve and the efferent is the VII cranial nerve. Having this reflex in this region is associated with muscle tension, thus depending on the patient's head positionThe post-auricular reflex (PAR) activity is usually triggered by a sudden acoustic reflex of high intensity, which could be recorded by means of an electrode placed on the mastoid - and this is considered a myogenic responseIt is likely that the anatomical structures involved in the post-auricular reflex are the cochlear nucleus, the superior olivary complex, the lateral lemniscus nucleus and very likely the inferior colliculusHaving the electrodes placed on the right and left mastoid to record the MLEAP, the presence of this post-auricular reflex can cause an alteration in wave latency and amplitude values - parameters used in the analysis of responses, interfering thus in the final diagnosisLiterature reports that the most efficient location of the electrode to minimize post-auricular reflex recording would be a non-encephalic region in the individual, such as the earlobes, for instanceConsidering such aspects, we see that it is extremely important to study the MLEAP using different electrode locations.Therefore, the present investigation aimed at assessing the interference of myogenic responses on the middle latency evoked auditory potential responses in audiologically normal individuals with different electrode locations (mastoids and earlobes), as well as comparing them among themselves.This study was prospective, and approved by the Ethics Committee of our institution under protocol # 274/06. 20 male individuals and 20 females on the age range between 18 and 40 participated in this study, all with normal hearing.For data collection we used the following equipment: otoscope ; middle ear analyzer ; audiometer ; supra-aural phone ; equipment for electrophysiological evaluation .Before the evaluations, the patient received information about the procedures which would be used for the study and, being in agreement to participate in it, they signed a free and informed consent form.The individuals were initially submitted to an interview, in which data was collected about the presence of risk factors for hearing impairment, complaints of otitis, among other alterations associated with the external and middle ears.Following that, we inspected the external acoustic meatus, aiming at seeing possible obstructions by cerumen. In the audiological evaluation we studied the acoustic immitance, tonal audiometry in the frequencies of 250, 500, 1000, 2000, 3000, 4000, 6000 and 8000 Hz, and vocal audiometry in order to select those individuals with normal hearing. Later on, we performed the BEAP with thin polarity click stimuli, presented to one ear only at 80 dB HL, at a presentation speed of 19.0 stimuli per second, with a total of 2,000 stimuli. The electrodes were positioned on the vertex (Cz) and the right and left mastoids (M1 and M2).•Tonal audiometry: threshold averages of the following frequencies: 500; 1,000 and 2,000 Hz lower than or equal to 25 dB HL•Vocal audiometry: Speech Recognition Threshold (SRT) with responses equal to or up to 10 dB above the mean values of the auditory thresholds of the following frequencies: 500, 1,000 and 2,000 Hz in tonal audiometry•Acoustic immitance values: type A tympanometric curve•BEAP: absolute latencies of waves I, III and V and interpeaks I-III, III-V and I-V within normal ranges, using values proposed by the Evoked Potential User Manual for the BIO-LOGIC equipment in individuals older than 24 months.Considering the tonal audiometry, vocal audiometry, acoustic impedance and BEAP data, normal individuals were those who had:After individual selection, MLEAP recording started with the electrodes placed on the right and left mastoids, as well as on the left and right earlobes. In order to obtain this potential, the skin was cleaned with an abrasive paste, and later on the electrodes were fixed to the individual's skin by means of an electrolytic past and adhesive tape (micropore) in predetermined positions, according to IES 10-20 .The electrodes' impedance values were checked, and they had to be below 5 kOhms. The acoustic stimuli used to trigger the response was the thin polarity click, being monaurally presented at 70 dB HL, at a presentation speed of 1,000 clicks. The test was carried out in an electrically protected environment which was also acoustically isolated.In the first MLEAP evaluation, the electrodes were positioned on the right and left mastoids (M2 and M1) (reference), right and left temporoparietal junctions (C4 and C3) (actives) and on the vertex (Cz).In the second MLEAP evaluation, the electrodes were placed on the right and left earlobes (A2 and A1) (reference), right and left temporoparietal junctions (C4 and C3) (actives) and on the vertex (Cz).After recording MLEAP, the waves obtained were analyzed according to their amplitude and latency duration, as well as the presence or absence of post-auricular reflexes, for each assessment carried out (facing different electrode positioning) . The preAt the end of this study, the data obtained with the electrodes fixed to both positions  was analyzed and compared.MLEAP results were initially analyzed from mean values, medians and standard deviations of the latencies and amplitudes of waves Na, Pa and Nb, for each mode - ipsilateral (C3/A1 and C4/A2) and contralateral (C3/A2 and C4/A1), in the two assessment situation.After this initial analysis, we compared the latency values of