Concrete, as an engineering material with extremely wide applications, is widely used in various infrastructure projects such as bridges, highways, and large buildings. However, structures such as highways and bridges often need to be situated in variable and harsh service environments for long periods. They not only face cyclic reciprocating vehicle loads but also have to contend with the effects of temperature cycling. Therefore, studying the impact and mechanism of temperature differential cycling on the compressive strength and fatigue life of cement concrete has certain theoretical significance and practical value. This study employed a comprehensive experimental design to investigate cement concrete specimens subjected to typical temperature variations (20–60 °C) and different numbers of temperature differential cycling . Axial compressive strength tests, ultrasonic tests, and compressive fatigue tests were conducted. The axial compressive strength test measured the compressive strength of the cement concrete. It was found that with an increase in the number of temperature differential cycling, the compressive strength exhibited a trend of an initial increase followed by a decrease: at 60 cycles, the strength increased by 10.8%, gradually declined; returned to near-initial strength at 120 cycles, and continued decreasing, reaching a decline of 19.4% at 300 cycles. The ultrasonic test measured the ultrasound velocity of the concrete specimens after different temperature differential cycling. It revealed a decreasing trend in ultrasound velocity with an increase in times of temperature differential cycling, showing a strong linear relationship between the ultrasound velocity loss and strength loss, confirming the correlation between the degree of concrete strength degradation and internal damage. The compressive fatigue test analyzed the fatigue life variation in cement concrete under different times of temperature differential cycling and stress levels, showing good adherence to the Weibull distribution pattern. Based on the approximation assumptions of log-normal distribution and the Weibull distribution, the Weibull distribution parameters for the compressive fatigue life of cement concrete under temperature differential cycling were obtained. Concrete is a solid stone-like material formed by mixing materials such as cement, aggregate, and water in suitable proportions. Because of its economy, high strength, and durability, concrete is extensively applied in large-scale projects and infrastructure such as high-rise buildings, bridges, tunnels, roads, and dams. For highway and other concrete structures, they must withstand the impact of changes in temperature while bearing the cyclic load of vehicle traffic. This is particularly true in regions of China like the Northeast and Northwest, where there are significant diurnal temperature variations, making it inevitable for concrete materials to experience long-term and repeated thermal effects ,2. It isScholars at home and abroad have conducted in-depth studies on the mechanical properties of cement concrete under thermal effects. Li et al.  have resIn recent years, in the study of temperature changes, the research on thermal fatigue damage of concrete has also attracted the attention of numerous scholars both domestically and internationally. An et al.  conducteThe compressive fatigue performance of cement concrete has always been a hot topic in the field of civil engineering ,19. SincIn summary, there is in-depth research on the static mechanical properties of concrete under fixed high or low temperatures, freeze–thaw or temperature cycling conditions , or in other complex environments ,25,26. HTo assess the impact of temperature differential cycling on the compressive strength, fatigue performance, and extent of damage to cement concrete more comprehensively and systematically, this study conducts axial compression tests, ultrasonic tests, and compressive fatigue tests at different stress levels on cement concrete specimens that have undergone different temperature differential cycling. It explores the patterns of change in compressive strength and the loss of ultrasonic wave speed, and also conducts statistical analysis of their fatigue life based on the two-parameter Weibull distribution model, in order to provide scientific theoretical basis and technical support for the application and optimization of concrete.The cement used in the experiment is ordinary Portland cement of P.O 42.5 grade of Jidong brand , which is widely used in various concrete products and building structures due to its superiority of early strength and durability. The main physical and mechanical properties are shown in The raw materials for the cement concrete in this experiment primarily consist of P.O 42.5 ordinary Portland cement, Class II fly ash, S95 slag powder, medium sand, and crushed stone , SM-1 type high-performance pumping agent, and water. The concrete mix proportion is designed referring to the standard JGJ55-2011 “Specification for mix proportion design of ordinary concrete”  with a dfr. Additionally, 9 specimens were designated for axial compressive fatigue tests, distributing 3 specimens for each stress level . The dimensions and quantity of the concrete specimens are detailed in The experiment follows the standards of GB/T 50081-2019 “Standard for test methods of concrete physical and mechanical properties”  and GB/TThe concrete specimens were demolded 24 h after fabrication and cured under standard conditions  for 28 days before the relevant tests were conducted.(I)Place the specimen at the starting temperature (20 °C) for 240 min;(II)Heat the temperature control chamber up at a rate of 2 °C/min to the highest boundary temperature (60 °C) and then maintain a constant temperature for 160 min, with a total duration of 180 min for this process;(III)Cool the temperature control chamber down at a rate of 2 °C/min to the lowest boundary temperature (20 °C) and then maintain a constant temperature for 160 min, with a total duration of 180 min for this process;(IV)Repeat steps (II) and (III) to complete a temperature differential cycling.The equipment used for temperature differential cycling test was the high–low temperature alternating test chamber from Shanghai Yihua Instrument Equipment Corporation   for 240 min, and once the temperature of the concrete block stabilizes, the temperature differential cycling treatment is started. The total duration of a temperature differential cycling lasts 360 min. The schematic diagram of the mechanism of temperature differential cycling is as shown in (I)Specimens of dimensions 100 mm × 100 mm × 300 mm were chosen as prism specimens. Inspection was carried out to verify the specimen dimensions and shape, ensuring that the dimensional tolerances met the specifications.(II)The surfaces of the upper and lower compression plates of the testing machine, as well as the surfaces of the specimens, were wiped clean. The specimens were then positioned vertically on the lower compression plate, aligning the specimen axis with the center of the lower compression plate.(III)The testing machine was activated, and the loading during the test was continuous and uniform, with a loading rate set as 0.5–0.8 MPa/s. (IV)The failure load was recorded during the test process.The axial compression strength test was conducted using a TYA-3000E type microcomputer-controlled constant loading pressure testing machine manufactured by Wuxi Xinluda Instrument Equipment Corporation . The procedures of the test followed the GB/T 50081-2019 “Standard for test methods of concrete physical and mechanical properties” . For eac(I)The compressive strength of the specimens obtained from the previous axial compressive strength test was recorded.(II)fr, followed by a second centering at 40% of fr. The position of the specimens was adjusted until the difference in deformation on both sides of the specimen was less than 5% of the average value.Static compression deformation was performed on the fatigue testing machine for centering: initial centering was conducted at 20% of (III)Q, stress level S, and the axial compressive strength fr: the stress ratio set to Q = 0.1 .Three stress levels were selected in this study: S1 = 0.70, S2 = 0.80, S3 = 0.90 . The fatigue testing machine was then activated. A sine wave was chosen as the loading form, with a loading frequency set to 4 Hz; the magnitude of the fatigue test loading was determined based on the stress ratio (IV)n = 2,000,000 was set as the test termination threshold in this study. That is: if the specimen completely fails when n < 2,000,000 cycles, the test is terminated, and the loading cycles at this point are recorded as the fatigue life of the specimen (N = n); if the specimen has still not completely failed when n = 2,000,000 cycles, the test is also terminated, and the residual strength of the specimen is measured at this time. The number of loading cycles N from the beginning of loading to complete failure was recorded as the fatigue life of the specimen. Moreover, to improve the efficiency of the test, the number of loading cycles The equipment for the axial compressive fatigue test was the PLS-500 dynamic and static fatigue testing machine from the Jinan East Testing Machine Factory  shown in . The tes(I)Use the cross-measurement method to arrange the measuring points, marking ultrasonic wave transmitting and receiving points on two parallel faces of the specimens, as depicted in (II)Couple the transmitting transducer and receiving transducer to the corresponding measuring points and initiate the instrument to transmit ultrasonic waves.