This study investigates the interface bond strength and anchorage performance of steel bars within prefabricated concrete. Twenty-two specimens were designed and manufactured to study the interface bond behavior of deformed and plain steel bars under a larger cover thickness. Diameter of steel bars, strength grade of concrete, and anchorage length were considered influential factors. The finite element method (ABAQUS) was used for the validation of experimental results. The interface bond’s failure mechanism and the anchorage length in the prefabricated concrete under different concrete strength levels were explored and compared to national and international codes. A suitable value of the basic anchoring length for the prefabricated structure was recommended. The results show that the interface bond strength of prefabricated bridge members is directly proportional to the strength grade of the concrete, inversely proportional to the reinforcement diameter, and less related to anchorage length. The effect of the cover thickness of the surrounding concrete is negligible. Conversely, the bearing capacity of prefabricated bridge members depends on the strength of the concrete, the diameter of the steel bar, and the anchorage length. Furthermore, it is concluded that the mechanical bond strength accounts for 88% of the bond strength within prefabricated concrete. In recent years, prefabricated reinforced concrete structures are widely used to construct commercial buildings, temporary safety protection structures, and large and medium-size bridges. The prefabricated structure has the advantages of a short construction periods, industrialized production, high dimensional accuracy, and less environmental pollution. The sufficient bond strength and anchorage performance of steel bars within prefabricated concrete are the key to ensure the service performance of the structure ,2,3.At present, the design of the anchorage length of the connecting reinforcement within the prefabricated concrete structure is usually considered according to the relevant provisions of the cast-in-place structure. However, due to the different characteristics of the prefabricated connection, the thickness of the protective layer of the connecting reinforcement is generally more than 50 mm larger than that of the cast-in-place structure and so the influence of the thickness of the protective layer can be ignored in the calculation of the anchorage length and interface bond strength. Presently, the existing relevant specifications of the assembled concrete structure still utilizes the relevant provisions of the cast-in-place structure for the anchorage length of the connecting steel bars, which is not suitable for calculating the bond strength and anchorage performance of the prefabricated assembled bridge with large protective layer thickness.There are only a few publications for the case of prefabricated concrete that have been published; this is the motivation for conducting the presented research. For the general case of a bond between reinforcement and concrete, however, much work has been conducted and reported in publications; the most relevant is mentioned here and shortly discussed. Steel bars have a significant effect on the mechanical properties and bond strength of concrete . Li et aThe experimental study analyzes the compressive bond anchorage properties of 500 MPa steel bars in concrete. Five influence factors, including concrete strength, the steel bar’s diameter, concrete cover, embedment length, and transverse reinforcement, were considered. The result shows that the influence of the surrounded concrete cover thickness on compressive bond strength is more than the steel bar’s diameter . Saeed eBond performance between concrete and steel bars was examined under corrosion level and temperature and it was found, from the study, that bond strength was influenced by temperature and corrosion . John etAnchorage and pull-out behavior depend on the geometry of steel fibers and is also related to the characteristics of the matrix . The forMuch numerical research has been carried out on the stochastic character of concrete ,19,20,21It is concluded from the literature review that research on the bond strength and anchorage performance of reinforcement-concrete used in prefabricated bridges is still limited in number. There are still some differences in the design standards of the assembly type concrete and anchoring. However, due to the differences in test conditions and test design set by different scholars, the current test results are relatively discrete and the conclusions are quite different. Moreover, the influence of the failure mode on the interface bond strength calculation has not been clearly distinguished in the existing experimental studies. The complete interface failure method was used to calculate the bond strength, leading to the smaller calculated value of bond strength between steel and prefabricated concrete. A high value of the anchorage length is not suitable for the design and construction of prefabricated bridges. Existing codes for prefabricated structures stipulate the anchorage length of a post-cast straight anchor connecting steel bars of precast concrete members (JGJ 145-2004) (GB 50010-20118) ,24. The This study investigates the influential factors that affect the interface bond strength and the anchorage performance of steel bars within prefabricated concrete. Twenty-two specimens were manufactured for the pull-out test by utilizing a larger cover thickness. Based on experimental results and finite element simulation, the failure mode, ultimate load, the load-displacement curves, and the effect of the different influential factors on interface bond strength and anchorage performance of steel bars within prefabricated concrete were analyzed.Furthermore, the bond strength calculation formulas and anchorage length for the steel bars within prefabricated concrete were fitted and derived. The national and international codes for the anchorage length and interface bond strength of steel bars within prefabricated concrete under different concrete strength levels were compared and analyzed. Recommended values of bond strength and the anchorage length of steel bars in the design of prefabricated bridges are given.Twenty-two reinforced concrete specimens were designed and manufactured for pull-out tests using larger cover thicknesses are shown in Hot-rolled Ribbed Bar (HRB400)  with theThe tensile test was carried out according to the Chinese code ,28 out oA bolt comprehensive parameter tester was used as a loading device. An intelligent digital pressure gauge indicates the load’s value and the loading end displacement was measured by electronic displacement, as shown in The failure mode and ultimate load of each specimen in the pull-out test are shown in Test results of all the specimens show the tensile failure of reinforcement. The ultimate load difference of C50 and C30 indicates that, for prefabricated concrete members with larger cover thicknesses, the concrete strength grade and reinforcement diameter significantly influences the ultimate load of the bond interface between reinforcement and concrete. The ultimate load of the ribbed steel bar specimen C50-20-300 is 8.5 times higher than that of the plain steel bar specimen C50-ø20-300.It can be estimated that the mechanical interlocking force accounts for about 88% of the bond strength between deformed steel bars and concrete. In addition, by comparing the failure modes of different specimens, it is concluded that, with the decrease in concrete strength and the increase in steel bar diameter, the failure mode of specimens gradually changes from the tensile failure of reinforcement to pull-out failure.The load-displacement curve from the experimental results is drawn and demonstrates the tensile failure of the specimen, as shown in Test specimens were loaded three times. At the first stage of loading, the specimen’s ductility was large and there is a yield strengthening stage before the steel bars yield. In the second stage of loading, when the load value reaches its maximum value of the first load the steel bars begin to yield and there is a longer yield deformation stage. At third stage of loading, when the load reaches the yield value the steel bars break and there is no obvious post-yield strengthening stage. The diameter of the reinforcement determines the ultimate bearing capacity of the specimen and it possesses a positive linear correlation with the cross-sectional area of the reinforcement.There were two main failure modes of the steel bar pull-out specimens in prefabricated concrete structures: reinforcement pull-out failure and tensile failure of the steel bar.(1)Pull-out failure of reinforcement.When the anchorage is insufficient, the pull-out force was greater than the bond interface bearing capacity of reinforced concrete and the failure of the bond interface occurs in the specimens. It can be seen from (2)Tensile failure of the steel bar.As shown in The bond interface between reinforcement-concrete was to be debonding and bond stress decreases between plain reinforcement and concrete with the increase in reinforcement diameter. Comparing the ultimate load of C50-8-300 and C50-G8-300 specimens with C50-20-300 and C50-G20-300, it is found that the interface bond strengths of plain steel bar are mainly composed of chemical bond strength and friction force between reinforced concrete, the mechanical interlocking force of bond interface is small or negligible, the bearing capacity of the bond interface between the plain steel bar and concrete is weak, and the ultimate load is far less than that of the deformed steel bar.The finite element model (ABAQUS) of reinforced concrete pull-out specimens was established to simulate the interface bond and anchorage characteristics of steel bars within prefabricated concrete. In order to ensure the accuracy of the model and improve the calculation efficiency, the bilinear axisymmetric quadrilateral reduced integral element CGAX4R was used for both reinforcement and concrete. The mesh density of the bonding interface, reinforcement axis, and concrete edges were 0.5 mm, 0.5 mm, and 2 mm, respectively. By establishing the surface size of the transverse rib, the influences of the artificial definition of interface elements on the analysis results were reduced. The model diagram and mesh generation are shown in The detailed characteristics of the bond interface between steel and concrete were considered. The normal behavior of the contact surface was simulated by “Hard Contact” and implemented by the Classical Lagrange Multiplier Method.The transmitted compressive stress between the contact surfaces was unlimited. When the pressure on the contact surface becomes negative or zero, the two contact surfaces were separated. The tangential behavior was simulated by “Penalty Friction.” In the Classical Coulomb Friction Model, the critical friction stress depends on the contact pressure and the elastic slip was allowed on the contact surface. Assuming that the friction coefficient μ between the contact surfaces was the same 0.1 , axial sIn order to study the interface bond strength and anchorage performance between the reinforcement and the concrete of the prefabricated bridge, the larger thickness of the protective layer was adopted according to the structural characteristics. The cross-sectional dimensions of the specimens were 300 mm × 300 mm according to the prefabricated structure specification, as per experimental design. The test parameters include the diameter of reinforcement, concrete strength, and anchorage length. The concrete strength grade was C30 and C50. The reinforcement diameter was 12 mm, 16 mm, and 20 mm and the anchorage length was 150 mm, 200 mm, and 300 mm, respectively.In order to simulate the failure and crack development of the bond interface of reinforced concrete, the