waves Na, Pa and Nb and Na-Pa and Pa-Nb amplitudes in the ipsilateral and contralateral modes between the two assessment situations (mastoid X earlobe), for that using the t Student test and a significance level of 5%.Later on we analyzed the occurrence of post-auricular reflexes (presence and absence) in the four positions , both with an electrode on the mastoid as well as on the earlobe, using the Wilcoxon Signed Rank Test with a significance level of 5% in order to check whether or not there was any difference in terms of the presence of these reflexes according to electrode positioning.And finally, we analyzed this post-auricular reflex latency obtaining data related to the mean, median and standard deviation, on the four positions , with the electrode placed on the mastoid and on the earlobe.Initially, we obtained the mean, median and standard deviation for the latency values for waves: Na, Pa, Nb and amplitudes NaPa and PaNb for both ears in the four positions , with electrodes on the mastoids and on the earlobes. Afterwards, we did a comparative study of the means for each variable studied between the different positions of electrodes (mastoid X earlobe)In comparing Na-Pa amplitude values obtained from mastoid and earlobe electrodes, we noticed a statistically significant difference for the following modes C3/A1, C3/A2 and C4/A2 .Table 1CWe did not find statistically significant mean differences in comparing Pa-Nb amplitudes obtained from the electrodes on the mastoid and on the earlobe for any of the modes studied .Table 2CComparing the Na wave latency in mastoid and earlobe electrodes, we found a statistically significant difference for modes C3/A2 and C4/A2 .Table 3CWe found a statistically significant difference when we compared Pa wave latencies obtained from mastoid and earlobe electrodes for modes C3/A1 and C3/A2 .Table 4CIn comparing wave Nb latencies obtained from mastoid and earlobe electrodes, there was no statistically significant difference in any of the modes analyzed .Table 5CBellow, we show a study on the presence and absence of post-auricular reflex (PAR) on the four positions , with electrodes on the mastoid and on the earlobe.In studying the post-auricular reflex for the different electrode positions, we noticed a statistically significant difference for all the modes analyzed .Table 6PAfter identifying the presence of a post-auricular reflex, we obtained the mean, median and standard deviation for the latency identifying the post-auricular reflex, we obtained the mean, median and standard deviation of the latency values for this reflex for both ears on the four positions , with the electrode placed on the mastoid and earlobe; however, it was not possible to do a comparative statistical analysis of the mean values between the mastoid and earlobe because of the fact that only one ear had a post-auricular reflex with the electrode placed on the lobe .Table 7PIn studying the mean latency values of waves Na, Pa, Nb and Na-Pa and Pa-Nb amplitude on the four modes , both with the electrode placed on the mastoid as with it placed on the earlobe, it was observed that these values were higher when the electrodes were placed on the mastoid, except for the Pa-Nb amplitude on mode C4/A1, having that this value was higher with the electrode placed on the earlobe , Table 5Studying the specialized literature, we found some papers discussing the possibility of changing the latency and amplitude values of MLEAP waves considering different electrode positions, using the presence of PAR with electrodes placed on the mastoidResults show that in comparing the mean values of latencies and amplitudes of MLEAPs, statistically significant values were found in comparing Na-Pa amplitude values for modes C3/A1, C3/A2 and C4/A2 , in compAlthough literature reports the importance of MLEAP as a complementary exam to subjective tests10 and the existence of studies attempting to improve the objectiveness of the auditory function evaluation in patients with hearing lossHowever, the data allows us to infer that the parameters which suffer the worst variation facing different electrode positions are the Na-Pa amplitude and Na and Pa wave latencies, which correspond to the first MLEAP waves, in other words, the Pa-Nb amplitude parameters  and Nb wSuch findings can be justified by the fact that the first waves are closer to the eventual PAR which can occur and be recorded with electrodes on the mastoid, interfering with the latency and amplitude values of the first MLEAP waves, and not on the latency and amplitude values of later waves.In investigating the presence and absence of PAR in the four positions , both in the mastoid electrode as well as that on the lobe, we noticed a greater occurrence of PAR with the electrode placed on the mastoid, on the four modes, when compared to the electrode placed on the earlobe .Numerous studies in the literature show a greater likelihood of PAR occurrence and recording as one places the electrode on the mastoid, having seen that in this position we can record many reflexes coming from the scalp muscles, PAR among themIn the present investigation we noticed that mean PAR latency values with the electrode placed on the mastoid were around 14 ms, in the four modes assessed . These dComparing the data obtained with the variation in electrode positioning for MLEAP recording, we can see that when placed on the mastoids, we found higher values for Na and Pa waves latency and Na-Pa amplitude, as well as a greater PAR occurrence.We can conclude that the presence of PAR changes latency and amplitude values for the first MLEAP waves, thus the importance of standardizing electrode positioning to record such potentials.We recommend that when using MLEAP, the electrodes should be placed on the right and left earlobes, thus reducing PAR recording and, consequently, producing more reliable results.