(III)t (μs). Record the transit time value (IV)l = 100 mm, the sound velocity v = l/t (km/s) can be calculated. Perform 3 measurements at each measuring point and take the average value as the sound velocity for that measuring point.Record ultrasound velocity: Given the specimen dimensions (100 × 100 × 300), for a measuring distance The ultrasonic test was carried out to measure the acoustic time using the HC-U81 concrete ultrasonic detector. The procedures of the test followed GECS21:2000 “Technical Specification for Inspection of Concrete Defects by Ultrasonic Method” . Three sDue to the large number of concrete specimen indicators measured in this study, as well as the time-consuming nature of the designed thermal differential cycle and fatigue tests, it is necessary to design a rigorous plan before conducting the experiments to improve test efficiency. To better simulate real-world engineering conditions, this study selected a temperature differential range of 20–60 °C. The relatively minor temperature variations and the influence on the mechanical properties of the specimens are also limited. Based on existing research, we believe that subjecting concrete specimens to mechanical performance tests after every 60 cycles of temperature differential changes adequately reflects the pattern of concrete’s mechanical properties in response to temperature variations. However, in terms of ultrasonic testing, as internal concrete damage cannot be observed through visual inspection and considering that ultrasonic testing is a non-destructive method, for a more accurate investigation into the development pattern of internal concrete damage due to temperature differential cycling, we chose to conduct an ultrasonic test on the specimens after every 30 cycles of temperature differential cycling.The procedure of the experiments carried out in this study is shown in By observing the changes in color in the fracture surfaces of the cement concrete specimens, it can be found that the color changed gradually from the dark gray of the un-cycled specimens to the lighter gray after 120 cycles, and then to gray–white after 300 cycles , there were no obvious through cracks in the concrete specimens before loading. However, when the load reached a certain level, cracks rapidly developed and extended in a short time until they penetrated the entire specimen. After a greater number of thermal cycles , cracks could be observed in the early stages of loading, and the specimens showed a denser pattern of cracks at the center.Furthermore, it can be seen from the morphology of the fracture surface in This indicates that with the continuous progression of temperature differential cycling, the number of damaged points on the interface between coarse aggregates and cement paste increases gradually. This phenomenon may stem from the differing thermal effects between coarse aggregates and the cementitious matrix when subjected to temperature fluctuations, leading to distinct deformations between them. These deformation disparities could trigger the formation of microcracks in the transitional area of the interface between coarse aggregates and cement paste. These microcracks continue to propagate and extend under the influence of temperature cycling, progressively compromising the overall integrity of the interface transitional zone and deteriorating its performance. Additionally, we have observed that with the ongoing temperature differential cycles, the points of damage gradually extend from the edge region of the specimen towards the central area. This suggests that the impact of temperature cycling on the specimen’s surface is more significant than its internal regions.ft is the compressive strength of the specimen obtained from the test.The failure loads and compressive strengths of the cement concrete specimens after different temperature differential cyclin measured by the axial compression tests are shown in It can be found from To observe the impact of temperature differential cycling on the strength of cement concrete more intuitively, a graph has been plotted to show the change in the average compressive strength of each group of specimens with the number of temperature differential cycling, as shown in It can be seen from Rs (Equation (2)) was selected in this study to characterize the strength degradation of cement concrete specimens after different temperature differential cycling quantitatively. The strength loss rate of the concrete specimens obtained from the axial compression test is shown in fcpT is the compressive strength of the concrete specimens after different temperature differential cycling, and fcp is the compressive strength of the concrete specimens at the baseline temperature (20 °C).The strength loss rate It is observed that comparing concrete that has not undergone temperature differential cycling, the strength loss of concrete specimens shows negative values in the early stages of cycling, indicating an upward trend in strength. However, after 180 cycles, some specimens begin to show a significant decrease in strength, by more than 20%. After 240 cycles, the loss in compressive strength of the specimens reaches 10–20%, and after 240 cycles, the compressive strength loss even reaches 10–30%.By observing the curve of the average compressive strength loss rate of concrete specimens in The possible reasons are as follows: in the early stages of temperature differential cycling (first 120 cycles), the rise in temperature promotes the hydration of some cement particles that have not yet fully hydrated, which could enhance the internal density of the concrete and thus increase the strength of the concrete specimens. Within the first 60 cycles, the strength increase caused by the hydration of the cement is greater than the deterioration caused by the temperature differential cycling on the strength of the concrete specimens, hence showing an increase in strength. During 120–180 cycles, as the number of temperature differential cycling increases, the unhydrated cement and fly ash particles decrease gradually, the hydration process slows down, the enhancement effect on strength weakens, and the impact of temperature cycling becomes the dominant factor, thus showing a strength loss rate rising from a negative to a positive value, with a relatively large rate of increase. In the later stages of temperature cycling (180–300 cycles), there are some microcracks inside the concrete specimens already, and the existence of those weakens the thermal stress caused by the different thermal properties between the concrete components, thus having a smaller impact on strength, which is reflected in the slowing rate of increase in the strength loss rate of the concrete specimens.The fatigue life of concrete specimens subjected to different temperature differential cycling under various stress levels is measured by axial compression fatigue tests as shown in The fatigue life of concrete, which means the maximum number of load cycles a concrete can withstand before failure under cyclic loading conditions, is one of the important characteristics for evaluating the service performance of concrete. In fact, the fatigue life of concrete is closely related to its own heterogeneity, the number of temperature differential cycling, and the level of the load, and thus typically exhibits a large degree of variability. Therefore, it is necessary to introduce probabilistic statistical methods to describe the distribution of fatigue life of concrete more accurately.It is well known that the distribution of fatigue life of concrete usually follows both a two-parameter Weibull distribution and a log-normal distribution. The two-parameter Weibull distribution is a continuous distribution that describes the distribution of fatigue life of materials through two parameters: shape and scale. Due to its convenience and straightforward derivation, it is widely used in the statistical analysis of material life ,32,33,34x follows a two-parameter Weibull distribution, the density function of which can be describe as , we can obtain a corresponding table as shown in Performing calculations of ω(According to Equation (15), Equations (13) and (14) can be further simplified:β) using Equation (16), then refer to β, and obtain the scale parameter η using Equation (17), thereby achieving the estimation of the two-parameter Weibull distribution parameters through the assumption of the log-normal distribution’s approximation. The calculated values of the Weibull distribution parameters for the compressive fatigue life of the cement concrete in this study are shown in Thus, we can calculate the value of ω(The Kolmogorov–Smirnov (K–S) test method was used in this study to evaluate the goodness of fit between the experimental distribution of the fatigue life of cement concrete and the two-parameter Weibull distribution model determined by the calculated distribution parameters ,38. As oFn(x) be the empirical distribution function of the sample:i is the sample index.Let Pn(x) be the cumulative probability distribution function of the reference distribution, then calculate the distance iD between the two distributions:Let Fn(x) and Pn(x), denoted as Dmax, is the statistic of K–S test:According to the significance of the K–S test, the maximum deviation between Fn(x) corresponds to the cumulative probability distribution function of the compressive fatigue life of the cement concrete obtained from the experiment, and Pn(x) corresponds to the cumulative probability distribution function of the two-parameter Weibull model:For this study, the empirical cumulative probability distribution function Taking the distribution of compressive fatigue life of cement concrete specimens under stress level S = 0.70 without temperature differential cycling (T = 0) as an example, the K–S test for the goodness of fit of the two-parameter Weibull distribution is shown in Dmax = 0.3656, and the standard value of which, according to the critical value table of the K–S test standard, is DC = 0.734  [Dmax < DC, the goodness of fit between the distribution of fatigue life and the two-parameter Weibull distribution can pass the K–S test, meaning that the distribution of compressive fatigue life of cement concrete under the condition of S = 0.70, T = 0 conforms to the two-parameter Weibull distribution. Using this method, the goodness of fit between the fatigue life distribution of cement concrete under all working conditions and the two-parameter Weibull distribution is tested, the results are shown in It can be seen that the value of test statistic calculated out is titions) . Since DIt can be seen that for all stress levels and temperature cycling conditions in this study, the goodness of fit of the two-parameter Weibull distribution of concrete specimen fatigue life can pass the K–S test, which means that at a 5% significance level, the fatigue life distribution of cement concrete can be well described by the two-parameter Weibull distribution.To characterize the impact of temperature cycling frequency on concrete life quantitatively, an exponential function was applied in this study to fit the variation in fatigue life with temperature cycling frequency, as shown in Equation (22):The specific values are shown in To verify the fit of the exponential fitting formula used in this study for compressive fatigue life of concrete, calculations were made using Equation (22) for