elastic-plastic damage constitutive model was adopted for concrete, which can be used to observe and analyze the development law of cracks by using the cloud diagram of concrete tensile damage (DAMAGET). The pull-out phenomenon of reinforced concrete’s bonding interface was observed through concrete compression damage (DAMAGEC) . The conThe constitutive model can be expressed in the following:In the simulation of reinforced concrete structure, the interface effect between reinforcement and concrete (such as bond-slip and locking behavior) was simulated by introducing “Tensile Hardening” into the concrete model. The tensile hardening data were defined according to the cracking strain According to the definition of compression hardening, the hardening data are defined according to the inelastic strain Taking C50-12-300 as an example, the finite element model was established. According to the results of finite element analysis, the load-displacement curve of the specimen was extracted and compared with the experimental result curve, as shown in Under the pull-out load, the failure mode of the bond interface of reinforced concrete was the same as that of the experimental test results. The development of cracks cannot be directly observed in the test, but the development law of cracks can be observed and analyzed by using the cloud picture of concrete tensile damage (DAMAGET) in the finite element results. The failure appearance of the bonding interface of reinforced concrete can be observed through concrete compression damage (DAMAGEC) and the main failure mode is shown in The bond strength between reinforcement and concrete was determined by the properties of the interface between them, mainly including three factors: (1) the chemical bond force between the concrete substrate and the surface coating of reinforcement; (2) the relative sliding friction resistance between reinforcement and concrete along with the interface; (3) the mechanical interlocking force caused by the unevenness of the interface between reinforcement and concrete ,31,32,33At the initial loading stage, the bonding interface’s slip resistance was assumed by the chemical bonding force. In contrast, the mechanical interlocking force and friction force do not play a role temporarily. With the increase in the load, the chemical bond force fails, the bond interface slips relatively, the mechanical interlocking force and friction force begin to play a role, and the interface slip resistance is then provided by the oblique extrusion force between the transverse rib and the concrete. The axial component of the oblique extrusion force renders the concrete between the ribs subject to bending and shearing as a cantilever beam. The radial component of the oblique extrusion force results in the concrete around the reinforcement producing circumferential tensile stress. At this time, the concrete around the reinforcement was in a three-phase stress state. As shown in According to the results of the finite element analysis, the load-displacement curve of the specimen was extracted, the stress characteristics and failure mechanism of different stages were analyzed, and the influences of concrete strength grade, reinforcement diameter, and anchorage length on the load-displacement curve were compared.As shown in F is the pull-out load, d is the diameter of reinforcement, and Parametric analysis was carried out with concrete strength, reinforcement diameter, and anchorage length as a variable. The parameters, failure mode, ultimate load, and bond strength of each specimen are shown in The load-displacement curves of the experiment and FEM are compared, which are in good agreement and shown in The bond strength and ultimate load of specimens with different concrete strengths were compared in According to At the same time, it can be observed from The load-displacement curves of specimens with different reinforcement diameters were compared, as shown in From the previous analysis, it can be seen that the interfacial bond strength between the reinforcement and the concrete was mainly composed of the mechanical interlocking force. Shape parameters determine the mechanical interlocking force, the transverse rib’s height, and the spacing between the transverse ribs. The relative rib area (the ratio of the projected area of the transverse rib on the surface of the reinforcement to the reinforcement’s surface area) is taken as the index to evaluate the bond performance.When the relative rib area was larger, the bond performance should be improved. According to (GB 1499.2-2018) , the relWhen the diameter of reinforcement increases, the bond area and mechanical interlocking depth of reinforcement concrete increases. The increment of interface bearing capacity caused by the increase in bond area was more significant than the decrease caused by the decrease in bond strength under the same conditions. It was concluded that the ultimate load still increases with the increase in reinforcement diameter.It can be observed from the C50-16-100 specimen in By comparing the ultimate load of different specimens in For calculating the bond strength of steel bars within the concrete, scholars and codes have given the corresponding semi-empirical and semi-theoretical calculation formulas ,24,33,34Although the concrete cover in the fabricated structure is sufficient, the bond strength of the connecting steel bar will not be affected due to the too-small cover thickness. Therefore, the existing formula for calculating the bond strength of reinforcement is not suitable for the calculation of the connecting reinforcement of prefabricated bridges, which will lead to the length of the reserved connecting reinforcement of prefabricated components and will have an adverse impact on the construction difficulty and assembly accuracy control.Therefore, the existing formula of bond strength of steel bars is not suitable for calculating the connection reinforcement of prefabricated bridges, which will lead to the long reserved connection reinforcement of prefabricated members and will have an adverse effect on the construction difficulty and accuracy control of the assembly. According to the above analysis, the main factors influencing the bond strength of the prefabricated and assembled concrete structure connecting steel bars are the diameter of the reinforcement and the concrete’s strength. Therefore, the calculation formula of the bond strength of the connecting steel bar can be analyzed.d is the diameter of reinforcement and c is the thickness of the protective layer. The anchorage length is d is the diameter of the steel bar.In the The minimum error between the fitting formula and the test value in literature is 2%, the maximum error is 11%, and the overall error is 7%. The minimum error is 0.6%, the maximum error is 7%, and the overall error is 4%. The average ratio of the fitting value to the test value is 1.04, and the standard deviation and coefficient of variation of the ratio are 0.06 and 5.77%, respectively. The results show that the fitting values are in good agreement with the experimental values and the dispersion is low.2 between the fitting value and the measured value is 0.87, which indicates that the fitting value is in good agreement with the test value and that the dispersion is low.According to the principle of the complete failure of interface, the formula (6) was compared with this paper’s test results and the references ,36. The Taking the diameter of a steel bar, 25 mm, and the thickness of the protective layer 85 mm commonly used in the connection of prefabricated concrete members as an example, the differences of bond strength calculation between domestic and foreign codes and the fitting formula in this paper are shown in crl. When the anchorage length was less than the critical anchorage length, the pull-out failure occurs.When the bond strength between reinforcement and concrete was constant, the failure mode of the specimen was determined by the anchorage length. There was a critical length between the pull-out failure and the tensile failure of the reinforcement, which is called the critical anchorage length d is the diameter of reinforcement, and When the anchorage length was greater than the critical anchorage length, the bearing capacity of the bonding interface was greater than the ultimate tensile load of the reinforcement and the tensile failure of the reinforcement occurs. For the calculation of the critical anchorage length of the connection reinforcement in the prefabricated concrete structure, the calculation formula of the critical anchorage length can be given according to the bond strength calculation formula as follows:aL of reinforcement specified in the code is generally determined based on the critical anchorage length calculated by the corresponding bond strength and multiplied by the corresponding safety factor. The calculation formula of the basic anchorage length of reinforcement, the ratio aL/crL of the basic anchorage length, and the critical anchorage length in domestic and foreign codes are shown in The basic anchorage length n is the concrete strength coefficient, d, c is the thickness of the protective reinforcement layer. The basic anchorage length of the tensile reinforcement is A is a cross-sectional area of anchorage reinforcement. By comparison of critical anchorage length between domestic and foreign codes (f AS3600  specificgn codes  it can bCompared with the provisions of domestic and foreign codes for the basic anchorage length of reinforcement , it can In short, if the current domestic codes of prefabricated concrete will still be followed for the design of the anchorage length of prefabricated members, the size of prefabricated components will be too large, which will increase the construction cost and construction difficulty. Therefore, it is necessary to modify the anchorage length of the existing specifications. By considering the safety factor of 1.7 times, it is suggested to take it as 0.5 times the design value of the existing JTG3362-2018 .Calculation of the interface bond strength and anchorage length of steel bar within prefabricated concrete are usually considered according to the relevant provisions of the cast-in-place structure. However, the cover thickness is generally more than 50 mm larger than that of the cast-in-place structure. The complete interface failure method was used to calculate the bond strength, which may lead to the smaller calculated value of bond strength and high values of the anchorage length are not suitable for the design and construction of prefabricated bridges. Combined with experimental research and numerical analysis methods, this paper calculates the formula for interface bond strength and anchorage length by considering the main influencing factors and compares it with national and international codes. It is recommended that the basic anchorage length should be 18 d when the concrete strength grade is C35 or less and 15 d when the strength grade of the concrete is C40 or more.From experimental research and numerical analysis methods, the following calculations are made.It is concluded that the effect of cover thickness of the surrounding concrete is negligible for calculating interface bond strength within prefabricated structures.Compared with C50-12-300 and C50-12-400, the ultimate load of C50-16-300 and C50-16-400 increased by 85.7% and 49.9%, respectively, and 19.8% and 20.8%, respectively, when compared with C30-16-300 and C30-16-400, which indicates that for precast concrete members with larger cover thicknesses, the concrete strength grade and reinforcement diameter possess a significant influence on the ultimate load of the bond interface between reinforcement and concrete.The ultimate load of the ribbed steel bar specimen C50-20-300 is 8.5 times higher than that of the plain steel bar specimen C50-G20-300. It is estimated that the mechanical interlocking force accounts for about 88% of the bond strength between deformed steel bars and concrete.It is concluded that domestic codes design the anchorage length of prefabricated members, leads to a large size, and increases the construction cost and construction difficulty. It is suggested to take it as 0.5 times the design value of the existing JTG3362-2018  by consi