While deficiency of C1s is associated with early-onset systemic lupus erythematosus and increased susceptibility to bacteria infections, the gain-of- function variants of C1r and C1s may lead to periodontal Ehlers Danlos syndrome. As C1s is activated under various pathological conditions and associated with inflammation, autoimmunity, and cancer development, it is becoming an informative biomarker for the diagnosis and treatment of a variety of diseases. Thus, more sensitive and convenient methods for assessing the level as well as activity of C1s in clinic samples are highly desirable. Meanwhile, a number of small molecules, peptides, and monoclonal antibodies targeting C1s have been developed. Some of them are being evaluated in clinical trials and one of the antibodies has been approved by US FDA for the treatment of cold agglutinin disease, an autoimmune hemolytic anemia. In this review, we will summarize the biological properties of C1s, its association with development and diagnosis of diseases, and recent progress in developing drugs targeting C1s. These progress illustrate that the C1s molecule is an effective biomarker and promising drug target.The molecules of the complement system connect the effectors of innate and adaptive immunity and play critical roles in maintaining homeostasis. Among them, the C1 complex, composed of C1q, C1r, and C1s (C1qr The complement system consists of more than 30 proteins found in soluble form or attached to cell membranes. Their biological functions include cell lysis, opsonization, degranulation of mast cells and basophils, activation of B lymphocytes, and clearance of immune complex and apoptotic cells. With its components mostly existing as precursor zymogens in the plasma, the system can be activated through the classical pathway (CP), alternative pathway (AP), and the lectin pathway (LP). Of note, the CP is typically activated by the immune complex formed by specific antibodies and antigens such as microbes, allo- and auto-antigens. Through the activation, the antibodies promote the formation of membrane attack complex that often lyses target cell directly, generate complement component-mediated opsonization, and augment inflammatory response. Thus, the classical pathway activation of the complement is involved in the initiation, progression, and prognosis of many bacterial and viral infections-induced responses, autoimmune diseases, and cancers –5. Conve2s2). In the classical pathway, antibodies complexed with antigens bind C1q and change its conformation, leading to activation of the protease activity of C1r, which in turn cleaves and activates C1s. C1q can also be activated through binding to C-reactive protein and the surface of pathogens , contains 12 exons and encodes a precursor C1s protein of 688 amino acids  from the cell surface and hydrolyzes 2 microglobin, affecting T cell-mediated immune response , 18. It The critical roles of the complement system and C1s in maintaining homeostasis make their dysfunction associating with a variety of disease. While mutation of C1s is associated with rare genetic diseases and susceptibility to infections and autoimmune disorders, ongoing studies have indicated that the aberrant activation of C1s contributes to the development of autoimmune and infectious diseases and cancer, and serves as an informative biomarker and therapeutic target , an autoimmune disease characterized by auto-antibody against multiple tissues and organs . The likvia an extensive interface encompassing the N-terminal regions of both proteins  is a rare disorder characterized mostly by early-onset periodontitis. It has been shown that this subtype of Ehlers Danlos syndrome is caused by mutations in the genes of C1r and C1s . The mutproteins . TherefoAs an important system involved both in innate and adaptive immunity, the aberrant activation of the complement is present in many autoimmune diseases, including cold agglutinin disease (CAD), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE) , 35. CADRheumatoid arthritis (RA) is an autoimmune disease of the joints, which results in inflammation and thickening of the joint capsule, and loss of underlying cartilage and bone. C1s activation has been found in the degenerative osteoarthrotic cartilage, but not in normal articular cartilage. The activated C1s was mostly restricted to the severely degraded part of cartilage in osteoarthritis, indicating the participation of C1s in the inflammation and the destroy of cartilage and bone . It has Paradoxically, the roles of C1s in SLE is complicated. While C1r and C1s deficiency predisposes to lupus –44, C1s Given the close association of complement with immunity, it is not unexpected that activation of C1 is associated with the development, progression, metastasis, and treatment of a variety of cancers , 51. In via forming transmembrane pores . In a study with aortic endothelial cells, it was demonstrated that the activation of the complement by anti-MHC antibodies facilitated the recruitment of monocytes. The anti-C1s antibodies TNT003 and TNT009 inhibited the antibody-induced complement