the fatigue life of concrete under different temperature differential cycling and different stress levels. The calculated fatigue life values were then compared with the experimental values, as shown in It can be found in 1 = 0.70 to S2 = 0.80, and from S2 = 0.80 to S3 = 0.90, results in a reduction in the fatigue life of the concrete specimens by 71.50–73.32% and 96.72–96.92%, respectively. Therefore, we believe that both temperature differential cycling and fatigue loading have a significant impact on the compressive fatigue life of cement concrete, with the influence of the stress level being more pronounced. At the same time, it is also noted that, unlike the change in compressive strength of specimens with temperature cycling frequency, the fatigue life of the specimens shows a consistent downward trend. A possible reason is that although the compressive strength of the specimens may increase after 60 cycles of temperature differential cycling, the internal micro-cracks and other defects continue to accumulate under the influence of fatigue loading. At this stage, both the number and width of internal micro-cracks in the specimens are greater than those before the temperature differential cycling, and the speed of propagation of micro-cracks increases with the load during the fatigue loading process, indicating that the overall integrity of the specimens continues to deteriorate, resulting in a reduction in fatigue life.For a more intuitive description of the relationship between stress level, temperature differential cycling, and fatigue life of concrete, a linear regression analysis was performed on the fatigue life of cement concrete after different temperature differential cycling. The equation of fatigue life for cement concrete after temperature differential cycling (Equation (23)) was obtained and the S-N curve was plotted .(23)LgNIt can be found clearly from The deterioration of compressive strength and the decrease in compressive fatigue life of concrete specimens after temperature differential cycling both, fundamentally, come down to changes in the microstructure. Hence, we approached the issue from the perspective of the microscopic level, employing ultrasonic testing methods to analyze the velocity of sound through concrete specimens after temperature differential cycling statistically.As is widely known, ultrasonics, as a form of mechanical wave, exhibit different phenomena such as reflection and refraction when passing through various media, which could result in different characteristics such as sound speed and amplitude. While the concrete material, which is a heterogeneous medium, undergoes dissimilar thermal strains in its components under the action of temperature differential cycling. This could lead to internal defects such as micro-cracks and voids in the concrete material, which inevitably affect the propagation path of sound waves, thus manifesting as a loss in wave speed. Therefore, we believe that the change in ultrasonic speed after temperature differential cycling can serve as a statistical measure for characterizing the internal damage patterns of concrete.The results of the ultrasonic test on concrete specimens after different numbers of temperature differential cycling in this study are shown in It can be found from The reducing rate of ultrasonic velocity is calculated as shown in Similar to the trend of strength loss with increasing number of temperature differential cycling, the reducing rate of ultrasonic velocity also shows a trend of gradual increasing. However, it is noted that during the early stages of the temperature differential cycling, there was no negative value in the reducing rate of ultrasonic velocity of the specimen. The possible reason is that in the early process of temperature differential cycling (first 120 cycles), although the hydration of some cement particles increased the hardness of the cement stone, thus improving the strength of the concrete specimen, it could not fully compensate for the microcracks continuously forming and developing at the interface between coarse aggregate and cement paste due to the effect of temperature differential cycling. It is precisely because of the presence of these microcracks that there is a certain degree of loss when ultrasonic waves penetrate the specimen. During the mid-process of temperature differential cycling (120–240 cycles), the cement particles are mostly hydrated, and the effects of temperature differential cycling become the predominant factor, causing a large number of internal microcracks to form in the specimen, which is then reflected in the rapid increase in the reducing rate of ultrasonic velocity. In the later process of the temperature differential cycling (240–300 cycles), the thermal stress between the coarse aggregate and the cement paste is reduced due to the presence of a large number of internal microcracks, resulting in a decreased rate of new microcrack formation. Although the existing cracks continue to absorb energy and gradually expand, the rate of expansion has become stable, so they have no longer a significant impact on the sound velocity, which is also reflected in the gradual leveling off of the rate of increase in the reducing rate of velocity.Rs) and the velocity loss rate (RU) of the concrete specimen was also conducted in this study. The fitting formula is as shown in Equation (24), and the fitting curve is shown in R2 reached 0.963, indicating a strong linear relationship between the strength loss rate and the velocity loss rate of the concrete specimen, which also verifies that the ultrasonic velocity loss rate of the concrete specimen can quantitatively characterize the deterioration of concrete strength.Considering that the trend in the reducing rate of ultrasonic velocity of the concrete specimen has a strong correlation with the rate of loss in compressive strength after the temperature differential cycling, a linear fitting analysis of the relationship between the strength loss rate  and one type of concrete . In reality, the impact of factors such as temperature ranges, raw materials of concrete, and concrete mix proportions on concrete performance remains a topic worthy of further in-depth investigation.


Durability and sustainability are important objectives within the development of cement and concrete composites , and havThis Special Issue, “Durability and Sustainability of Cement and Concrete Composites” aims to collate a set of papers presenting recent advances in the field of durability and sustainability of cement and concrete composites, including design, processing, performance, curing and maintenance, and on-site applications of cement and concrete composites. In total, we received 19 submissions, and 12 of them were published. Researchers from China, the United Kingdom, Japan, and France chose to share their recent research findings via this particular issue.Solid wastes , as alternative cementitious binders, help to reduce carbon footprint and improve the sustainability of cement and concrete composites. Ground-granulated blast-furnace slag (GGBS) is a kind of high-activity waste slag formed via the quenching and granulation of silicate and alumino-silicate as the main components in blast-furnace smelting pig iron. Feng et al.  prepared2 and forming more compact hydration products. Furthermore, Yan et al. [In order to improve the negative effects of alternative cementitious binder on the early mechanical properties of cement-based materials, Wang et al.  used poln et al.  used natIn addition, low reactive or inert solid waste can also be used as an alternative aggregate. Recycled tyre shreds and crumbs were used by Maddalena  to repla2 curing, and investigated its influence on the corrosion resistance of cement concrete. It was found that the freeze/thaw resistance and corrosion resistance of inner steel bars were improved by the application of CO2-cured sludge ceramsite. It was also found by Xu et al. [The major characteristic and advantage of sustainable development is the more efficient use of energy and the greater recycling of materials, achieved by conserving and reducing waste. Therefore, improving the durability of concrete can contribute to the sustainable development of concrete. It was reported in  that theu et al.  that theu et al.  investigFurthermore, during its service life, the shrinkage and high-temperature resistance of concrete are also important factors affecting its durability. Yang et al.  comparedThe findings presented in this editorial contribute to recent progress in the durability and sustainability of cement and concrete composites. Durability and sustainability will always be the focus of cement concrete research. Therefore, low carbon emissions and long service life should be the key objectives of the future development of the concrete industry.


The third and fourth groups received BLM plus Stachys pilifera hydroalcoholic extract (SPHE) . Vitamin E  was given to group V in addition to BLM. After 14 days, the animals were euthanized to assess biochemical parameters and lung histopathology. Malondialdehyde (MDA), nitric oxide (NO), total thiol (TSH), and glutathione (GSH) levels were measured. In addition, hydroxyproline (HYP) levels along with histological changes in lung tissue were also assessed.  In this experimental study, 35 male Wistar rats (120–180 g) were divided into five groups , and Vit E. SPHE also significantly increased GSH and TSH levels in comparison to the BLM group.HPLC analyses showed the presence of thymol (55.47 ng/mL) and carvacrol (109.91 ng/mL) in SPHE as potential bioactive phenolic compounds.  