The purpose of this paper is to investigate the impacts of employees’ creative self-efficacy (CSE) and creative self-identity (CSI) on their innovative work behaviour (IWB), with the indirect effects of creative process engagement (CPE) and creative climate (CC). Following the deductive reasoning approach, the study was conducted on IT-based firms in Bangladesh. A total of 348 surveys were collected using a multi-item questionnaire. The collected data were then analyzed using structural equation modeling (SEM). The study reveals a significant relationship between CSE and IWB, and CSI and IWB. It further explores the significant mediating effects of CPE and the moderating effects of CC on CSE and IWB, and CSI and IB, relationships. Based on the premise of interactionist perspectives on creativity, this study contributes to the literature proposing a distinctive model comprising five variables to investigate employees’ IWB from a multi-level perspective. This integrated model, using predictors from multiple levels, supports the theoretical assumption that IB results from employees’ CSE, CSI, and, finally, CPE. Distinct from the other literature, the study also portrays the moderating and mediating impact of CC and CPE simultaneously. There is no other option in this competitive world but to ‘innovate or die’; if not, organizations will lose their position to competitors ,2,3. To Consequently, researchers have endeavored to incorporate complementary variables such as CSI, CC, CSE, CPE, and IWB . IndividThe study enriches the CPE and IWB literature by adding several valuable aspects with novel discernment ,16,17. FSelf-efficacy refers to the intrinsic confidence of individuals in achieving particular goals that shape both the quality and amount of the efforts they make towards successful attainment of specific tasks ,24. CSE Hypothesis 1 (H1).There is a significant relationship between creative self-efficacy and innovative behaviour.CSI is another dominant factor contributing to IWB, which refers to the self-perceived concept of creative identity . In thisHypothesis 2 (H2).There is a significant relationship between creative self-identity and innovative behaviour.Most studies in the field of creativity have focused on uncovering the IWB of organizations, rather than investigating the factors responsible for creativity and innovation ,39. ContHypothesis 3 (H3).Creative process engagement has a significant influence on employees’ innovative work behaviour.Employees with CSE tend to be more inclined towards achieving dynamic results than those with low CSE . Being iHypothesis 4 (H4).Creative process engagement mediates the relationship between creative self-efficacy and innovative behaviour.CSI enables employees to be confident about their abilities to be creative in their work and encourages them to engage in innovation in order to reaffirm the identity consigned to them . PossessHypothesis 5 (H5).Creative process engagement mediates the creative self-identity-innovative behaviour relationship.Adaptation to volatility and sustainable competitiveness requires a favourable working climate . CreatioThe underlying notion of CSI is that the expectations of an employee to gain personal and social advantage in an institutional setting encourage them to engage in the creative process . TherefoHypothesis 6 (H6).Creative climate moderates the influence of creative self-efficacy on creative process engagement.Hypothesis 7 (H7).Creative climate moderates the influence of creative personal identity on creative process engagement.The research employed a quantitative research method with a deductive reasoning approach ,62. The The study used the convenience sampling method to collect cross-sectional data because the convenience sampling is the viable solution when the population is homogenous in nature and questionnaires need to reach respondents quickly . The datThree hundred and forty-eight matched replies from both managers and employees were received after sending repeat emails and making personal visits, a response rate of 58%, which has been found to be acceptable in similar contexts ,8,18. DuThe study observed that the application of cross-sectional data curtailed the relationship strength of the structural connotation and inhibited the generalizability of the research outcomes . The aut2/df = 1.278; GFI = 0.927; CFI = 0.987; TLI = 0.986; SRMR = 0.031; RMSEA = 0.029) generated a better fit index than the 1-factor model  [Moreover, alternative model analysis was conducted to establish model-fit measures . A compa= 0.154) . Finally= 0.154) ,72. TherAll the measurement instruments were adopted from previous studies. Each item was measured on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The survey measure of Jaiswal and Dhar  was usedThe study aimed to analyze the collected data using different concurrent statistical tools such as IBM SPSS 23, IBM SPSS AMOS 23, and Microsoft Excel. Structural equation modeling (SEM) was used to justify the robustness of the outcomes by incorporating both the measurement model and structural model. Moreover, the study also included confirmatory factor analysis (CFA) and structural path estimates ,77.2/df = 1.278; GFI = 0.927; CFI = 0.987; TLI = 0.986; SRMR = 0.031; RMSEA = 0.029).In order to substantiate the measurement model, the item fitness suitability was assessed by examining the CFA along with the reliability and validity estimates. The estimation of reliability and validity can be seen in p = 0.002). Therefore, Hypothesis 1 is supported. In Hypothesis 2, it is proposed that there is a positive influence of CSI on IWB. Surprisingly, in contrast to the hypothesis, the estimation showed an insignificant relationship . Therefore, Hypothesis 2 is not supported. Moreover, in Hypothesis 3, it is proposed that CPE has a positive influence on IWB. In line with the hypothesis, it was found that there was a significant relationship between CPE and IWB . Therefore, the hypothesis is supported.With reference to Hypotheses 1 and 2, the direct influence of CSE and CSI on IWB was estimated. In The bootstrap method in PROCESS macros was run to examine the mediating effect of CPE on the influence of CSE and CSI on IWB. According to the criteria, the bias-corrected confidence interval must not contain zero for the specific indirect effect to have a mediation effect . Table 4p < 0.05). Moreover, we investigated the moderating effect of CC on the CSE-CPE (Hypothesis 6) and CSI-CPE (Hypothesis 7) relationships. The results shown in Furthermore, we plotted the estimates in p = 0.002). Hypothesis 2 proposes that CSI positively predicts IWB. Surprisingly, in contrast to the hypothesis and previous research findings, the estimate does not support the significant prediction  [p = 0.000) and consistent with the findings of previous studies [The prevailing evidence that reflects the existence of a significant relationship between CSE and IWB  is clear= 0.074) ,38. Medi= 0.074) . In Hypo studies ,18.This study explored an additional area by investigating the mediating effect of CPE on CSE and IWB, and on CSI and IWB. According to our results, CPE mediates the relationship between CSE and IWB, strengthening the notion that employees who are self-efficacious in terms of creativity tend to engage themselves in the creative process, which ultimately facilitates IWB . TherefoFurthermore, the mediating effect of CPE on the relationship between CSI and IWB is also statistically significant, which implies that self-identified creative individuals portray innovative behaviour by engaging themselves in the creative process . MoreoveThe study also sheds light on the moderating effect of CC on the relationships between CSE and CPE, and CSI and CPE. The results demonstrate that CC does not moderate the CSE-CPE relationship, thus indicating that CC is possibly not relevant to the study context. On the other hand, CC was also not found to significantly moderate the relationship between CSI and CPE. According to the results, individuals with creative identity do not associate themselves with the creative process more or less, given a favorable creative climate . The finIn the context of the 21st century, organizations need to invest a substantial proportion of their resources in the development of their employees, as well as fostering innovation through research in order to overcome the challenges of competition and to adjust to the knowledge- driven society. The outcomes of the study augment the existing pool of literature by adding several insightful contributions. First, in the field of creativity and innovation literature, most studies have striven to explore the independent role of CSE and CSI on CPE and IWB, but very few have explored the moderated and mediated impact of CC and CPE on the concerned relationship. Holistically, the study has endeavored to connect the integrative impact of CSE and CSI on IWB through the moderation and mediation mechanisms in the hypothesized relationship. Second, the role of creative individuals with efficacy and self-identity in creative behaviour in the south Asian context has not been portrayed adequately in the literature. This study, therefore, enhances the prevailing knowledge through its significant contribution to the creative literature. Third, the study has endeavored to demonstrate the application of the interactionist perspective from the employee viewpoint, with its multi-faceted implications in measuring employees’ IWB.Achieving organizational sustainability in today’s competitive atmosphere clearly necessitates the effective management of employees’ creativity, which eventually facilitates the innovative outcome of organizations. Most ICT firms are facing innovation challenges from their competitors due to the frequent changes in consumer preferences. Employees can play an important driving role in taking advantage of opportunities from market dynamics through their resilience and innovativeness . ManagerThough the use of both reports’ survey and other precautions during the data collection process prevents the estimated results from being affected by common method bias, the study also has some underlying limitations. First, the data were collected only from ICT firms listed in BASIS, which weakens the general applicability of the findings. Future researchers could minimize this problem by incorporating diverse organizations from varying industries in the data collection process. Second, in order to examine the causal inferences, the study used cross-sectional data to draw inferences, which may have generated erroneous causal relationships. Future studies could overcome this limitation by using experimental or longitudinal research paradigms . Third, Using the lens of multiple theories, the present study aimed to examine the predictors of IWB in a moderated mechanism with both rated samples from IT-related firms in an emerging country, Bangladesh. It also tested the mediating role of CPE in the CSE-IWE and CSI-IWB relationships in the light of multi-theory perspectives. The existing body of literature in the field of creativity will be strengthened by this research through its fascinating findings. The study reveals that CSE and CSI have unswerving impact on IWB. Moreover, it demonstrates the full mediating role of CPE on the CSE-IWB and CSI-IWB affiliations, while also revealing the insignificant moderating impact of CC on the CSE-CPE and CSI-CPE relationships. Subsequently, sustainable IWB is strengthened when CPE plays a dominant mediating role in the CSE-IWB and CSI-IWB relationships.