activation and blocked C3d deposition  in solid organ transplantation is caused by donor-specific antibodies. It is characterized by the activation of the complement system and infiltration of macrophages . All patients remained transfusion free while receiving sutimlimab, and there were no treatment-related serious adverse events. In another trial with sutimlimab, 13 out of 24 (54%) CAD patients reached the primary end point. Of the 11 patients that did not meet the predefined criteria, 6 of them showed a therapeutic response , chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), cold agglutinin disease (CAD), thrombocytopenic purpura, and autoimmune hemolysis Table 1.response . These aresponse . Of noteVarious methods have been developed to detect the complement in clinical samples.Traditionally, the total complement activity of CP is quantified by hemolysis assay in serum, based on the dilution achieving 50% of hemolysis (CH50). Functional ELISAs using coated IgM and colorimetric substrate have also been developed to detect the activity of CP in serum , 88. HowThe level of the total C1s protein can be detected by bilateral diffusion , ELISA , gelatinThe critical roles of the complement system in innate and acquired immunity make the activation of C1s an informative biomarker for a variety of diseases, particularly the inflammatory, autoimmune diseases and cancers. With the success of specific antibody preparation, C1s has been validated as an effective target of therapeutic intervention. It is conceivable that more small molecular, peptide/protein, and antibodies targeting C1s for the treatment of caner, autoimmune, and infectious diseases will be developed. Thus, the complement component is a unique target for both diagnostic and therapeutic actions. Further, ongoing clinical trials and the emerging of novel therapeutics against C1s will likely bring new and better drugs into clinic in the not-so-distant future.It has been shown recently that the expression of C1s and another two other genes is associated with Age-related macular degeneration (AMD), a progressive neurodegenerative disease of the central retina and a leading cause of vision loss in older adults worldwide . MechaniJY and SX designed the project, JY wrote and SX revised the manuscript, PY and YY participated in the project and revised the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Medical Innovation Team Foundation of Taizhou People’s Hospital (No. CXTDB201904), Research fund of the “333 project” of Jiangsu Province (No. BRA2020190), 2020 Taizhou People’s Hospital Mandatory Project (No. ZL202022) and the National Natural Science Foundation of China .We apologize for those colleagues whose important works were not cited due to limited space.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Alzheimer’s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas , the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer’s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50. Alzheimer’s disease (AD) is an insidious and slowly progressive neurodegenerative disease, which is mainly found in the elderly population over 60 years of age and is clinically manifested as amnesia, loss of mobility, language ability, etc. . AlzheimThe pathogenesis of Alzheimer’s disease is complex, among which age is an important factor in the cause of this disease, and genetic factors, external trauma, education level, trace elements, etc., are also important reasons for the occurrence of this disease . The bioa priori knowledge, which is a tedious extraction process and has human factors interfering  within the field of Alzheimer’s disease recognition. However, due to the complex structure of the human brain during Alzheimer’s disease and the difficulty of detecting subtle structural changes in the brain during mild disease, and the fact that the aging process of normal people is accompanied by shrinkage of brain structures, Alzheimer’s patients also suffer from shrinkage of brain areas, which poses many difficulties for research . Only thTo deal with the problem of classification imbalance in the dataset, the most basic approach is either to directly copy the minority classes and add them to the sample set or to employ a certain percentage of the majority classes as the training set to obtain a relatively balanced dataset . HoweverGiven the little variation in the Alzheimer’s MRI images in different disease stages and the small proportion of regions of interest, The contributions of this study are as follows.(1)To solve the problem of classification imbalance in the Alzheimer’s disease dataset, we increased the data volume in the minority classes using data expansion methods such as flipping, adding noise, and contrast adjustment (as depicted in (2)To extract the scattered and subtle pathological features of Alzheimer’s disease, the MPC-STANet is proposed in this study. (a) We employ Dilated Convolution in STAGE 1 of the network to extract features from scattered pathological regions of Alzheimer’s disease to obtain a larger range of feature information. As displayed in (3)The recognition accuracy of the recognition methods proposed in this study for the first four stages of Alzheimer’s disease in non-demented, very mild demented, Mild Demented and Moderate Demented are 97, 95, 98, and 94%, respectively. Other performance evaluations are shown in Therefore, we propose a method in this study for recognizing disease stages of Alzheimer’s disease that combine class-balancing