The results suggest that SPHE alleviates the development of BLM-induced pulmonary fibrosis by inhibiting the proliferation of fibroblasts mediated by antioxidant pathways. Other mechanisms underlying this Effect of SPHE need to be clarified through further research. E are activated [Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease characterized by proliferation and differentiation of fibroblasts and accumulation of extracellular matrix, primarily collagen, leading to irreversible damage to lung architecture , 2. In sctivated . Other activated . Numerouctivated , 9, 10. ctivated .Bleomycin (BLM) is a chemotherapeutic antibiotic used to treat a variety of malignancies. Since one of the most crucial side effects of BLM during the chemotherapy process is pulmonary fibrosis, it is a classic combination for modeling pulmonary fibrosis in laboratory animals , 11. IntStachys pilifera is a perennial and fragrant plant belonging to the Lamiaceae family [Stachys species have been utilized as antinephritis, antidiarrhea, and wound disinfectants [Stachys species have been confirmed [S. pilifera [S. pilifera hydroalcoholic extract (SPHE) on bleomycin-induced pulmonary fibrosis in rats.e family . In tradfectants , 15. Moronfirmed . Differeonfirmed . Farjam pilifera . In our pilifera , 20. HenBleomycin (BLM), 2,4,6-tri(2-pyridyl)-1,3,5-triazine (TPTZ), sulfanilamide, n-(1-naphthyl) ethylenediamine dihydrochloride, thiobarbituric acid (TBA), trichloroacetic acid (TCA), ethylenediaminetetraacetic acid (EDTA) and 5,5′-dithiobis (2-nitrobenzoic acid) (DTNB) were purchased from Merk- Germany. Standards of thymol and carvacrol were obtained from Sigma Aldrich Co. . Acetonitrile  was purchased from Merck, Germany.Stachys pilifera were collected from the Kakan region in Kohgiluyeh and Boyer-Ahmad Province in spring 2018. Dr. Azizollah Jafari confirmed the collected samples from the Department of Botany, Center for Research in Natural Resource and Animal Husbandry, Yasuj University, Yasuj, Iran, where a voucher specimen (herbarium No. 1897) was deposited. The plant materials were transferred to the laboratory and dried in a dark place.The aerial parts of n-hexane through the liquid-liquid extraction (LLE) method. The remained fraction defatted was stored in the refrigerator for technical HPLC analysis.The air-dried powdered plant (100 g) was macerated in 70% methanol (500 mL) and kept at room temperature for 48 hours (three times 500 mL each). Having filtrated the extract by Whatman No. 1, the solvent was evaporated by rotary apparatus to yield a dark green gummy residue. The extract was stored in the refrigerator at 4°C for further experiments. Briefly, a portion of the extract was dissolved in methanol/water using sonication and stored in a cold place , 22. The18; 250 mm × 3.9 mm i.d., 5 μm). Moreover, the flow rate and the UV-Vis light detector were set at 1.1 mL/min and 220 nm, respectively. In order to have a calibration curve, about 2 mg of standard thymol and carvacrol (each 1 mg) was dissolved in methanol (HPLC grade) to reach the final concentration of 2000 μg/mL. The calibration curves for standards were prepared using concentrations of 0.5 to 2000 μg/mL [In order to calculate the amount of thymol and carvacrol as simple phenolics in SPHE, a high-performance liquid chromatography  apparatus was applied equipped with a quaternary K-1001 controller pumps and a K-2550 UV absorbance detector. A binary mobile phase consisting of 45% water and 55% MeCN was gone through a stainless steel analytical column  with a 12-hour light/dark-light cycle and access to adequate food and water. All ethical issues approved by the Committee for Supervision of Laboratory Animals were followed in this work. The code of IR.YUMS.REC.1396.124 was enclosed by the ethics committee of Yasuj University of Medical Sciences.Animals were sedated with intraperitoneal injections of ketamine (50 mg/kg) and xylazine (5 mg/kg). A single intratracheal instillation of BLM  was administered to anesthetized rats by placing them on a biased panel and suspending them by their upper canine teeth, followed by a single intratracheal instillation of Next . The placebo group received the same volume of intratracheal normal saline as the BLM group. The animals were euthanized two weeks following PLF induction .n = 7):  Group I received a single intratracheal instillation of normal saline (control group)  Group II I received a single intratracheal instillation of BLM solution (7.5 IU/kg in 0.9% NaCl) (BLM-treated group)  Group III was given a single intratracheal instillation of BLM solution (7.5 IU/kg in 0.9% NaCl) and a daily SPHE  for two weeks (BLM/PPX 0.5)  Group IV received a single intratracheal instillation of BLM solution (7.5 IU/kg in 0.9% NaCl) and a daily SPHE  for two weeks (BLM/PPX 1)E  for two weeks (BLM/Vit E)  Group V was given a single intratracheal instillation of BLM solution (7.5 IU/kg in 0.9% NaCl) and a daily Vit Out of fifty male Wister rats, 35 were randomly divided into the following 5 groups  was mixed with 500 t 535 nm , 26.μL of serum or tissue homogenate with 100 μL grease reagent ) was transferred to a 96-well plate and placed in the dark for 15 minutes at room temperature. The absorbance of the samples was then read at 541 nm [Since nitric oxide (NO) is an unstable compound rapidly converted to nitrate and nitrite, its direct measurement is a bit difficult, so the amount of nitrite as a stable NO metabolite was measured by the grease method. In summary, 10 t 541 nm .The Tietze method was used to determine glutathione. In this method, an enzyme cycle in which glutathione reductase (GR) plays a significant role is used to quantify the glutathione present .μL of DTNB, 150 μL of tris-EDTA, and 790 μL of methanol were added to microtubes containing 25 μL of the sample. After incubation at room temperature, the samples were centrifuged, then the adsorption of the supernatant was read at 412 nm, and the amount of TSH was calculated using a molar absorption coefficient of 13,600 M−1·cm−l [The amount of total thiol (TSH) was determined based on the reaction of DTNB with thiol groups and the formation of TNB (2-nitro-5-thiobenzoic acid) and disulfide compounds. Briefly, 10 M−1·cm−l .3+) ions and their conversion to ferrous (Fe2+) at an acidic pH in the presence of tripyridyl-S-triazine, the Fe-TPTZ complex is formed in blue. The resulting color intensity was measured by spectrophotometry at 593 nm [Benzie and strain method was used to measure ferric reducing antioxidant power (FRAP) activity. In this method, the ability of plasma to reduce ferric ions is measured. Following the reduction of ferric  levels in lung tissues .μm sections were prepared. After staining with hematoxylin-eosin, lung tissue was examined by light microscopy.Lung tissue samples were kept in 10% formalin buffer. These samples were dehydrated with alcohol and immersed in paraffin. Tissues were placed in paraffin, and 5 P < 0.05 was considered significant.Data were analyzed by SPSS software for normality and expressed as Mean ± SEM. Statistical calculations were performed for significant differences between the experimental groups using analysis of variance (ANOVA) and Tukey post hoc test. Stachys pilifera aerial parts showed the presence of low-weight phenolics, thymol (55.47 ng/mL), and carvacrol (109.91 ng/mL) . However, using SPHE at doses of 300 and 600 mg/kg caused a substantial decrease in these indices. BLM administration significantly reduced TSH and GSH levels compared to the control group. However, in the groups receiving SPHE + BLM, these markers increased significantly compared to the BLM-treated group. Vitamin E treatment also had a similar effect to SPHE . However, SPHE significantly reduced hydroxyproline when compared to the BLM group alone  is one of the most important of its side effects. This compound damages lung tissue by increasing extracellular matrix remodeling and redox imbalance . These dMedicinal plants and their bioactive phenolic and flavonoid compounds have gained more attention due to their beneficial properties, such as antioxidant and anti-inflammatory potencies .The present study investigated the possible protective effects of SPHE against the harmful effects of BLM on pulmonary tissue. In a manner similar to IPF, BLM destroys alveolar epithelial cells and provokes a pronounced inflammatory response. Reference . It is aBased on our results, it was determined that BLM caused significant hyperplasia and alveolar damage in the animals. BLM administration dramatically elevated the concentrations of hydroxyproline (HYP), MDA, and NO, and reduced TSH and GSH levels. Oral treatment with SPHE alleviated BLM-induced pulmonary damage by restoring lung tissue architecture and normalizing the indicated oxidative stress markers.The inflammatory mediators' cascade was activated following inflammatory cell infiltration in the early stages of pulmonary fibrosis. In the later stages, fibroblasts activate and stimulate collagen secretion . As a ref HYP in lung tissue compared to the saline-treated group. Treatment with SPHE significantly reduced the elevation of HYP levels in the lung tissue due to BLM intoxication.In accordance with Bahri et al.  and HuanThis activity could be attributed to phenolic and flavonoid ingredients in SPHE that produce anti-inflammatory and antioxidant effects . In thisThe critical role of oxidative stress during the PF process has been demonstrated. Increased ROS and oxidant/antioxidant imbalances have been shown to play a critical role in lung tissue damage in IPF patients , 40. TheThere is increasing evidence that NO plays an essential role in the pathogenesis of numerous lung diseases. An isoform of NO called iNOS is responsible for producing cytokines in lung diseases. This isoform is involved in the pathogenesis of PF by producing ROS such as peroxynitrite and nitrogen dioxide. A significant increase in NO levels was observed in the BLM-treated group compared with the control group, which was consistent with the results reported by Kseibati et al. . In the SPHE-treated groups had lower FRAP levels than BLM-treated groups, which is in line with our previous findings. As antioxidants, the thiol groups of proteins and glutathione play a significant role in inhibiting ROS and RNS. Oxidative stress reduces -SH reserves by oxidizing the -SH groups into disulfide or sulfonic acid. References , 44. FurE as an antioxidant behaved similarly to SPEH in the evaluated parameters, but it did not significantly affect GSH levels. Polyphenols such as resveratrol, mangiferin, quercetin, and dihydroquercetin mitigate lung damage after BLM induction in animal models [However, Vit l models . It is al models . Carvacrl models –47. Salil models . Hence, Future research could address two significant limitations of the study. First, SPHE is also examined for its effects on apoptosis and inflammation in BLM-induced lung fibrosis. The second step is to prepare and evaluate the extract's different fractions or principal components.The evidence from this study suggests SPHE on BLM-induced fibrosis. Histological examination of lung tissue confirmed the biochemical results. Thus, it can be postulated that a defatted SPHE involving thymol and carvacrol as the significant constituents may effectively reduce BLM damage due to its antioxidant effects. More research is required to determine the precise mechanisms of SPHE in managing pulmonary fibrosis.