The range of environmental conditions in marine life is tremendous at different physico-chemical criteria . Since marine life has colonized all these ecosystems, the resulting marine biodiversity is complex to embrace in its entirety and has led to a prolific metabolic chemo-diversity. The global technological progresses  will provide access to this gold-mine of bioactive compounds ,2. ThereBy now, the world emergency in term of public health is to re-prime the antibiotic pipeline in order to renew the anti-infectious therapeutic arsenal and to fight against antimicrobial resistance (AMR). Indeed, AMR is now recognised as one of the world’s biggest economic and security threats . DespiteBy contrast, the preclinical pipeline is more dynamic and innovative. It includes 292 diverse antibacterial agents and a wider range of drug development projects. However, the success rate from preclinical stage to FDA approval is minimal. It has to be said that the antibiotic pipeline is almost dry. Antibiotic discovery has to be reinvigorated and marine biodiversity as an under-explored reservoir of bioactive compounds must play its full role in the race against time to tackle AMR.Pseudoalteromonas [Penicillium citrinum [Laurencia johnstonii [Sepia officinalis [This special issue, “Marine Antibiotics 2020”, gathers 5 publications, including a review, a communication and 3 research articles. The antimicrobial compounds described were characterized in the marine bacteria eromonas , the funhnstonii  and the icinalis . The auticinalis . Marine Drugs for their support and kind help.As guest editor, I hope that this special issue will illustrate the huge biotechnological potential of marine biodiversity. I would like to sincerely thank, of course, the authors, but also the reviewers who worked behind the scenes and without whom nothing would be possible, and finally, the editorial boards and the editorial office of


Antibiotic overprescribing is one of the main drivers of the global and growing problem of antibiotic resistance, especially in primary care and for respiratory tract infections (RTIs). RTIs are the most common reason for patients to consult out-of-hours (OOH) primary care. The COVID-19 pandemic has changed the way general practitioners (GPs) work, both during office hours and OOH. In Belgian OOH primary care, remote consultations with the possibility of issuing prescriptions and telephone triage were implemented. We aimed to describe the impact of COVID-19 on GPs’ antibiotic prescribing during OOH primary care. In an observational study, using routinely collected health data from GP cooperatives (GPCs) in Flanders, we analyzed GPs’ antibiotic prescriptions in 2019 (10 GPCs) and 2020 (20 GPCs) during OOH consultations (telephone and face-to-face). We used autoregressive integrated moving average (ARIMA) modeling to identify any changes after lockdowns were implemented. In total, 388,293 contacts and 268,430 prescriptions were analyzed in detail. The number of antibiotic prescriptions per weekend, per 100,000 population was 11.47 (95% CI: 9.08–13.87) or 42.9% lower after compared to before the implementation of lockdown among all contacts. For antibiotic prescribing per contact, we found a decrease of 12.2 percentage points (95% CI: 10.6–13.7) or 56.5% among all contacts and of 5.3 percentage points (95% CI: 3.7–6.9) or 23.2% for face-to-face contacts only. The decrease in the number of prescriptions was more pronounced for cases with respiratory symptoms that corresponded with symptoms of COVID-19 and for antibiotics that are frequently prescribed for RTIs, such as amoxicillin (a decrease of 64.9%) and amoxicillin/clavulanate (a decrease of 38.1%) but did not appear for others such as nitrofurantoin. The implementation of COVID-19 lockdown measures coincided with an unprecedented drop in the number of antibiotic prescriptions, which can be explained by a decrease in face-to-face patient contacts, as well as a lower number of antibiotics prescriptions per face-to-face patient contact. The decrease was seen for antibiotics used for RTIs but not for nitrofurantoin, the first-choice antibiotic for urinary tract infections. The start of the COVID-19 pandemic raised new and radical logistical and clinical challenges for primary care physicians, who were forced to respond promptly to a fast-changing environment ,3,4. OutAntimicrobial resistance and the widespread use of antibiotics is a major global health problem, and the antimicrobial stewardship principles remain important during this global pandemic . Before Early in the pandemic, the Belgian Antibiotic Policy Coordination Committee (BAPCOC) issued a recommendation on 23 March not to prescribe azithromycin or any other antibiotic for COVID-19-suspected cases in ambulatory care and to adhere to the Belgian antibiotic guidelines if choosing an antibiotic . SimilarThis observational study describes trends in the antibiotic prescribing of GPs for RTIs, before and after the start of the COVID-19 lockdown in Belgian OOH care, using routinely collected health data.The database contained 481,362 contacts from 2019 and 2020, of which 93,531 were excluded: 36,956 COVID-19-test contacts (solely contacts for testing reasons), 57,447 home visits and 668 because of a data collection problem. We included 388,293 contacts and 268,430 prescriptions: on average 0.69 (SD 0.89) prescriptions per contact, ranging from 1 to 12 prescriptions. Patients’ average age was 34.0 years (SD 23.5) and 54.1% were female. The study population before the start of the first lockdown (13 March 2020) was older (mean 36 years) and more often female (54.3%), compared to the population after implementation of the lockdown .We observed a drop  of 11.47 (95% CI: 9.08–13.87) or 42.9% in the number of antibiotic prescriptions per weekend, per 100,000 inhabitants, after compared to before the implementation of the lockdown. Amoxicillin and amoxicillin/clavulanate are the antibiotics most often used for RTIs. The observed decrease was larger for amoxicillin than for amoxicillin/clavulanate with 7.31 (95% CI: 5.67–8.95) or 65.2% and 2.20 (95% CI: 1.74–2.67) or 38.1% fewer prescriptions, respectively. The average number of antibiotic prescriptions for the period before and after lockdown can be found in We added the trend in UTI diagnoses per population of 100,000 and the UTI contacts, where nitrofurantoin was prescribed, per population of 100,000, which both stayed stable before and after the lockdown, as On average, the total number of contacts did increase after lockdown . This inAntibiotic prescribing per contact, both face-to-face and telephone contacts, decreased by 12.2 percentage points (95% CI: 10.6–13.7) or 56.5%, while it decreased by 5.3 percentage points (3.7–6.9) or 23.2% when only taking face-to-face contacts into account. The decrease in the total number of antibiotics prescribed during all face-to-face contacts could be attributed to the decrease of 42.8% for antibiotics prescribed per face-to-face contact and to the decrease of 57.2% for the number of contacts. Our findings show a remarkable decrease in the number of antibiotic prescriptions issued in OOH primary care after the implementation of the COVID-19 pandemic lockdown. When focusing on RTI-related contacts (excluding ear/throat infections), this decline is even more pronounced. We see a marked decrease in antibiotic prescriptions for RTI-related face-to-face contacts after the start of the first wave, only, for a small part, compensated by antibiotic prescriptions issued during telephone consultations. Only a limited number of antibiotic prescriptions were delivered during telephone contacts, which was a new service implemented after the start of the lockdown in 2020. In the summer of 2020, we see a partial recovery of antibiotic prescribing during OOH care, followed by a second decline during the second wave.The observed decrease in antibiotic prescribing during OOH care at the population level can be explained by both a decrease in the number of face-to-face contacts and a lower number of prescriptions per face-to-face patient contact, before versus after the implementation of lockdown.The observed decline in the number of antibiotic prescriptions after the start of the pandemic is even more pronounced when looking at the number of prescriptions for amoxicillin. Amoxicillin is the most commonly prescribed and first-choice antibiotic for most RTIs.Throughout Europe, primary care reorganized itself to provide primary care for patients with or without COVID-19 ,3,4. In During the first wave of the pandemic, there was no access to COVID-19 tests. Later, COVID-19 tests were available, but even then, the results would not yet be available during the patient contact . TherefoPatients’ threshold to consult their GP was potentially higher and reasons could be: the implementation of triage, the discouragement of patients to contact healthcare unless it was urgent, the discouragement of non-essential movements, the fear of patients to have a healthcare contact or the communicated pressure on healthcare by the media ,6,32,33.In primary care, findings are mixed, but in general, they indicate a decrease when it comes to the number of antibiotic prescriptions dispended . For exaWith all the challenges evoked by the outbreak of COVID-19, one must not forget the challenge of antibiotic resistance ,43. BactA strength of our study is the use of interrupted time series (ITS), which provides one of the strongest evaluative designs if randomization is not possible. Moreover, as our study was undertaken in a real-world setting, the use of ITS results in strong external validity . The useWe did not assess the appropriateness of prescribing, and it is unclear whether there was, for example, an undertreatment of infections or a rise in complications. Data were routinely registered by GPs and automatically extracted from the electronic health record. Although registration is required, we have to take into account a possible under-registration or incorrect registration by the GPs. It is not possible to document a shift of prescribing from OOH to in-hours with our current data set. Belgian pharmacy data show that there was an overall significant decrease in antibiotic prescribing in Belgium, but not for chronic medications .There was a lower age of patients presenting after the start of the lockdown. We did not compare the same age groups pre- and post-COVID-19; therefore we do not know what percentage of the difference can be explained by the difference in age.We were not able to control for potential seasonal effects, as data were only analyzed for one time unit  preceding the event. However, no clear patterns of seasonality could be distinguished from the graphical presentation of the data.We performed an observational study using health data routinely collected in 20 GPCs , covering a population of 3,162,345 , in rural as well as urban areas, situated in the northern Dutch-speaking part of Belgium (Flanders).In Belgium, the first wave of the COVID-19 pandemic occurred between 1 March and 21 June 2020 (with a peak in week 15) and the second wave between 31 August 2020 and 14 February 2021 (with a peak in weeks 45–46). The first lockdown was a strict lockdown with, amongst other restrictions, closure of all schools, closure of non-essential shops, a travel ban and mandatory teleworking, while for example, during the second lockdown, school contacts were only partially limited, travelling was limited, non-essential shops were closed again and teleworking was encouraged when possible.All variables were collected using routinely collected electronic health record data, extracted from the iCAREdata database ,48.All contacts with a GP, for patients with a national insurance number, presenting themselves at a participating GPC during weekends and bank holidays (from here onwards we refer to weekends and bank holidays as weekends) in 2019 and 2020 were studied. Home visits were excluded due to a lack of correct prescription registration. Starting in May 2020, most of the studied GPCs were offered a COVID-19 testing service by a nurse. These patients were either not ill (travel testing and contact tracing) or had a previous telephone consultation (the government did not recommend a face-to-face consultation). These testing contacts were excluded. Due to a data collection problem, the contacts during the weekend of 18 September 2020 were excluded as well.The units of analysis were contacts and prescriptions. Our study population corresponds with the Flemish (Dutch speaking part of Belgium) population for age and gender.The following variables were collected: timing of presentation (date and time), contact type , sex , age (years), diagnosis   and presThe following outcomes were studied: (1a) the number of systemic antibiotic prescriptions (ATC chapter J01) per weekend, per 100,000 inhabitants, for 2019 and 2020, (1b) the number of antibiotic prescriptions (ATC chapter J) per weekend, per 100,000 inhabitants, for 2019 and 2020, for RTI-unrelated contacts and for RTI-related contacts , (2) the number of amoxicillin and amoxicillin/clavulanate prescriptions per weekend, per 100,000 inhabitants , (3) the number of nitrofurantoin prescriptions per weekend, per 100,000 inhabitants (representing antibiotic use for non-respiratory infections) and (4) the number of antibiotic prescriptions per weekend, per number of face-to-face and telephone contacts.The outcome variables were rates of the number of prescriptions per weekend, per 100,000 population, for different groups of antibiotics: J01, amoxicillin (J01CA04), amoxicillin/clavulanate (J01CR02) and nitrofurantoin (J01XE01). Long weekends were readjusted based on the number of hours (84 vs. 60). Interrupted time series (ITS) analysis was used to quantify changes between the period before lockdown was implemented  and the period after implementation , while accounting for autoregression and controlling for age and gender. Auto-Regressive Integrated Moving Average (ARIMA) models were used as they accommodate continuous variables, such as the rates we used as outcome variables . To idenThe COVID-19 pandemic has led to an unprecedented drop in the number of antibiotic prescriptions in Belgian OOH primary care after the implementation of the lockdown, which can be explained by a decrease in the number of face-to-face contacts as well as lower antibiotic prescribing rates per face-to-face contacts. This result was seen for antibiotics used for RTIs but not for nitrofurantoin, the first-choice antibiotic for UTI.