preprocessing and the MPC-STANet. The recognition principle is depicted in Dataset is an important part of the field of pattern recognition and data mining. Since the main motivation of this study is to design a deep learning framework for Alzheimer’s disease classification, the adopted Alzheimer’s MRI dataset was created by researcher Sarvesh Dubey  and was As displayed in Training the neural network model with more datasets allows it to learn more effective feature points to improve the recognition accuracy of the model, prevent overfitting, etc. We use MATLAB 2020b to flip the image, add random Gaussian noise, contrast adjustment, and other data expansion methods to expand the minority classes to improve the training effect of the neural network model. Also, the data expansion method is an important way to balance the data volume of different classes and the result images are shown in Tand set a sampling ratio to determine the magnification N according to the sample imbalance ratio so that the sample can be expanded by N times after sampling. The algorithm steps of the SMOTE are as follows:Suppose the number of the minority classes samples are x ∈ {1, …, T} in the minority class, calculate its distance to all samples in the minority classes based on the Euclidean distance, and select K the nearest neighbors.Step1: Consider a sample B from the K nearest neighbors and combine it with the original sample to synthesize a new sample according to the following formula.Step2: Randomly select a sample N times.Step3: Repeat Step 2 and Step 3 T samples of the minority classes.Step4: Repeat the above steps for ResNet50 constructs the deep network model as a shallow network model and an additional layer of self-mapping connects the trained shallow structure with the additional layer of self-mapping through residual units, transmits the input across layers through a shortcut, and then adds the output after convolution to achieve the effect of fully training the underlying network. ResNet50 has 6 STAGE (STAGE1∼ STAGE6), containing 49 convolutional layers and 1 fully connected layer. Among them, the 49 convolutional layers consist of two basic blocks. As shown in The actual Alzheimer’s disease needs to be judged by looking at the pathological features such as the degree of enlargement of the sulcal gaps, and the degree of atrophy in the hippocampus region and medial temporal lobe. However, due to the small variation in MRI image features during the four disease stages: mild dementia (MID), moderate dementia (MOD), non-dementia (ND), and very mild dementia (VMD), more subtle pathological features need to be extracted to better discriminate. To extract more subtle pathological features, this study employs ResNet50, the winner of the ImageNet large-scale visual recognition competition in 2015, as the basic network. However, the properties of the Alzheimer’s MRI images are very different from those of ordinary images, and designing according to the pathological characteristics of Alzheimer’s disease can effectively improve the accuracy of the model. Therefore, we propose a recognition network of Alzheimer’s disease based on Multi-Phantom Convolution and Space Conversion Attention Mechanism (MPC-STANet), which is improved based on ResNet50.The MPC-STANet is upgraded based on ResNet50, and the network structure is depicted in The feature extraction network of ResNet50 consists of 7 ×7 convolution and 3 ×3 maximum pooling layer (STAGE1), convolutional residual extraction network composed of Conv Block and Identity Block (STAGE2~STAGE5), average pooling layer, and fully connected layer (STAGE6). The MPC-STANet proposed in this study is based on ResNet50, using the Dilated Convolution (DC) instead of 7 ×7 convolution of STAGE1; changing the two basic blocks of Conv Block and Identity Block using Multi-Parallel Convolution (MPC) and averaging pooling layer, proposing Multi-Conv Block and Multi-Identity Block; adding the Space Conversion Attention Mechanism (SCAM) between the convolution blocks. This study improves the network structure, and more details will be provided in the following chapters.The pathological feature points of different disease stages of Alzheimer’s disease are obscure and scattered. To classify disease stages based on the Alzheimer’s MRI medical images more accurately, more effective subtle pathological features need to be extracted. ResNet50 uses a 7 × 7 convolution with a large perceptual field in STAGE1, which is sufficient for extracting features from common and ordinary images in the ImageNet database, but it is difficult to adequately consider the subtle pathological features of MRI. Therefore, to reduce the information loss during the extraction process and improve the recognition ability of the model, we employ Dilated Convolution (DC) to replace the 7 × 7 convolution in STAGE1. Dilated Convolution increases the perceptual field while maintaining the size of the feature map unchanged and does not cause problems such as information loss.r). Among them, r determines the interval size of the holes injected in the convolution. If r is too small, the range of the perceptual field is limited, and if r is too large, the features in the perceptual field lose some relevance. Dilated Convolution can be regarded as inserting a zero value of r-1 into the convolution kernel during ordinary convolution. For ordinary convolution, the convolution kernel of 3 × 3 is calculated on the feature map, and the perceptual field of the new feature