The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study’s limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model’s predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R The field of concrete mix design has seen significant advancements in recent years with the integration of machine learning techniques. The ability of these models to predict the compressive strength of concrete has the potential to revolutionise the construction industry by providing more efficient and accurate methods for mix design. However, the prediction accuracy of these models depends on many factors, including the computational complexity of the model itself. This study aims to investigate the influence of computational complexity on the prediction accuracy of concrete compressive strength in machine learning models for concrete mix design. The findings of this research will provide insights into the trade-off between model accuracy and computational efficiency and will guide the development of more effective machine learning models for concrete mix design in the future. The concrete mix composition consists of cement, water, a combination of fine and coarse aggregates, and supplementary materials referred to as additives and admixtures, where additives are incorporated during the cement manufacturing stage, whereas admixtures are introduced during concrete mix preparation. These substances are formulated to enhance the chemical properties and performance of concrete, specifically with regard to compressive strength, durability, and workability. There are various types of additives and admixtures ,2,3,4, iProperly designing a concrete mixture is a crucial aspect of the construction process, with multiple factors to consider. It should be designed with economy in mind, ensuring that the desired properties can be achieved using the most cost-effective raw materials. The mixture must also be optimised for the specific technology used in the construction process, considering elements such as workability and setting speed. Environmental conditions, such as temperature ,8, preciMachine learning, a prominent subfield of artificial intelligence, has garnered significant attention in recent years due to its vast applications and transformative potential across various domains. The fundamental concept behind machine learning involves empowering computers to acquire knowledge from data, recognize patterns, and arrive at informed choices while minimizing the need for extensive human involvement. By utilising algorithms and statistical models, machine learning systems can adapt and improve their performance over time, making them valuable tools for a multitude of tasks, ranging from natural language processing  and imagIn this paper, the influence of computational complexity on the performance of machine learning models used for predicting the compressive strength of concrete is explored. This research assessed three sets of five deep neural network models , each with differing levels of computational complexity. Through an examination of various machine learning models, the goal is to identify the trade-offs between accuracy and computational efficiency. This could provide valuable insights into the development of robust and cost-effective models for concrete mix design.Designing an optimal concrete mix is a multifaceted challenge requiring a comprehensive understanding of concrete technology and significant practical experience. The primary objective of the design process is to determine suitable material compositions to achieve the desired properties in fresh concrete during transportation and placement and in hardened concrete. Distinct mechanical properties are anticipated at each stage of the concrete fabrication process. Various characteristics influence concrete performance, including plasticity, durability, compressive strength, and modulus of elasticity. The significance of these properties may vary at different stages, for instance, adequate compressive strength is crucial for the designed ultimate limit state, whereas sufficient durability is critical in aggressive environments. Designing a mix with inappropriate specifications can result in severe consequences. Therefore, fearing noncompliance with the necessary criteria, concrete mix manufacturers often intentionally exceed the designed parameters .Global corporate engineering practices related to concrete technology exhibit considerable variation and notable commonalities. Within the European Union, the primary standard governing concrete technology issues is EN 206 Concrete: Specification, Performance, Production, and Conformity , while EThe approach that combines experimental and analytical methods involves determining the required quantity of ingredients through analytical calculations and confirming its accuracy through destructive lab testing. This technique enables the researchers to establish the proportions of cement, water, and aggregate by weight for a given volume, employing three formulas related to consistency (1), strength (2), and impermeability (3). The consistency formula (1) is integrated into the water requirement equation, which assists in identifying the optimal consistency. The demand for water in cement and aggregate relies on factors such as particle size, form, texture, ratio within the mixture, and the desired consistency of the concrete blend. To account for the water needs of concrete additives and admixtures, they are added to the aggregate or cement based on the particle dimensions. Factors such as grain size, shape, surface roughness, composition proportion, and the concrete mix’s required consistency influence the cement–water and aggregate–water demand indices created by Bolomey and Stern .(1)W=C·W represents the water quantity in 1 cubic meter of concrete (measured in L), p is the air content in one cubic meter of concrete (measured in dm3), and 3) of concrete, while C stands for the cement weight in kilograms per cubic meter (m3) of concrete. Additionally, 3, K denotes the weight of cement in kg per cubic meter (m3) of concrete, and 3. Using the equations mentioned above, one can determine the quantitative composition of a concrete mix, which consists of the quantities of cement, water, and aggregate in a cubic meter (m3) of the mixture. The three equations method does have specific boundary constraints, for instance, the porosity of the concrete mix must not surpass 0.002 of the mix volume without air-entraining additives or 0.008 of the mix volume when incorporating air-entraining additives.In Equation (1), The process of creating a concrete mix design incorporates several steps such as formulating initial suppositions, defining the necessary characteristics of both the fresh and cured concrete, selecting and assessing the components of the mix, crafting the blend, testing its properties in a lab setting, and finally devising a workable formula. In formulating initial suppositions, key considerations include the concrete’s intended application and the specific traits of the new structure. These factors include location, degree of reinforcement, and the structural cross-section’s geometric properties. The primary attributes of interest for fresh concrete are its bulk density, consistency, and air content. As for the hardened concrete, we look at its frost and fire resistance and the grade of its compressive strength. It is essential to scrutinise the technological process and evaluate the conditions under which the concrete matures and the method used for compacting the fresh mix. The concrete exposure class is a significant parameter, defining the level and nature of environmental stress the material can withstand. Further specifications such as the concrete’s impermeability need to be established. Parameters such as maximum aggregate size and mix workability also need determination. The components of the concrete mix, including the appropriate type of cement, water, and aggregate quality, should be selected and appraised as per relevant standards. Following the mix design and lab tests, the final stage involves creating a functional formula for one cubic meter of the concrete mix. One should also anticipate potential recipe modifications due to the aggregate’s moisture content and adjust it according to specific circumstances, such as the transportation vehicle’s capacity ,28.Machine learning has permeated numerous scientific domains, showcasing its versatility and potency. It has a particularly large number of applications in the field of structural and material engineering. Machine learning has found use within this branch of science in areas such as structural health monitoring, crack detection, life-cycle cost assessment, or prediction of diffusivity . ParticuThe complexities of predicting concrete strength through machine learning were first articulated by Yeh et al.  in 1998.In 2003, this subject was further refined by Seung-Chang Lee , who empIn 2005, Hola, J. and Schabowicz, K. ,34 introIn 2006, a neural-expert system for predicting the compressive strength of high-performance concrete was proposed by Gupta et al. . They foThe advent of deep learning in this field was introduced by Fangming Deng et al.  in 2018.Ziolkowski, P. et al.  introducIn a publication from 2020, Nunez, I. and his team  shared iAnother noteworthy contribution from the same year is a study by Marani, A. and his colleagues , who expIn 2021 Ziolkowski, P. et al.  introducAdil, M. et al.  investigFeng, W., in their paper  exploresTavares, C. et al., in their two-part study ,47, propEndzhievskaya, I.G. et al.  presentenssm) of concrete. This work’s novelty lies in developing a single model that simultaneously predicts two crucial concrete properties, compressive strength and Dnssm. The gradient boosting model demonstrated the most impressive prediction accuracy, yielding the mean absolute error (MAE) of 6.683 and 1.363, mean squared error (MSE) of 83.369 and 3.712, and root mean squared error (RMSE) of 9.131 and 1.927 for compressive strength and Dnssm, respectively. The authors stress the necessity to expand the model with comprehensive datasets encompassing a wider range of concrete properties to improve its versatility.The study presented by Taffese, W.Z. and Espinosa-Leal, L.  stands oMachine learning models can be used to predict concrete’s technical properties based on the mix composition. This study tries to determine the impact of increasing the computational complexity of the artificial neural network on the model’s performance. The quantity of layers is one of the deep neural network (DNN)’s ,51 featuHowever, while an increased depth can enable a model to learn more complex representations, it poses new challenges. The augmentation in the number of layers directly expands the model’s parameters, thereby escalating the risk of overfitting. Overfitting manifests when a model excessively adapts to the training data, compromising its ability to generalise to unseen data effectively ,56,57. TThe analysis adopted a classical approach involving the construction of a model that will estimate the concrete compressive strength determined by the quantitative concrete mix composition. The prepared analysis used a database from previous studies ,43, whicck—concrete compressive strength (MPa), C—cement (kg/m3), W—water–cement ratio (-), FA—fine aggregate (kg/m3), CA—coarse aggregate (kg/m3). These synthetic data do not create new knowledge but helps to achieve better robustness of the AI model. The parameters utilized have been showcased in The database used in this research contains 6187 records, generated using a dedicated AI model from the original database that contained 741 records of concrete recipes ,43, alonThe tabular long short-term memory (TLSTM) model ,70 was uJSD represents the degree of resemblance between the field distributions of the original and synthetic data. It is a method commonly applied for comparing two distributions. The average Jensen–Shannon divergence value across all fields inversely correlates with the data quality, with lower value indicating higher quality. A visual comparison of the original and synthetic field distributions is facilitated by presenting bar charts in The compressive strength of concrete was assessed in a laboratory setting utilising standardised samples under the EN-206-01 standard . The samThe above database was used to train a