Polycystic ovary syndrome (PCOS) affects up to 18% of reproductive-age females. The prevalence of obesity in PCOS patients reaches up to 80%, which is 2-fold higher than the general population.The present study aimed to compare the effectiveness of 55 pharmacological interventions across 17 different outcomes in overweight/obese PCOS patients with hyperandrogenism manifestations for both short- and long-term follow-ups. A comprehensive literature search was performed on PubMed, Scopus, Embase, Science Direct, Web of Science, and Cochrane CENTRAL for randomized controlled trials comparing any conventional pharmacological intervention as a monotherapy or a combination in overweight/obese patients with polycystic ovary syndrome and hyperandrogenism manifestations. Extracted data included three main parameters; I. Anthropometric parameters , II. Hormonal parameters , and III. Metabolic parameters . Critical appraisal and risk of bias assessments were performed using the modified Jadad scale, and the overall quality of this network meta-analysis was evaluated according to the CINeMA framework. We performed both a pairwise meta-analysis and a network meta-analysis to evaluate the effect sizes with 95% CI, and we calculated the surface under the cumulative ranking curve (SUCRA) for each intervention.th 2021 retrieved 23,305 unique citations from searching six electronic databases. Eventually, 101 RCTs of 108 reports with a total of 8,765 patients were included in our systematic review and multi-treatments meta-analysis. 55 different interventions were included: 22 monotherapies, and 33 combinations. The two-dimensional cluster ranking of the average SUCRA values for metabolic and hormonal parameters with significant estimates revealed flutamide  as the highest and rosiglitazone  as the lowest, in terms of the overall efficacy in reducing weight and hyperandrogenism. However, cyproterone-acetate+ethinylestradiol exhibited a higher ranking in improving hormonal parameters (71.1%), but even a lower-ranking regarding metabolic parameters (34.5%).Our final search on May 15Current evidence demonstrated the superiority of flutamide in improving both metabolic and hormonal parameters, and the higher efficacy of cyproterone-acetate+ethinylestradiol only in improving hormonal parameters. Nearly all interventions were comparable in female hormones, FGS, HDL, glucose, and insulin levels improvements. Polycystic ovary syndrome (PCOS) is a complex endocrinal disorder affecting up to 18% of young females . While pThe pathophysiology of PCOS is still unclear, but evidence suggests a mixture of environmental factors and genetic susceptibility . One of Pharmacological interventions mainly involve: oral contraceptives, antiandrogens, oral hypoglycemics, insulin sensitizers, ovulation induction agents, and conventional obesity treatments . The recStill, long-term use of these agents increases the risk of venous thrombosis and disrupts the metabolic parameters . HypotheGiven that the symptoms upon diagnosis are usually confined to irregular menstruation or infertility, physicians may disregard the possible long-term metabolic and anthropometric disturbances . SubsequAccordingly, we performed this network meta-analysis to compare the effectiveness of 55 pharmacological interventions across 17 different clinical and biochemical outcomes in overweight PCOS patients for both short- and long-term follow-ups.We followed the PRISMA statement guidelines —PRISMA  during tth 2021 and May 15th 2021 covering all selected databases  OR (polycystic ovary syndrome[Title/Abstract])) OR (PCOS[Title/Abstract])) OR ) OR ) OR (anovulation[MeSH Terms])) OR (anovulation[Title/Abstract])) OR (amenorrhea[MeSH Terms])) OR (amenorrhea[Title/Abstract])) OR (ovarian dysfunction[Title/Abstract])) OR (ovarian failure[Title/Abstract])) OR (Oligo-amenorrhea[Title/Abstract]))) AND (metformin[Title/Abstract])) OR (metformin[MeSH Terms])) OR (liraglutide[Title/Abstract])) OR (orlistat[Title/Abstract])) OR (orlistat[MeSH Terms])) OR (inositol[MeSH Terms])) OR (inositol[Title/Abstract])) OR ) OR ) OR (Ethinyl estradiol[MeSH Terms])) OR (Ethinyl estradiol[Title/Abstract])) OR (ethinylestradiol[MeSH Terms])) OR (ethinylestradiol[Title/Abstract])) OR (diane[Title/Abstract])) OR (cyproterone[MeSH Terms])) OR (cyproterone[Title/Abstract])) OR ) OR ) OR (OCP[Title/Abstract])) OR (CC[Title/Abstract])) OR (marvelon[MeSH Terms])) OR (marvelon[Title/Abstract])) OR (desogestrel[MeSH Terms])) OR (desogestrel[Title/Abstract])) OR (yasmin[Title/Abstract])) OR (drospirenone[Title/Abstract])) OR (letrozole[MeSH Terms])) OR (letrozole[Title/Abstract])) OR (FSH[Title/Abstract])) OR (hMG[Title/Abstract])) OR (menotropin[MeSH Terms])) OR (menotropin[Title/Abstract])) OR (pioglitazone[MeSH Terms])) OR (pioglitazone[Title/Abstract])) OR (rosiglitazone[MeSH Terms])) OR (rosiglitazone[Title/Abstract])) OR (troglitazone[MeSH Terms])) OR (troglitazone[Title/Abstract])) OR (litraglutide[Title/Abstract])) OR (flutamide[MeSH Terms])) OR (flutamide[Title/Abstract])) OR (clomiphene[MeSH Terms])) OR (clomiphene[Title/Abstract])) OR (clomifene[Title/Abstract])) OR (clomifene[MeSH Terms])) OR (chlormadinone[MeSH Terms])) OR (chlormadinone[Title/Abstract])) OR (gonadotropin[Title/Abstract])) OR (gonadotropin[MeSH Terms])) OR (simvastatin[MeSH Terms])) OR (simvastatin[Title/Abstract])) OR (atorvastatin[Title/Abstract])) OR (atorvastatin[MeSH Terms])) OR (acarbose[MeSH Terms])) OR (acarbose[Title/Abstract])) OR ) OR (anastrozole[MeSH Terms])) OR (anastrozole[Title/Abstract])) OR (clomiphene citrate[Title/Abstract])) OR (clomiphene citrate[MeSH Terms])) OR (exenatide[MeSH Terms])) OR (exenatide[Title/Abstract])) OR (folic acid[Title/Abstract])) OR (folic acid[MeSH Terms])) OR (pure follicle-stimulating hormone[MeSH Terms])) OR (pure follicle-stimulating hormone[Title/Abstract])) OR ) OR ) OR (letrozole[MeSH Terms])) OR (letrozole[Title/Abstract])) OR (liraglutide[Title/Abstract])) OR (medroxyprogesterone acetate[MeSH Terms])) OR (medroxyprogesterone acetate[Title/Abstract])) OR (N-acetyl cysteine[Title/Abstract])) OR (N-acetyl cysteine[MeSH Terms])) OR (pioglitazone[MeSH Terms])) OR (pioglitazone[Title/Abstract])) OR (rosiglitazone[Title/Abstract])) OR (rosiglitazone[MeSH Terms])) OR (sibutramine[Title/Abstract])) from inception till 28 August 2020 and search update was conducted on March 28atabases . All pubWe included all studies satisfying the following criteria:2) with polycystic ovary syndrome defined by Rotterdam, NIH, or Androgen Excess Society criteria for PCOs with a mutual presentation of obesity and hyperandrogenism across criteria;  Intervention and Comparison: any conventional pharmacological intervention; (4) Outcomes: Extracted data included three main parameters; I. Anthropometric parameters , II. Hormonal parameters , and III. Metabolic parameters , and (5) Study design: blinded randomized controlled trials (RCTs). We excluded the following: 1) non-randomized trials, 2) open-label and cross-over studies 3) surgical, herbal, and supplemental interventions, and 4) studies whose data were unreliable for extraction and analysis including post hoc analyses and preliminary reports. Duplicates were removed and retrieved references were screened in two steps: the first step was to screen titles/abstracts for matching our inclusion criteria and the second step was to screen the full-text articles of eligible abstracts for eligibility to meta-analysis. Given the challenges in this unique design of the network-meta analysis, we included comparable RCTs in their methodology and quality to guarantee the assumption of transitivity and the lowest possible heterogeneity. We analyzed only well-designed blinded RCTs that applied globally recognized diagnostic criteria for PCOS. Regarding the BMI, we considered both the mean and the standard deviation (SD) in determining the eligibility of the studies’ population. For instance, studies that had an average BMI above 25 but had a standard deviation that crosses the 25-mean into a lower value for some patients were excluded. Also, we separated studies with short-term follow-ups from those with long-term follow-ups in the statistical combinations. Eventually, each included intervention was administered as primary therapy in its original study. So, a critical distinction has to be made between a tertiary/off-label use of a drug and the primary use of the same drug.Population: overweight/obese patients , so it would not be fair to compare these agents to each other regarding the same outcome. With that in mind, we had two prospects when planning for this study. Firstly, we could have focused the study on the used interventions a particular PCOS phenotype  only. Even though this option would have been much simpler to handle, the work would have contributed more to widening the current knowledge gap. Given that PCOS has a progressive nature, it does not restrain itself to the presented phenotype, let alone that the borders that should determine different managements between various phenotypes are inevitably interleaving -implying a dire need for a much comprehensive investigation. Alternatively, we selected 17 measurable parameters that are mutual between various phenotypes and grouped them into anthropometric, metabolic, and hormonal parameters. Following, we examined the effect of each intervention on each parameter of these 17 parameters . That is how even when intervention X has primary use for the first five parameters (with a secondary or tertiary effect on the rest) and intervention Y has primary use for the last five parameters (with secondary or tertiary effect on the rest), we can still draw an overall performance across parameters between the two interventions in an objective manner. Eventually, the data of this extensive analysis would help in drawing step-wise management for different phenotypes based on the best performing intervention across the prioritized parameters of that phenotype . This algorithm will further promote the clinical practice to be more data-driven instead of theory-driven regarding PCOS management.Eight independent authors extracted the relevant data from the included studies, four authors  performed the literature search and validation, then, another four authors  re-performed the search and validation. Disagreements were resolved through discussion and consensus among the reviewers. The screening and de-duplication were conducted through Endnote X7 and Microsoft Excel 2016. The extracted data included the following:Baseline characteristics Study outcomes: I. Anthropometric parameters, II. Hormonal, and III. Metabolic parameters -as previously defined.Critical appraisal and risk of bias assessments of the included RCTs were performed using the modified Jadad scale from Oxford University . This eiStatistical analyses were performed using Stata 16.0 software. First, we conducted a pair-wise meta-analysis employing the IVhet random-effects model. All reported units were converted to standard SI units. All data were continuous (means and standard deviations "SD") and were pooled as weighted mean differences (MD) with 95% confidence intervals. Missing SD was calculated from the standard error or 95% CI or range according to Wan et al.  or obtaiSecond, a network meta-analysis was performed with a frequentist framework to compare different interventions that have no direct comparisons. We applied the node-splitting and loop-specific approaches to verify inconsistencies across the network, where a p<0.05 indicated a significant inconsistency. When no significant inconsistency was detected, we employed a consistency model; otherwise, an inconsistency model was adopted. We also utilized a global inconsistency test based on a random-effects design-by-treatment interaction model. Additionally, the surface under the curve ranking area (SUCRA) was calculated to rank different interventions for each