point is three, as depicted in r = 2, one zero value is inserted between the 3 × 3 convolution kernels to obtain its perceptual field of five, as depicted in Dilated Convolution expands the perceptual field size of ordinary convolution by setting different dilation rates  and a shortcut branch with 1 × 1 convolution, where the linear branch is used to extract feature information in the feature map and generate the output feature matrix; the shortcut branch uses 1 × 1 convolution to increase the number of channels and match the number of channels of the linear branch, which is used to avoid the problems of gradient disappearance and gradient explosion caused by network depth. Finally, the output feature matrixes of the two branches are summed to obtain the feature map of residual block output, and then the feature map is put into the Relu activation function to enhance the non-linearization of the model. However, due to the variety of structural changes in MRI images in different stages of Alzheimer’s disease such as the changes in the cerebral cortex, especially in the temporal and parietal regions, the features that appear tend to show only subtle differences; whereas the structure of the hippocampal region of suffering from Alzheimer’s disease is significantly changed in different stages.To address this problem, we propose the Multi-Phantom Convolution (MPC) by borrowing the convolution by channel direction in the Inception network , and incThe specific implementation process of Multi-Phantom Residual Block:Step1: The input feature matrix is successively passed through the 1 × 1 convolution layer, batch norm layer, and Relu activation function, and takes the result as the input of step 2; the input feature matrix is successively passed through the average pool layer, 1 × 1 convolution layer and batch norm layer as the output of the shortcut branch.Step2: The feature matrix of the linear branch is divided into four parts according to the channel direction.Step3: The feature map I: passing through the 1 × 1 convolution layer; the feature map II: passing through the 1 × 1 convolution layer and 3 × 3 convolution layer successively; the feature map III: passing through the 1 × 1 convolution layer and 5 × 5 convolution layer successively; the feature map IV: passing through the maximum pool layer and 1 × 1 convolution layer successively.Step4: The four feature maps are concatenated according to the channel direction and passed through the batch norm layer and Relu activate function.Step5: The feature map output from step 4 with the feature map output through the Space Conversion Attention Mechanism (SCAM) are summed by pixels, and the sum result successively passes through the 1 × 1 convolution layer and batch norm layer.Step6: Sum the linear branch (the output of step 5) with the shortcut branch, and pass through the Relu activate function.The visual attention mechanism is a brain signal processing mechanism that is unique to human vision. By quickly scanning the global image, human vision obtains the target region to be focused on, which is generally called the focus of attention, and then devotes more attention resources to this region to obtain more detailed information about the target to be focused on, while suppressing other useless information. The core goal of adding an attention mechanism to the network is essentially similar to the human selective visual attention mechanism, which also selects the more critical information for the current task goal from a multitude of information and ignores other redundant information to successfully improve the expressive power of the network.The more popular attention mechanisms include SENet , CBAM W, and NonThe space dimension of the image refers to the height (H) and width (W) of the image, and C represents the feature channel of the image. The space attention mechanism pays attention to the importance of the space location features of the image, generating space attention coefficients for the output feature maps, and enhancing or suppressing different space location features according to the feature weights. The traditional space attention mechanism tends to focus on weight assignment in only one direction, which inevitably leads to the loss of important information in the image. For the Alzheimer’s MRI images, it is important to observe space changes in different disease stages, such as small changes in the cerebral cortex and structural changes in the hippocampal region, which are important for determining the stage of Alzheimer’s disease. Therefore, we propose a Space Conversion Attention Mechanism (SCAM) that assigns weights based on both vertical and horizontal directions, and the specific structure is depicted in The Space Conversion Attention Mechanism is composed of three parts:(a)The horizontal spatial attention mechanism is used to generate horizontally oriented weight coefficients for each row of features; the vertical spatial attention mechanism is used to generate vertically oriented weight coefficients for each column of features.ei,j represents the weight coefficients assigned by the horizontal or vertical attention mechanism, pixel j represents the sequence feature, i represents the temporal features at a certain moment, and hj represents the hidden layer information of the feature sequence j. cI = {c1, c2 … ci−1, ci} represents the weight coefficients of the vertical attention mechanism in the feature sequence; cΠ = {c1, c2 … ci−1, ci} represents the weight coefficients of the horizontal attention mechanism in the feature sequence.Among them, (b)To further expand the difference between the weight coefficients, we add the horizontal and vertical weight coefficients (addition strategy). for example, the small weight coefficient maybe 0.1 + 0.3 and the large weight coefficient maybe 0.8 + 0.9. In contrast, the difference between the summed weight coefficients is more obvious.