series of deep artificial neural network models. The goal of the analysis is to investigate the influence of the computational complexity of DNNs on the accuracy of predicting technical parameters of concrete. Created models, given the quantitative composition, can estimate the compressive strength of concrete. In the analysis below, five neural network models of varying computational complexity, differentiated by the number of hidden layers , were compared, repeating the entire process in three series  for validation purposes. From the least complex model, MLM1 has two hidden layers, to MLM5, which has six hidden layers. The number of neurons in a typical hidden layer is four. Each model has five parameters, four input variables, and one output variable. For effective training of deep neural networks, the dataset is typically divided into three independent parts: training, validation (or selection), and testing. This is a standard procedure applied in deep learning ,80. The The architecture of the first model, MLM1, consists of 20 neurons, including four input neurons, nine neurons spread over two hidden layers, with four neurons in one scaling layer, one neuron in the descaling layer, one neuron in the bonding layer, and one neuron in the output layer. The architecture of the second model, MLM2, consists of 28 neurons, including four input neurons, 17 neurons spread over three hidden layers, with four neurons in one scaling layer, one neuron in the descaling layer, one neuron in the bonding layer, and one neuron in the output layer. The architecture of the third model, MLM3, consists of 36 neurons, including four input neurons, 25 neurons spread over four hidden layers, with four neurons in one scaling layer, one neuron in the descaling layer, one neuron in the bonding layer, and one neuron in the output layer. The architecture of the fourth model, MLM4, consists of 44 neurons, including four input neurons, 33 neurons spread over five hidden layers, with four neurons in one scaling layer, one neuron in the descaling layer, one neuron in the bonding layer, and one neuron in the output layer. The architecture of the fifth model, MLM5, consists of 52 neurons, including four input neurons, 41 neurons spread over six hidden layers, with four neurons in one scaling layer, one neuron in the descaling layer, one neuron in the bonding layer, and one neuron in the output layer. The data features, represented as input variables, are assigned to the input neurons of the neural network structure, while the output neuron is connected to the target variables. To enhance the model’s effectiveness, a method known as feature scaling was implemented across all models. This process entails converting the numerical attributes of the data into a specific scale . This scThe quasi-Newton method ,92 was eIt should be noted that the models were trained on a specific set of data. Therefore, when using the models, one should operate within the values indicated in In the following study, five models of deep artificial neural networks with varying degrees of computational complexity were analysed, named consecutively MLM1, MLM2, MLM3, MLM4, and MLM5, starting from the least complex network to the most complex, in three series I, II, III. First, a feature correlation analysis was prepared to determine precise relationships between individual variables. The result of the analysis is a feature correlation heatmap visible in The feature correlation heatmap reveals the relationships of individual variables, where a value closer to 1.0 indicates a stronger correlation . One can2 [2 determines the fraction of this discrepancy that can be accounted for by the model. In a scenario where the model fit is ideal, resulting in output values perfectly matching the target values, the R2 coefficient would equate to one. 2).Each of the models was subjected to a goodness-of-fit test ,102,103.2 ,105. Thi2 values were calculated for the target variable fck in the individual models and series. In series I, it was 0.5691 for MLM1, 0.6268 for MLM2, 0.6053 for MLM3, 0.6438 for MLM4, and 0.6453 for MLM5. In series II, it was 0.5467 for MLM1, 0.6017 for MLM2, 0.6227 for MLM3, 0.6285 for MLM4, and 0.6514 for MLM5. In series III, it was 0.5272 for MLM1, 0.5959 for MLM2, 0.6136 for MLM3, 0.6337 for MLM4, and 0.6571 for MLM5. The values of the coefficient of determination (R2) for each model are shown in In the considered issue, a series of R2 value can be noticed in more complex models, suggesting that these models exhibit better predictive capabilities than less complex models. This observation is evident in three series, except for series I, where the MLM2 model has a higher R2 coefficient value than the MLM3 model. This deviation was not observed in the remaining series and may result from various reasons. An exhaustive analysis of the model’s errors was performed by computing a range of error metrics across each series. This analysis included metrics such as mean squared error, Minkowski error, normalized squared error, root mean squared error, sum squared error [It can be observed that a reasonably good performance of the created models was achieved. Simultaneously, with the increased computational complexity, a higher Red error ,108,109.In ck. The abscissa represents the centres of the containers, and the ordinate represents their corresponding frequencies. Error histograms show the distribution of errors from the model for the test subset. A normal distribution for the target variable is expected. For Analysing For For It should be noted that all error histograms presented in In concrete structure production technology, a key challenge lies in ensuring predictable characteristics in the raw concrete mix and the hardened end product. Concrete mix manufacturers are responsible for guaranteeing that the concrete they deliver to construction sites meets the desired standards. However, achieving these standards consistently throughout the manufacturing process can prove daunting. Reliable prediction of concrete technical parameters is intricate, and most current solutions in the engineering field are estimations, which have increasingly become obsolete due to rapid advancements in material engineering. Predictive analytics dedicated to forecasting various phenomena, attributes, and patterns based on machine learning holds the potential to enhance the methodology behind concrete mix design substantially. The paper analyses the impact of computational complexity on the effectiveness of predicting the compressive strength of concrete using machine learning models. This study focuses on the growing interest in applying machine learning algorithms in material engineering, specifically in predicting the compressive strength of concrete, a key indicator of its quality. Computational complexity in this research refers to the number of hidden layers in the neural network architecture. In the context of machine learning models, computational complexity is essential, as it can influence the speed and effectiveness of the model’s training and its ability to generalise to new data. This study evaluated five deep neural network models  of varying computational complexity in three series. Each of the MLM1-MLM5 models underwent training, selection, and testing in each three series. The crux of this research was to establish an ideal deep neural network structure and train it using a vast database of concrete mix recipes and their associated laboratory-based destructive tests.2) increase was observed along with the increase in the model’s computational complexity. The smallest value of R2 was captured for model MLM1 and the largest for MLM5. It can be observed that errors in the five analysed metrics, namely mean squared error (MSE), Minkowski error (ME), normalized squared error (NSE), root mean squared error (RMSE), sum squared error (SSE), in training, selection, and testing decrease with the increase in the model’s computational complexity, with the greatest error decrease observable for the NSE metric in training, selection, and testing and the ME metric in training. Furthermore, it is worth examining the mean values of absolute error, relative error, and percentage error, which tend to decrease as the computational complexity of the model increases. The error histograms for all analysed models in all series have a normal distribution. While the proposed method offers a promising solution, it possesses certain limitations and does not comprehensively encapsulate all the interactions between concrete mix components and their resulting properties. This is a research gap that necessitates further exploration. Nonetheless, the outcomes of this paper inspire optimism for this method’s expanded application in practical engineering settings.Presented machine learning models predicts the compressive strength of the concrete mix based on its unique composition. Based on the obtained results, the following conclusions can be formulated. There is a relationship between the computational complexity of deep neural network models and their ability to predict the compressive strength of concrete. The conducted analyses showed that as the computational complexity of the model increases, so does its predictive ability. This means that the more complex the neural network architecture, the more effective it is in predicting the compressive strength of concrete within the conducted analyses. Several parameters point to the above conclusion. In all three series, the coefficient of determination (RFuture investigations should aim to broaden this method’s utility in the concrete mix design process by predicting additional fresh and hardened concrete properties such as durability, workability, air entrainment, and reliability. A more holistic strategy for optimising concrete mix design is also essential to development. The findings from this research lay a robust groundwork for further refinement and application of the proposed AI-driven method in concrete mix design. AI-driven methods automate the traditionally labour-intensive tasks, granting civil and structural engineers the liberty to tackle more intricate and inventive challenges. It is anticipated that this method will improve both the efficiency and precision of the design process, particularly in an era where advancements in hardware are accelerating the computational capabilities of AI.


By quantifying changes in magnitude and location of light scattering caused by subcellular movement, we can detect metabolic changes that occur when bacteria are challenged with antibiotics.2–5 Here, we show that improvements to SCFI’s deep-learning models can correctly classify metabolic cell states for Escherichia coli  and determine cell states for UTI-related species  with front-line antibiotics (trimethoprim and nitrofurantoin).We have developed a phenotypic and label-free antibiotic susceptibility test (AST) termed Sub-Cellular Fluctuation Imaging (SCFI) to address rising rates of antimicrobial resistance.E. coli, K. pneumoniae and P. mirabilis species to determine their respective performance metrics .A total of 100 μL per sample is introduced to microfluidic flow chambers and immobilized using a species-specific antibody coating for 10 min. The bacterial suspension is removed, washed (to minimize non-bound cells) and incubated with 100 μL of MHB broth containing either a treated (with antibiotic) or untreated (without antibiotic) condition for 30 min. Images are captured at a laser intensity of 20 Hz for 20 s, for ≥50 individual bacterial cells per test. Convolutional neural networks (CNN) were developed to enable classifications of bacteriostatic antibiotics (trimethoprim and methicillin) on n=150 cells) and demonstrated high accuracy  when compared with known MICs.CNN models were developed to successfully classify treated, untreated, resistant and susceptible bacterial conditions. All conditions were tested in triplicate (In these experiments we successfully demonstrated rapid (30 min) and accurate (≥90%) classification of bacterial resistance states by deep learning techniques. These data also continue to support existing literature that SCFI is an AST that is agnostic to the antibiotic class and bacterial species used. This system is undergoing product development and will be translated into a bespoke hardware system for clinical and antibiotic research applications.