outcome. Further, a meta-regression was conducted to examine the relationship between anthropometric, hormonal, and metabolic parameters.Our updated search retrieved 23,305 unique citations from searching electronic databases. Following title and abstract screening, 408 full-text articles were retrieved and screened for eligibility. Of them, 307 articles were excluded, and 101 RCTs , 111–12055 different interventions were included: 22 monotherapies, and 33 combinations. The monotherapies included acarbose (ACR), alfacalcidol (ALF), anastrozole (ANZ), clomiphene citrate (CC), exenatide (EXN), folic acid (FA), flutamide (FLT) pure follicle-stimulating hormone (FSH), human menopausal gonadotropins (HMG), inositol (INS), letrozole (LET), liraglutide (LIR), metformin (MET), medroxyprogesterone acetate (MPA), N-acetyl cysteine (NAC), orlistat (ORL), pioglitazone (PGZ), placebo (PLC), rosiglitazone (RGZ), sibutramine (SBT), simvastatin (SMV), and troglitazone (TGZ).The combinations included acarbose+clomiphene citrate (ACR+CC), alfacalcidiol+metformin (ALF+MET), atorvastatin+metformin (ATR+MET), bromocriptine+clomiphene citrate (BRM+CC), bromocriptine+metformin (BRM+MET), clomiphene citrate+dexamethasone (CC+DEX), clomiphene citrate+ketoconazole (CC+KTZ), clomiphene citrate+l-carnitine (CC+LC), clomiphene citrate+l-carnitine+metformin (CC+LC+MET), clomiphene citrate+metformin (CC+MET), clomiphene citrate+N-acetylcysteine (CC+NAC), clomiphene citrate+rosiglitazone (CC+RGZ), chlormadinone acetate+ethinylestradiol (CHA+EE), cyproterone acetate+ethinylestradiol (CPA+EE), cyproterone acetate+ethinylestradiol+metformin (CPA+EE+MET), cyproterone acetate+ethinylestradiol+metformin+orlistat (CPA+EE+MET+ORL), cyproterone acetate+ethinylestradiol+orlistat (CPA+EE+ORL), cyproterone acetate+ethinylestradiol+spironolactone (CPA+EE+SPR), dexamethasone+metformin (DEX+MET), desogestrel+ethinylestradiol (DGT+EE), drospirenone+ethinylestradiol (DPN+EE), drospirenone+ethinylestradiol+metformin (DPN+EE+MET), ethinylestradiol+flutamide+levonorgestrel (EE+FLT+LVT), ethinylestradiol+gestodene (EE+GTN), ethinylestradiol+metformin+norgestimate (EE+MET+NRG), ethinylestradiol+norgestimate (EE+NRG), folic acid+inositol (FA+INS), flutamide+metformin (FLT+MET), human menopausal gonadotropins+ leuprolide (HMG+LPR), inositol+monacolin k (INS+MNK), letrozole+metformin (LET+MET), metformin+rosuvastatin (MET+RSV), and metformin+simvastatin (MET+SMV).A network map was formed to visually display the size of studies involved in each direct comparison for each outcome , and a sAll of the included studies were randomized, blinded, and were treated as an intention to treat (ITT) analysis; thus, exhibiting a low risk of bias. The funnel plot was visually symmetrical , indicatWe performed a pairwise meta-analysis for RCTs that compared the same interventions employing the random-effects IVhet model. The results of these analyses are displayed in For BMI, only the following comparisons revealed significance: CPA+EE+MET+ORL# vs. CPA+EE# , CPA+EE+MET+ORL# vs. CPA+EE+ORL# , FLT vs. MET , FLT vs. PLC , and FLT+MET vs. PLC . Pooled analysis was homogenous .(mIU/ml), only the following comparisons revealed significance: MET vs. PLC . LIR was inferior to PLC in reducing LH levels . Pooled analysis was moderately heterogeneous , and heterogeneity did not resolve after further sensitivity analysis.For LH (nmol/L), only the following comparisons revealed significance: CPA+EE vs. MET , CPA+EE vs. RGZ , D(ng/dl), only the following comparisons revealed significance: CPA+EE vs. MET , DGT+EE vs. MET , DGT+EE vs. PLC , and CC+DEX# vs. PLC . Pooled analysis was homogenous .For Total Testosterone (μg/dl), only the following comparisons revealed significance: FLT vs. MET , FLT vs. PLC , INS# vs. PLC , and MET+RSV# vs. MET# . Pooled analysis was homogenous .For DHEAS (mg/dl), only the following comparisons revealed significance: MET+SMV# vs. MET# . Pooled analysis was homogenous .For Total Cholesterol (mg/dl), only the following comparisons revealed significance: INS+MNK vs. INS , INS+MNK vs. MET , MET vs. DGT+EE , MET# vs. PLC , MET+SMV# vs. MET# , and ORL# vs. PLC . Pooled analysis was moderately heterogeneous , and heterogeneity did not resolve after further sensitivity analysis.For LDL (mg/dl), only the following comparisons revealed significance: FLT vs. MET , FLT vs. PLC , and MET+RSV# vs. MET# . DPN+EE was inferior to RGZ in reducing Triglycerides levels . Pooled analysis was homogenous .For Triglycerides (mg/dl), only the following comparisons revealed significance: MET# vs. PLC , and MET# vs. ORL# . Pooled analysis was homogenous .For Fasting Glucose (pmol/L), only the following comparisons revealed significance: CC+MET vs. CC , and MET vs. CC . CPA+EE and DPN+EE were inferior to RGZ in reducing Fasting Insulin levels  and ; respectively. Pooled analysis was moderately heterogeneous , and heterogeneity did not resolve after further sensitivity analysis.For Fasting Insulin For HOMA-IR, only the following comparisons revealed significance: ALF vs. ALF+MET , CC+MET vs. CC , CPA+EE+MET# vs. CPA+EE# , DGT+EE vs. CPA+EE , DGT+EE vs. DPN+EE , MET vs. ALF+MET , MET vs. CC , MET vs. CPA+EE , PGZ vs. PLC , and RGZ vs. CPA+EE . Pooled analysis was moderately heterogeneous , and heterogeneity did not resolve after further sensitivity analysis.Additionally, we performed a frequentist network meta-analysis. Following the results of node-splitting analyses, we adopted the consistency model. The estimated value of between-study variance in the network ranged from 2.2 to 309.7. Among indirect comparisons, significant inconsistencies were identified in the closed-loop of MET#-ORL#-PLC and DGT+EE-DPN+EE-MET-RGZ . FurtherResults of each direct and indirect comparison in the network meta-analysis are detailed extensively in The ranking probabilities of the highest and lowest intervention for each outcome are available in We further employed multiple regression models to assess the interaction between anthropometric, metabolic, and hormonal parameters with significant estimates. The results of these meta-regressions are available in In contrast, LDL and Triglyceride levels showed no significant associations with Total Testosterone  or DHEAS levels . However, changes in LDL and Triglyceride levels were significantly associated with changes in SHBG  and ; respectively. The inversed regression for the effect of LDL and Triglycerides on SHBG was not significant . Likewise, no significant associations were detected between HOMA-IR and either SHBG (P = 0.9) or Total Testosterone (P = 0.95) or DHEAS (P = 0.97).In the present systematic review and network meta-analysis: 55 interventions were evaluated for efficacy in reducing weight and hyperandrogenism through 7,858 comparisons across 17 outcomes. The included interventions can be categorized pharmacologically into ten categories: Oral contraceptives, Gonadotropins modulators, Estrogen modulators, Aromatase inhibitors, Catecholamines modulators, Antiandrogens, Antidiabetics, Cholesterol modulators, Antioxidants, and Anti-inflammatories. After a long chain of analyses, the competition settled between Antiandrogens, Oral contraceptives, Anti-diabetics, Cholesterol modulators, and combinations in-between categories.Flutamide, an antiandrogen, proved efficacy in improving anthropometric, androgenic, and lipid parameters. Cyproterone acetate+ethinylestradiol, an antiandrogen with an oral contraceptive, demonstrated the highest efficacy in improving androgenic parameters. However, it did not exhibit any superiority in the remaining parameters. Inositol+monacolin K, an antidiabetic and a cholesterol modulator, displayed efficacy in improving androgenic and lipid parameters. Likewise, metformin+simvastatin/rosuvastatin and orlistat, an antidiabetic and cholesterol modulators, significantly improved lipid parameters. Nonetheless, these improvements were only observable in the short term follow-up.Ideally, all interventions were comparable in female hormones, FGS, HDL, glucose, and insulin levels improvements. As an exception, liraglutide, an antidiabetic, showed a significantly lower efficacy in reducing LH levels. Clomiphene citrate, an estrogen modulator, was the least effective agent in improving insulin levels. Eventually, pioglitazone, an antidiabetic, demonstrated efficacy in reducing HOMA-IR.Meanwhile, results of meta-regression revealed no significant associations between changes in hormonal and metabolic parameters. Even those few significant associations had a very small R-squared. This finding indicates that a drug’s action on hormonal parameters does not necessarily modify metabolic parameters and vice versa. Also, this finding is counter-intuitive to previous studies that attributed PCOS progression to lipid metabolism disturbance , 138. ThOn the other hand, meta-regression revealed a significant effect of hormonal parameters on anthropometric parameters. This finding could explain why traditional obesity interventions show limited efficacy and limited duration in obese PCOS patients , 140. FuGiven the high prevalence of obesity among PCOS patients, effective treatments that improve both obesity and reproductive functions are urgently needed , 142. EvFlutamide works by inhibiting androgen uptake or nuclear binding in the target tissues . HoweverThe mainstream literature approaches PCOS either as a mere metabolic disturbance or a fertility challenge –150. FurIn our systematic review and network meta-analysis: we assessed multi-dimensional outcomes, developed strict inclusion criteria, separated short-term from long term comparisons, and analyzed only well-designed RCTs in the past 30 years. Our findings settle a group of assumptions and advocate a reliable reference for future clinical decisions and guidelines. To the best of our knowledge: this is the first meta-analysis to investigate this size of outcomes with this number of interventions in the management of PCOS. The findings for various treatments involved were consistent for all measured outcomes, and the evidence presented was highly rated.Even so, some limitations can be identified in our work: most RCTs had relatively small sample sizes; thus, the wide 95% CI of most comparisons indicates insufficient power. Also, we restricted the average BMI to over 25; hence, the implications can only apply to overweight/obese PCOS patients. The modifications in the clinical definitions and diagnostic criteria of PCOS may contribute to the clinical heterogeneity.Overall, the current evidence demonstrated the superiority of flutamide in improving both metabolic and hormonal parameters. And the higher efficacy of cyproterone acetate+ethinylestradiol only in improving hormonal parameters. Nearly all interventions were comparable in female hormones, FGS, HDL, glucose, and insulin levels improvements. Even though inositol+monacolin K, metformin+simvastatin/rosuvastatin, and orlistat ranked higher in improving lipid parameters, their efficacy lasted only for short-term follow-ups. Liraglutide exhibited the lowest efficacy in reducing LH levels, and clomiphene citrate was the least effective agent in improving insulin levels. Pioglitazone demonstrated the highest efficacy in reducing HOMA-IR on the long-term follow-up. In the management of PCOS: a drug’s action on hormonal parameters does not necessarily modify metabolic parameters and vice versa. Obesity in PCOS is a unique case of obesity that should not be merely addressed by traditional weight-loss interventions. Prospective large-scale clinical trials are crucially required to study the appropriate dosage of flutamide and to assess the efficacy of combined flutamide+cyproterone acetate+ethinylestradiol.S1 File(DOCX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(PDF)Click here for additional data file.S5 File(DOCX)Click here for additional data file.S6 File(DOCX)Click here for additional data file.