(c)To select the more interesting regions, we match the horizontal and vertical weight coefficients to find the maximum value (maximum strategy), which is used to complement the results of the second part of the weight coefficients .cI and cΠ through formula (6), and the concatenated results are passed through 1 × 1 convolution and sigmoid function to make the dimension of input and output consistent.Finally, we concatenate the weight coefficients calculated by the above strategy with SPA represents the Space Conversion Attention Mechanism, Fh represents 1 × 1 convolution, σ represents the sigmoid function, and Weight represents the feature weights.Among them, Batchsize to 32 for training and 8 for testing. The learning rate Ir was set to 10−3, and the epochs was set to 140. The Adam optimizer and Cross-Entropy Loss were used during training, and the incremental gradient descent was used as the training method. After class-balancing preprocessing, there were 12,600 MRI images in Alzheimer’s disease dataset. In this experiment, according to the ratio of 7:2:1, we divided the dataset into a training set, test set, and validation set for training and testing the MPC-STANet.All of the trains and tests in this work are carried out on the same hardware and software platform. The hardware environment is Windows (64bit) operating system, Intel Core i7-9700U CPU, and 2080Ti GPU. The software programming environment for data expansion is MATLAB 2020b; The software programming environment for the MPC-STANet is Python 3.8.12, Pytorch 1.8.2, and CUDA 10.2. Considering the memory size of the GPU and the time of the experiment, we set the To verify whether the training with a class-balanced preprocessed dataset can improve the performance of the model and improve the recognition accuracy, we input the original dataset and the preprocessed dataset into ResNet50, ResNet50-SPAM, and the MPC-STANet, respectively, for experiments. We used Dilated Convolution (DC) in STAGE 1 of the MPC-STANet. To verify the effect of the DC on classification performance, we conducted experiments on ResNet50, ResNet50-DC, and the MPC-STANet under the same test environment. To verify the effect of the Multiple-Phantom Residual Block (MPRB) on model accuracy and parameter, we trained and tested ResNet50, ResNet50-MPRB, and the MPC-STANet using the same dataset. As displayed in To more intuitively understand the improvement of the network accuracy by Space Conversion Attention Mechanism (SCAM), we trained and tested ResNet50, ResNet50-SE, ResNet50-CMBA, ResNet50-SCAM, and the MPC-STANet, respectively, on the preprocessed dataset. To fully validate the effectiveness of the method proposed in this study, we employed the same dataset and experimental environment, and only changed the parts that needed to be compared in each experiment. In this experiment, ResNet50 is selected as the backbone network, and one or more of the three methods, Dilated Conv (DC), Multi-Phantom Residual Block (MPRB), and Space Conversion Attention Mechanism (SCAM), are added to compare the effects of different schemes on model parameters and recognizing accuracy. The comparing results are displayed in Based on the accuracy of the network, the accuracy of the MPC-STANet was higher than other networks, reaching 96.2%. When SCAM was applied to ResNet50, its accuracy improved by 5.5% compared to the original ResNet50. Similarly, ResNet50 using Dilated Conv or MPRB methods improved by 2.1 and 4.9%, respectively, compared to the original ResNet50. The preceding evidence indicates that all three methods are effective for increasing accuracy. And the solution of DC paired with MPRB or SCAM has the largest improvement in accuracy with 8.7 and 9.2%, respectively.Based on the number of parameters of the network, the network with Dilated Conv is the same in terms of the number of parameters as the network that keeps a single variable, which is consistent with the principle that Dilated Convolution does not change the number of parameters. In terms of the number of parameters, ResNet50-MPRB is 4.46M less than ResNet50, demonstrating that the MPRB method aids in network compression.In the same environment, the MPC-STANet has a more stable learning process and higher recognition accuracy than its backbone network ResNet50. The performance of the MPC-STANet on the four disease stages are depicted in We employ four indexes, Recall, F1-score, Precision, and mAP to evaluate the performance of the MPC-STANet. The results are displayed in In this study, we construct the MPC-STANet capable of discriminating the first four disease stages of Alzheimer’s disease and use the Alzheimer’s MRI images created by researcher Sarvesh Dubey as the dataset. We employ data expansion and SMOTE to perform class-balancing preprocessing of the dataset, and then input the preprocessed dataset into the MPC-STANet for recognition, and its average recognition accuracy reaches 96.25%. The experiments show that the combination of class-balancing preprocessing and MPC-STANet for the recognition of the first four disease stages of