This case report describes a 55-year-old male who presented with a two-year history of abdominal pain and weight loss, along with recent onset nausea, vomiting, and anorexia. Laboratory tests revealed elevated serum IgG4 levels and anemia, and abdominal CT showed diffuse pancreatic enlargement and multiple retroperitoneal lymphadenopathy. Endoscopic ultrasound-guided fine-needle aspiration of the pancreatic mass revealed dense lymphoplasmacytic infiltration with fibrosis and increased numbers of IgG4-positive plasma cells, consistent with the diagnosis of IgG4-related pancreatitis. Treatment with prednisone led to a significant improvement in the patient's symptoms and laboratory values, with subsequent reduction in the size of the pancreatic mass and retroperitoneal lymphadenopathy. The patient remained asymptomatic at the six-month follow-up visit, and serum IgG4 levels remained within the normal range. This case highlights the importance of considering IgG4-related disease (IgG4-RD) in patients presenting with abdominal pain and weight loss and the potential effectiveness of corticosteroid therapy in managing this condition. IgG4-related disease (IgG4-RD) is a rare autoimmune condition affecting various body organs, including the pancreas, salivary glands, and lymph nodes. The presence of lymphoplasmacytic infiltration and fibrosis identifies this illness, and it can present with vague symptoms like abdominal pain, fatigue, and weight loss. Since the first description of IgG4-RD in 2003, there has been growing recognition of this disease. It is now considered a critical differential diagnosis for patients with unexplained organ enlargement or fibrosis. Diagnosis is made by analyzing clinical and laboratory data and histological features of the biopsy sample. Elevated serum IgG4 levels are a hallmark of the disease. However, not all patients with IgG4-RD have elevated IgG4 levels, moreover, other conditions can also cause elevated IgG4 levels. Therefore, biopsy remains an essential tool for diagnosing IgG4-RD .Glucocorticoids are the first-line treatment for IgG4-RD and can induce remission in most patients. However, long-term follow-up is necessary to monitor for disease recurrence and the potential development of other autoimmune conditions . Here, wA 55-year-old male presented to the Internal Medicine clinic with a two-year history of abdominal pain and weight loss. The patient reported a recent onset of nausea, vomiting, and anorexia. He denied any history of tobacco or alcohol use and had no significant past medical history.On physical examination, the patient appeared thin and pale. The patient's abdomen was soft but diffusely tender to palpation, with no palpable mass. The rest of his physical examination was unremarkable. The patient's laboratory values showed anemia and elevated serum IgG4 levels Table .Other laboratory values were within normal limits, including liver function tests, triglyceride levels, renal function tests, and electrolytes. Serological tests for hepatitis B and C viruses and human immunodeficiency virus (HIV) were negative.Abdominal CT showed diffuse pancreatic enlargement and multiple retroperitoneal lymphadenopathy Figure . There wEndoscopic ultrasound (EUS) revealed a 3-cm hypoechoic mass in the head of the pancreas. Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) of the pancreatic mass was performed, which revealed dense lymphoplasmacytic infiltration with fibrosis and increased numbers of IgG4-positive plasma cells Figure . These fBased on the clinical and histological findings, he was diagnosed with IgG4-RD. The patient was started on prednisone 40 mg/day for four weeks, significantly improving his symptoms and laboratory values. After two weeks of treatment, his serum IgG4 levels decreased to 88 mg/dL, and his hemoglobin levels normalized. We gradually tapered the dose by 5 mg/week over two months and discontinued it entirely. At the six-month follow-up visit, the patient remained asymptomatic, and repeat imaging showed a significant reduction in the size of the pancreatic mass and retroperitoneal lymphadenopathies. The serum IgG4 levels remained within the normal range.IgG4-related disease is a rare autoimmune disease affecting various organs, including the pancreas, salivary glands, lymph nodes, and others. The disease is characterized by lymphoplasmacytic infiltration and fibrosis, which can lead to organ dysfunction and failure. The exact pathogenesis of the disease still needs to be better understood. However, it is thought to involve a dysregulated immune response, with increased production of IgG4 antibodies and activation of inflammatory cells .The diagnosis of IgG4-RD is challenging due to its non-specific clinical presentation. The disease often presents with non-specific symptoms, including abdominal pain, weight loss, and fatigue, which can mimic other medical conditions. Laboratory tests may reveal elevated serum IgG4 levels, but this finding is not specific and can also be seen in other autoimmune diseases, infections, and malignancies. Imaging studies, such as CT, MRI, and positron emission tomography (PET), can show characteristic features of IgG4-RD, such as diffuse organ enlargement, lymphadenopathy, and fibrosis. However, these imaging findings are not specific and can be seen in other medical conditions.The gold standard for diagnosing IgG4-RD is a histological examination of affected tissues, which can show characteristic features of the disease, including dense lymphoplasmacytic infiltration, fibrosis, and increased numbers of IgG4-positive plasma cells. Various methods, including percutaneous biopsy, endoscopic biopsy, and surgical biopsy, can obtain biopsy samples. The choice of the biopsy method depends on the location and accessibility of the affected organ and the medical team's expertise [The treatment of IgG4-RD is primarily based on glucocorticoids, which can induce remission in most patients. The exact mechanism of action of glucocorticoids in IgG4-RD is still poorly understood, but it is thought to involve suppression of the dysregulated immune response and reduction of inflammation and fibrosis. The initial dose of glucocorticoids is typically high, ranging from 30 to 60 mg/day, depending on the severity of the disease and the patient's comorbidities. The dose is then gradually tapered over several months, closely monitoring symptoms and laboratory values. The goal of treatment is to achieve remission of the disease and prevent disease relapse and the progression of fibrosis in affected organs .In addition to glucocorticoids, other immunosuppressive agents, such as azathioprine, mycophenolate mofetil, and rituximab, have been used to treat IgG4-RD, either alone or in combination with glucocorticoids. These agents are typically used in patients who do not respond to glucocorticoids or develop glucocorticoid-related adverse effects. However, their efficacy and safety in treating IgG4-RD still need to be well established, and further studies are required to determine their role in managing the disease .Long-teBased on the clinical and histological findings, this case report describes a 55-year-old male with a two-year history of abdominal pain and weight loss who was diagnosed with IgG4-related pancreatitis. The patient's symptoms and laboratory values improved significantly with prednisone therapy, and repeat imaging showed a reduction in the size of the pancreatic mass and retroperitoneal lymphadenopathies. The serum IgG4 levels remained within normal at the six-month follow-up visit. This case highlights the importance of considering IgG4-related disease in the differential diagnosis of patients with unexplained abdominal pain and weight loss and the potential effectiveness of glucocorticoid therapy in managing this condition.