There is a need for robust antibiotic stewardship programs (ASPs) in the neonatal population. This study's objectives were to assess neonatal antibiotic use practices over an extended period across an integrated delivery network (IDN), including six Neonatal Intensive Care Units (NICUs), to identify those most successful practices reducing use rates.A retrospective cohort study was conducted, including 15,015 NICU admissions from an integrated delivery network, across six hospitals over eight years (50% Level III and 50% Level II) computing antibiotic use rates (AURs) stratified by usage: in the first few days of the stay vs. later in the stay and by gestational age. Several metrics were examined for assumptions of strong correlation with AUR: (1) the percentage of infants given antibiotics early in their stays and (2) durations of courses of antibiotics.Results conclude a wide variation in AURs and trends that these rates followed over time. However, there was a decrease in overall AUR from 15.7–16.6 to 10.1–10.8%, with four of the six NICUs recording statistically significant reductions in AUR vs. their first year of measurement. Specifically, the level III NICUs overall AUR decreases from 15.1–16.22 to 8.6–9.4%, and level II NICUs overall AUR 20.3–24.4 to 14.1–16.1%. A particularly successful level II NICU decreased its AUR from 22.9–30.6 to 5.9–9.4%.To our knowledge, this is the first study to utilize data analytics at an IDN level to identify trends in AUR, We have identified practices that allowed an institution to reduce NICU AURs significantly, and which, if done as a standard practice, could be replicated on a broader scale. The early use of antibiotics in the neonatal period increases antimicrobial resistance . In 2015Life-threatening infections in the NICU generally fall into the two broad categories of early-onset sepsis (EOS) and late-onset sepsis (LOS). A diagnosis of EOS occurs within the first 48–72 h of life with an incidence of 0.3–1.0 per 1000 live births, and a LOS diagnosis is after the initial 48–72 h with a higher rate of 2.2 per 1000 live births , 6.The uAntibiotic use in the NICU in the first week of life increases morbidity and mortality, results in mother and child separation, and increasing healthcare costs , 8. EarlConsequently, there is a need for robust antibiotic stewardship programs (ASPs), particularly in the neonatal population, who have underdeveloped immune systems. ASPs are hospital-based programs dedicated to improving antibiotic use by optimizing the treatment of infections and reducing adverse events associated with antibiotics. The Infectious Disease Society of America and the Society for Healthcare Epidemiology of America recommend that APS, compromised of appropriate antibiotic selection, proper dosing, therapy duration, as well as the route of administration, be implemented in the NICU to decrease inappropriate use and resistance . For insImplementing an ASP provides clinical management, surveillance interventions, and work strategies, including prescribing practices such as choosing the correct antibiotic, dosing, course duration, and monitoring, as well as educating healthcare workers . FollowiDespite the prevalence of ASPs, there continues to be significant variability in antibiotic use rate (AUR), defined as the number of patient days that neonates were exposed to antibiotics (1 or more) per 100 patient days. Alarmingly, misuse of antibiotics in the NICU may be as high as 20–50% . AnalysiThe American Academy of Pediatrics (AAP) endorses the use of a multivariate risk calculator to guide initiation of antibiotics in infants > 34 weeks gestation at risk for early-onset sepsis with prompt discontinuation of antibiotics after 36–48 h if blood culture remains sterile . AnotherUtilization of the NICU BacT/Alert microbial detection technology allows for detecting infection in as few as 24–36 h . In factThe Center for Disease Control and Prevention (CDC)  developeA collaboration of 146 NICUs participating in the Choosing Antibiotics Wisely Campaign, a QI collaborative by Vermont Oxford Network online program with interactive web sessions, decreased the median AUR from 16.7 to 12.1%, a 34% relative risk reduction . ConcurrWe collected retrospective data of all NICU admissions at birth age 0 to 1 day) to a medium-sized integrated delivery network (IDN) from years 2010 to 2017, excluding NICU readmissions (which accounted for less than 6% of admissions). The description of the resulting cohort is shown in Table  day to aFor each NICU and each year, all corresponding admissions were identified. For the AUR computation, the numerator is the sum of the number of days of antibiotics for all admitted neonates, and the denominator is the sum of the lengths-of-stay divided by 100 . Note that fractional patient days were rounded-up to the nearest whole number of days, an approach that was standardized across hospitals. Note also that days where multiple antibiotics were given were counted as single antibiotic days.However, unlike in previous studies, the AUR was utilized as a rate parameter inferred from the discrete measurements. Furthermore, this is akin to determining if a coin is fair from a limited number of trials—our confidence in the estimate of the rate parameter increases as the number of trials increases. Notably, confidence bounds can be quite wide; however, it is plausible to infer statistical statements about improvements over time. For each hospital and each year after the first, the statistical significance  was calculated for the one-sided z-proportions test for the reduction in the proportion of antibiotic days as a fraction of patient days, as compared to both the previous year and the first year of recorded data for that institution. Additionally, NICUs joined the IDN at different points in time, somewhat complicating the analysis.Interestingly, particularly for level II NICUs, nearly all antibiotics prescribed were given in the first three days of life. For example, for Hospital 4, between 90.3 and 98.9% of all antibiotic days belonged to courses starting in the first three days of life, presumably for empiric treatment of infections believed to be early-onset sepsis (EOS). Given recent research showing that the administration of antibiotics to only those neonates with a demonstrable risk of sepsis hypothesized that NICUs could significantly reduce their antibiotic usage by lowering the use of early antibiotics. Statistical bounds computation was utilized for the proportions of neonates given early antibiotics, using the z-proportions test to assess differences in rates over time.We also considered durations of antibiotics, where a “course” of antibiotics was defined as a sequence of doses where each dose was separated by less than 24 h. We considered courses of antibiotics associated with bacterial blood cultures (taken up to two days before the start of the course until the end of the course) as other uses of antibiotics may not be explicitly for suspicion of inspection but for other purposes . Bootstrap sampling was performed to compute statistical bounds for the mean duration of antibiotics. Utilization of the non-parametric Mann–Whitney U-test to assess whether there were statistically significant differences in mean course durations both year-to-year and compared to the first year for which data were available a given NICU. Finally, although these results are reported elsewhere, an assessment was completed for the positive and negative predictive values of provisional bacterial culture results at 24, 36, and 48 h.The AUR for the six NICUs as a function of time are shown in Table The proportion of neonates given antibiotics starting in the first three days of life is shown in Table Quantitatively speaking, there are two main contributors to the AUR: firstly, the proportion of neonates receiving antibiotics and, secondly, the duration of antibiotics they were given. After looking into the first question, we turned our attention to antibiotic durations, generating the results shown in Table Findings show strong evidence that in many cases, courses of antibiotics with a negative provisional result can be stopped as early as after 36 h and, in most cases, by 48 h. In analyzing the results of blood cultures overall for all six institutions, we found that the negative predictive values (specificities) of negative culture results at 24, 36, and 48 h were 96.9%, 98.9%, and 99.5%, respectively. The sensitivities of positive culture results at the same time intervals were 81.7%, 93.9%, and 97.3%. Discontinuing a course of antibiotics at 24 h risks missing approximately 18% of positive cases, but this risk drops to approximately 6% at 36 h and less than 3% at 48 h.The substantial sample size of 15,015 NICU admissions from a large IDN with the inclusion of both Level II and III NICU's over eight years provides insights into antibiotic stewardship. A data-driven approach provides an additional tool for assessing NICU ASPs, including AUR, reporting, and tracking ASPs and identifying best practices for reducing unnecessary early antibiotic use in the neonatal period. From 2010 to 2017, overall AUR declined from 15.7–16.6 to 10.1–10.8%. Furthermore, the utilization of data analytics at the IDN level over eight years highlights the improvement in ASPs while identifying areas for improvements and best practices. In the study, a variance was seen in antibiotic practices in the six NICUs, noting one of the Level II NICUs significantly reduced AUR over the study's course despite previous studies indicating level II's have higher rates of AUR. Also, coinciding with other findings, the timing of negative predictive value of blood cultures was 98.9% at 36 h and 99.5% 48 h, providing further justification for ASPs practices such as a 48-h electronic, hard stop admission order. Two successful practices were identified, including a reduction in the proportion of late preterm  neonates receiving empiric antibiotics by a factor of three and a decrease in the duration of empiric antibiotic therapy to approximately 1.5 days for all gestational ages.The WHO stresses the importance of reducing unnecessary antibiotic use to decrease antimicrobial resistance, which has an estimated cost to society of 1.2 trillion dollars in health expenditures by 2050 . LowerinAlthough antimicrobial resistance was deemed a potential problem during the period in question, and there was some institutional understanding of the need to limit antibiotics in the early postnatal period, few IDN-wide practice