Alzheimer’s disease is effective and does not require operations such as numerous feature enhancement preprocessing or manual feature extraction, but the following explorations are needed: (1) The researcher who provided the Alzheimer’s MRI dataset did not provide any statistical information about patients and did not account for this condition, which raises doubts about our recommended approach. Therefore, Alzheimer’s disease datasets with detailed statistics need to be further considered in future explorations to be more convincing. (2) In the Space Conversion Attention Mechanism, we employ the maximum strategy to match the vertical and horizontal weight coefficients to select the regions of interest. However, we tend to ignore the data in the small value regions using the maximum value strategy, resulting in data loss. Therefore, it is worth thinking about considering both maximum and minimum values. (3) The actual data volume of Alzheimer’s disease collected in this study is not enough. In the future, the Alzheimer’s MRI image data should be further enriched to improve the generalization ability of the model.To address the problems of classification imbalance of the Alzheimer’s MRI datasets, small structural changes during different disease stages, small proportion of feature regions to the whole MRI image, and scattered features, we propose a novel method for recognizing different disease stages of Alzheimer’s disease based on class-balancing preprocessing and Multi-Phantom Convolution and Space Conversion Attention Mechanism recognition network (MPC-STANet). First, we perform class-balancing preprocessing on the Alzheimer’s MRI datasets using data expansion methods such as flipping, adding noise and contrast adjustment, and SMOTE. Then, we propose the MPC-STANet with ResNet50 as the backbone network. In the MPC-STANet, Dilated Convolution is used to increase the perceptual field of the network to recognize scattered feature regions, and Space Conversion Attention Mechanism is used to enhance feature extraction of subtle changes in the MRI Alzheimer’s image. Based on Multi-Phantom Convolution, Multi-Phantom Residual Block (including Multi-Conv Block and Multi-Identity Block) is proposed to extract subtle brain feature points. For the recognition of different disease stages of Alzheimer’s disease, the proposed MPC-STANet has higher recognition accuracy and a smaller number of parameters compared with the ResNet50 backbone network. The experimental results indicate that the recognition accuracy of the MPC-STANet is 96.2% and the number of parameters is only 1.69M higher than that of ResNet50.Based on the detection of the disease stages of Alzheimer’s disease has been a hot research topic in the field of computer vision-aided diagnosis, The MPC-STANet can be used for disease stage recognition after acquiring the Alzheimer’s MRI dataset, which is significant for doctors to distinguish the disease and take corresponding treatment. Future research in this study will focus on how the network can handle complex structural brain features, how to enhance the extraction ability for subtle and scattered features, and how to handle datasets that are not preprocessed. In addition, we need to consider how to further optimize the structure of the network model to facilitate a more effective recognition of Alzheimer’s disease and delay the deterioration of this disease promptly.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.YL: methodology, writing-original draft preparation, conceptualization, and data curation. KT: software, data acquisition, and investigation. GZ: validation and project administration. AC: supervision and funding acquisition. WC: model guidance. RL: formal analysis and resources. LL: visualization and writing—review and editing. All authors contributed to the article and approved the submitted 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Tunisia found itself in an exceptional situation during the covid 19 pandemic requiring a legal regime of exceptionality and sanitary necessity with a double challenge: the fight against the sanitary crisis, and the preservation of democratic gainsTo describe the legislative framework put in place concerning patients with COVID-19 who stayed at the compulsory containment and to discuss the legality of these emergency decisionsThe authors conducted a descriptive cross-sectional study of patients with COVID-19 staying in the compulsory containment centre of Monastir, with a review of the literature The data were collected through telephone calls. A review of the literature as well as a consultation of the different legislativeThe average age was 41.39 ± 1.26 and the sex ratio was 1.17.Imported cases represented 45.3% of the sample and 23% of them expressed a desire to consult a specialist. The duration of mandatory confinement was on average 35.86±1.31 days with extremes ranging from 7 to 86 days. Concerning the legislative framework of the emergency decisions taken during the first wave, the President of the Republic and the Head of Government used Articles 80 and 70 of the Tunisian Constitution, respectively, to issue legislative texts announcing the state of emergency and accompanying. Thus, these legislative measures were restrictive of rights and freedoms and seriously threatened the fragile gains of our democracyCOVID-19 redefined not only the health system but also the economic conditions, as well as the normative and legislative system 2014No significant relationships.