Heritage building structures in many situations contribute unique value to national cultural heritage. In engineering practice, monitoring of historic structures includes visual assessment. This article assesses the condition of the concrete in one of the most recognizable buildings in Łódź, the former German Reformed Gymnasium, located at Tadeusza Kościuszki Avenue. The paper reports a visual assessment of the structure and the degree of technical wear affecting selected structural components of the building. A historical analysis of the building’s state of preservation, characterization of the structural system, and an assessment of the condition of the floor-slab concrete were carried out. The state of preservation of the eastern and southern facades of the building was found to be satisfactory, while on the western side (with the courtyard) the facade is in a poor state of preservation. Tests were also conducted out on concrete samples taken from individual ceilings. The concrete cores were tested for compressive strength, water absorption, density, porosity, and carbonation depth. Corrosion processes including the degree of carbonization and the phase composition of the concrete were identified by X-ray diffraction. The results are indicative of the high quality of the concrete produced more than 100 years ago. Historic building structures are often of unique value to national cultural heritage. When a building is in use, it is sometimes periodically out of service. The reasons for this can vary and may be affected by the socioeconomic conditions in which the building is used. Monitoring of a historic building structure can take the form of periodical or comprehensive inspection of the technical conditions of the structure, for example, in the context of a planned upgrade. In engineering practice, the monitoring of historic structures includes a visual assessment. Visual assessment is usually the first step in the diagnosis of any structure . A generThe analysis of the condition of a public utility building was carried out for one of the most recognizable buildings in Łódź, the former German Reformed Gymnasium located at Tadeusza Kościuszki Avenue. Due to its historical and historic nature, prior to the examination of the structural elements, it was necessary to carry out historical analysis involving visual assessment of the building, the condition of the façade, and the nature of its structure.The work included a visual assessment of the object and the degree of technical wear affecting a selected structural element. Historical analysis was conducted to assess the state of preservation of the building and the characteristics of the structural system. Structural diagnosis is an integral part of the above-mentioned tests.Analysis of the technical condition of the facility was carried out due to its historic character. As the literature indicates, the diagnostics of building objects is a complex task due to the unique nature of the objects, and in many cases requires an individual research approach ,9,10. ItWhen planning the analysis of the technical condition of the building, access to individual structural elements was taken into account, which can often be limited due to geometry and location. Currently, preparatory works are being carried out on the premises, as part of the planned reconstruction and modernization of the facility. The purpose of this research was to analyze the technical conditions, and the study process was divided into three main areas: historical analysis, analysis of the existing conditions, and assessment of concrete properties, according to the scheme shown in A former school building, the German Reformed Gymnasium  is locatThe main school building  between multi-sash windows .The former school building is one of the most recognizable buildings in Łódź, its architecture corresponding to the trends of Berlin architecture at the time of its construction. It is a corner building with the eastern front facade facing Kościuszki Avenue and the southern facade facing Zamenhofa Street.The object is preserved in its original state. It survived the First and Second World Wars virtually intact, and the structure of the object is currently intact. The source materials mention only replacement of the roofing (in the years 1945-72), i.e., the removal of ceramic tiles, probably destroyed during the war, with zinc sheeting. Due to its historical nature, it was necessary to carry out a visual assessment of the facility, the condition of the facade and the structural system. An analysis of the condition of the building facade, located in the city centre directly among streets with heavy traffic, showed that the building has been exposed to pollutants from the air, i.e., car-exhaust fumes, and in previous years also industrial fumes containing sulfur and nitrogen oxides, as well as dust and soot from buildings using coal-fired stoves. This has resulted in dark or black deposit layers on the facades. The deposits have had a destructive effect on porous materials, e.g., bricks, plaster, and roof tiles .The facades show numerous cracks in the masonry and cavities. The plaster is in a very bad condition. Superficial defects in the plaster are visible, due to the large cavities and damaged flashings , which have caused individual layers to separate, exposing the bricks in permanently damp areas. The increased moisture from rainwater has caused biological corrosion of the plaster, manifesting itself in crumbling, delamination, and peeling, and consequently in the plaster falling off the brick wall .Corrosion of the reinforced concrete structure is visible on the eastern and southern facades. The layer around the reinforcement has been destroyed there, and the corroded rebar is visible .Numerous cavities and areas replastered with secondary cement mortar are visible on all facades of the building .The windows, a large percentage of which are the original ones, present a varying degree of preservation. Due to the effects of the weather conditions, the paint coatings have become cracked and chipped, and have been subject to numerous secondary coatings. There is visible unsealing of the joinery joints, damage to the wood and the paint coatings, as well as numerous paint coats that are clearly detached from the wooden structure of the windows.The exterior stairs, on the eastern and southern facades, were constructed of red sandstone. Their surface shows damage and cavities, and there are mosses, algae, and lichens due to the long-term presence of moisture .The architectural details on the building’s facades are very rich and varied. In terms of their construction material, they can be divided into two groups. The first group includes moldings made of cement–lime mortar, and the second group consists of reliefs made of artificial stone (gypsum mortar). The architectural details are in a good state of preservation. The top surfaces of the cement–lime mortar reliefs show superficial cavities in the plaster, damp spots, and cracks.The state of preservation of the eastern and southern facades of the building is satisfactory, while on the western side (the side of the courtyard) the facade presents a poor state of preservation.An external visual inspection of the condition of the structure indicated damage to the building’s facade. The cause of the damage shown in the example 2. An important part of concrete diagnostics, especially in a more than 100-year-old structure, is the evaluation of its physical–mechanical and durability characteristics, including tests of the progress of carbonation in floor slabs. An important part of the material diagnostics is an assessment of their condition within the structure. Progression of corrosion processes depends primarily on the internal structure of the medium, namely the hardened structure of concrete, and the amount of moisture it contains . CorrosiSince the building required upgrading after such a long period of use, a study was conducted to determine the condition of the structural components. This paper presents the test results forthe floor slab components. A compressive strength test and a saturation test were carried out on samples taken using a core drill. Six core borings were taken from each floor slab on every floor. Since the building is supervised by a conservation officer, the opportunity to take samples was limited to only the selected sites that were to be demolished. The locations where the concrete core samples were taken are shown in The tests were conducted on core samples of diameter 100 mm and length 100 mm. The number of borehole samples that may be taken from historic structures may be limited; however, according to ,20, it cThe weight and volumetric absorption and the bulk density of the concrete were determined for the core samples, according to standards . Weight water absorption as the ratio of the mass of water absorbed to the mass of the dry material sample was determined using Formula (1):Volumetric water absorption as the ratio of the volume of water contained in the tested sample of material to the volume of this sample in a dry state, was calculated according to (2):The open porosity test was performed in accordance with the standard . The tesThe depth of carbonation was measured on fresh fractures of core borehole samples by spraying a phenolphthalein solution, according to standard . The samples for phase composition analysis were crushed and the coarse aggregate was separated. The mineral composition of the aggregate itself was determined. Materials for analysis in the form of powder were ground manually in an agate mortar to a size less than 63 μm. Tests were conducted in an Empyrean X-ray diffractometer  using a Cu lamp and an X’Celerator detector in the 2θ angle range from 5 to 75°2θ. The ICDD PDF-2 database was used as a reference database for the diffractogram analysis . The average results of the determination of physical and mechanical characteristics performed on the samples are shown in A ≤ 4% [The concrete samples taken from individual floor slabs had very similar physical and mechanical characteristics. The weight absorption was in the range of 3.03 to 3.30% and the volumetric absorption was in the range of 7.43 to 8.00%. The concrete from floor slab 2 had the lowest weight absorption. The literature recommends limiting the weight absorption of concrete to a certain level wA ≤ 4% ,27 3. The open porosity of the concrete samples varied, ranging from 10.70 to 12.04%. The air content of the different concrete samples, as determined by measuring the open porosity, was similar in the range 3.26 to 4.03%. The results obtained may indicate different degrees of porosity in the analyzed concrete samples.The concrete in the analyzed floors showed a similar bulk density of about 2500 kg/mThe average compressive strength (for three samples) determined for the three analyzed floors was in the range of 27.5 to 36.5 MPa. The relationships between the weight absorption and the compressive strength were determined. The results of the analyses are shown in The weight absorption of the concrete samples from floor slabs 1 and 2 was similar, and ranged from 2.8 to 3.2%, but the samples had different compressive strengths. The concrete samples from floor 3 had the highest compressive strength and weight absorption among all the samples.To determine the corrosion hazard to the reinforcing steel, the degree of carbonation of the concrete was measured and the phase composition was determined by X-ray diffraction.The tests showed that the thickness of the near-surface layer of concrete that was not stained in the phenolphthalein test reached 3 mm in the top part of the floor slab, while the depth of concrete carbonation in the bottom part of the floor slab was 10 mm . The topThe analyzed concrete consists of a cement binder and natural aggregate, showing variations under macroscopic evaluation. An X-ray analysis indicated that the concrete was composed of aggregate from sedimentary rocks, mainly limestone , feldspar (anorthoclase), and quartz. Clay minerals were identified, i.e., illite, found in small amounts, as well as muscovite, a common rock-forming mineral .Analysis of the phase composition of the cement slurry demonstrated the presence of polymorphic varieties of calcium carbonate, i.e., calcite, vaterite, and aragonite .2 with portlandite, both of which are formed in carbonated concrete, and transforms into calcite [Vaterite is reported to be a product of the reaction of CO calcite . DespiteDifferences in the intensity of the reflections of portlandite, vaterite, and calcite were observed. The intensity of the portlandite reflections was lower in the concrete taken from the bottom layer of the floor slab, with simultaneously less intense vaterite reflections and higher intensity of calcite reflections comoared with the concrete from the top layer of the floor slab. The results indicate a higher degree of concrete carbonation processes in the bottom layer of the floor slab.A former school building, the German Reformed Gymnasium, located in the downtown area of Łódź, at 65 Tadeusza Kościuszko Avenue, is an example of a building with features of Berlin Modernism and elements of Art Nouveau. The state of preservation of the southern and eastern facades of the building is satisfactory, while on the western side (with the courtyard side) the facade presents a poor state of preservation. An important part of the diagnostics of reinforced concrete structures of historical value is an assessment of the corrosion hazard to reinforcing steel. The tests and measurements that were carried out indicate that the floor slabs are in a good condition despite their long service life. From the point of view of the durability of the reinforced concrete structure, the process of carbonation is not advanced, as evidenced by the comparatively thin near-surface layer of concrete that was unstained in the phenolphthalein test. It should be noted that the thickness of the carbonated layer depends on time. According to the Verbeck relationship cited by Kurdowski, the thickness of the layer after 100 years can be about 5 cm .The slow progression of carbonation processes in the floor slab concrete is also indicated by the phase composition of the cement slurry, in which portlandite was detected by X-ray radiography despite the concrete’s age of 100 years. In addition, low values of absorbability by weight (in the range of 3.03 to 3.26), under 5% in all the floor slabs tested, testify to the tightness and high homogeneity of the concrete structure.Nowadays, when designing concretes, the type of structural component and the classes of exposure affecting the concrete are taken into account. Analysis of the strength properties of the tested concrete and the contemporary requirements  indicateBased on the test results obtained, it can be concluded that this concrete produced more than 100 years ago is of high quality.