standards in place codified best practices. This situation has recently changed, introducing an automatic 48-h stop for antibiotics orders and introducing a new element in the electronic medical record across all of the studied hospitals, computing and reporting the Kaiser Permanente risk score for early-onset sepsis mainly based on maternal risk factors. Incorporating automatic 48-h stops for antibiotic orders has been shown to reduce antibiotic use by 25–35% . While aTherefore, potentially the variances observed in our study have been reduced with the introduction of these IDN-wide standards, which may be a future study topic. Despite these recent changes, we feel that our study is of current relevance, as many institutions may not have put into place system-wide or hospital-wide neonatal antibiotic use standards, and maybe informed to find variances in their use of antibiotics in the neonatal ICU setting, using techniques similar to those described here.Our study's main limitation was that it was an observational retrospective cohort in a single IDN in one part of the country, which may not be reflective of the entire population. The data of the study is quantitative, limiting knowledge on actual protocols and ASPs per NICU. Another limitation, pointed-out to us by one of the anonymous reviewers, is that NICUs do often treat “older” neonates and that there is significant usage of antibiotics for peri-operative purposes, for example after surgery to treat congenital heart defects . This asWe conducted a retrospective cohort study of NICU antibiotic use over an extended period within a multi-hospital IDN, overall showing a reduction in AUR from 15.7–16.6 to 10.1–10.8% over eight years. Despite the overall reduction, there was wide variation in the rates of decrease in AURs over time, even within NICU classes. We identified specific practices, namely reducing antibiotic use rates due to suspicion of early-onset sepsis and reducing the duration of antibiotics for culture-negative cases, that appeared to contribute most significantly to the overall reductions over time for the most successful NICUs.


The properties and performance of geopolymer at different length scales have been intensively studied, but only limited studies on geopolymer have investigated the zone located between paste and aggregates, which is called the interfacial transition zone (ITZ). The microstructure of ITZ and its nanomechanical properties in geopolymer concrete are examined in this study. Fly ash-based geopolymer has great potential to be an alternative to traditional concrete. To this end, scanning electron microscopy (SEM) and nanoindentation tests were performed to investigate the microstructural characteristics and nanomechanical properties of the ITZ, and the results were compared with the ITZ of traditional concrete. Results show that traditional concrete demonstrated a weak ITZ with pores and microcracks, while the geopolymer concrete microstructure did not present weak ITZs in the vicinity of aggregates. More pores and crack were observed in the ITZ in traditional concrete. Further, a considerable amount of fly ash particles, that appear to be unreacted or partially reacted in the matrix phase, was observed. Based on the nanoindentation results, 58% of the microstructure is composed of unreacted or partially reacted fly ash particles. The results of nano- and microscale tests will enhance the understanding of how concrete behaves and performs at large scales. The macroscale properties of a composite is greatly influenced by its interfaces. The interfacial transition zone (ITZ), which is known as the most important interface in concrete, is located between cement paste and aggregate in traditional concrete ,2. In thMany studies have been conducted to characterize the microstructure of the ITZ in concrete using different tools such as scanning electron microscopy (SEM) and energy dispersive X-ray (EDX) ,11. Thes2 [2 compared to traditional concrete [It is well known that the most commonly used binding agent in construction is Portland cement (PC), but its production releases a large amount of CO2 ,23. On tconcrete ,25. Geopconcrete . Its worconcrete . The reaconcrete ,28. The concrete ,30,31,32concrete . The comconcrete . After eDespite the great attention given to using geopolymer concrete as an alternative to traditional concrete, only limited studies have explored the ITZ in geopolymer concrete ,34. WithThe main aim of this research is to study the microstructures and nanomechanical properties of the ITZ in a geopolymer concrete mixture. Toward this end, SEM and nanoindentation tests were utilized to investigate the properties of the ITZ in geopolymer concrete at the microscale, and the results were compared with ITZ in conventional concrete.The SEM microstructure image of the geopolymer concrete mixture is shown in Nanoindentation tests were conducted to measure the elastic properties in the microstructures, including the ITZ region in geopolymer concrete and OPC concrete. A total distance of approximately 140 μm crossing multiple phases (from the aggregate to cementitious matrix) in the microstructures was inspected. One example is shown in The elastic modulus distributions of the geopolymer mixture are shown in The elastic modulus distributions of the OPC mixture are shown in In addition to the sequential indentations made along the longitudinal direction across the three zones (aggregate–interphase–matrix) to investigate property changes with a particular interest in the interphase zone, the nanoindentation test was also conducted to further examine properties of individual phases observed in the geopolymer matrix. Based on the SEM images, the indentation moduli measured within the cementitious matrix can be categorized into four different major phases, as marked in SEM test results show that traditional concrete developed a weak ITZ with voids and cracks, while the geopolymer concrete microstructure did not present weak ITZs in the vicinity of aggregates.Nanoindentation results showed that, in geopolymer mixtures, there is no clear weak zone identified around the aggregate with lower modulus such as the ITZ typically observed from OPC mixtures. This observation can be attributed to the high bond strength of the geopolymer matrix.Geopolymer matrix presented multiple phases in different geometries (SEM results) and mechanical properties (nanoindentation results). Highly scattered stiffness values within the matrix phase were observed and seem strongly related to the formation of different compositions such as N-A-S-H gel, partially reacted fly ash particles and pores.The investigation of geopolymer matrix performed in this study will provide a better understanding on performance at large scales, which could lead to enlarge the filed application of geopolymer.For future study, it is recommended to investigate the effect of aggregate surface texture on the bond properties between the aggregate and binding agents.This study examined microstructures and nanomechanical properties of geopolymer concrete mixtures where Class F fly ash is considered an alkali-activated binder. Two primary laboratory tests, SEM analysis and nanoindentation were conducted for geopolymer mixtures and an OPC concrete mixture as a counterpart of the geopolymer mixture. Based on test and analyses results, the main conclusions are drawn below:2 = 51.80%, Al2O3 = 23.06%, Fe2O3 = 13.03%, CaO = 2.81%, MgO = 0.85%, SO3 = 1.23%, Na2O = 0.71%, and K2O = 2.52%. Sodium hydroxide with a molar concentration of 12 is mixed with sodium silicate to prepare the alkaline solutions used to activate the fly ash. As reported in previous studies [2 to Na2O in sodium silicate solution is 3.1, and 63% of sodium silicate solution is water.The binding agent used to produce geopolymer is class F fly ash with a specific gravity of 2.37. The chemical composition of fly ash is SiO studies , combiniThe preparation of geopolymer concrete specimens begins with dry mixing the fly ash with limestone for three minutes. Then, the alkaline solution is added gradually and mixed for five minutes. The ratio of sodium silicate solution to sodium hydroxide solution used in this study is 2.5. Immediately after mixing, the mixtures were cast in . The geopolymer specimens were placed in an oven at 60 °C for 24 h. After 24 h of curing in the oven, the specimens were taken out and demolded, then left for 28 days in the lab. The alkaline solution for fly ash was 0.4. OPC (Type I) concrete was also prepared with a design strength of 35 MPa in order to investigate the ITZ formation in OPC concrete and compare the results with those of geopolymer concrete. The size distribution and quantity of coarse aggregates were the same for all mixtures to ensure an effective comparison. The aggregates that were used to prepare concrete specimens were coarse aggregates that were retained on sieves of 4.75 to 12.5 mm. Fine aggregate was excluded to focus the study on the ITZs. The mix proportions are summarized in To prepare the specimens for nanoindentation and SEM tests from the cylinder sample , small thin sections measuring approximately 10 mm by 10 mm by 5 mm were cut with a low-speed precise diamond saw. Since the nanoindentation tests were conducted at small scales, the roughness of the specimen surfaces has a huge impact on the accuracy of the results. In order to obtain a smooth surface that is crucial for micro- and nano-level experiments, each sample cut was grounded and polished to achieve a final level of smoothness of less than 100 nm. An optical microscope was used to examine the effectiveness of the grinding and polishing procedures. Finally, specimens were cleaned in an ultrasonic bath and then kept in airtight containers.In order to investigate the microstructure in geopolymer and OPC concrete mixtures, SEM was used on the polished surface to acquire microscale images. SEM provides images that contain significant details about the microstructure. One of the powerful SEM imaging techniques is Backscattered Electron mode. By using this technique, different phases of the sample can be identified depending on their atomic numbers, which appear at different grey levels. Differences in atomic numbers indicate the different chemical compositions of each phase.To determine the nanomechanical properties of each specimen with a focus on the ITZ, a Hysitron Triboindentor equipped with a pyramidal-shaped diamond Berkovich tip was utilized to conduct the nanoindentation tests. Similar to these studies ,50, the