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Abstract In this work, a novel series of N-phenylacetamide-1,2,3-triazole-indole-2-carboxamide derivatives 5a–n were designed by consideration of the potent α-glucosidase inhibitors containing indole and carboxamide-1,2,3-triazole-N-phenylacetamide moieties. These compounds were synthesized by click reaction and evaluated against yeast α-glucosidase. All the newly title compounds demonstrated superior potency when compared with acarbose as a standard inhibitor. Particularly, compound 5k possessed the best inhibitory activity against α-glucosidase with around a 28-fold improvement in the inhibition effect in comparison standard inhibitor. This compound showed a competitive type of inhibition in the kinetics. The molecular docking and dynamics demonstrated that compound 5k with a favorable binding energy well occupied the active site of α-glucosidase.

Background: There is an ongoing debate about the optimal dosage of gonadotropin-releasing hormone (GnRH)agonist for oocyte triggering in polycystic ovarian syndrome (PCOS) patients at risk for ovarian hyperstimulation syndrome(OHSS). In this study, we intend to ascertain whether the use of repeated doses of a GnRH agonist for oocytetriggering in these patients can enhance the outcomes of controlled ovarian stimulation (COS) for in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles.Materials and Methods: This randomised clinical trial enrolled 70 PCOS women candidates for IVF/ICSI with thestandard antagonist protocol at Royan Institute (Tehran, Iran) from May 2020 to June 2022. Patients at risk of OHSSwith oestradiol (E2) levels >3000 pg/ml on the day of trigger were randomly assigned to a control or experimentalgroup. Group A (control group) patients received 0.2 mg triptorelin (Decapeptyl®) for final oocyte maturation. GroupB (experimental group) patients received a second dose of 0.1 mg Decapeptyl®12 hours after their first dose, for a totaldose of 0.3 mg. IVF/ICSI outcomes were compared between the groups.Results: Ultimately, 35 women from the study group and 33 from the control group completed the treatment cycle. Bothgroups were comparable in terms of demographic characteristics, baseline hormonal profiles, and PCOS phenotypes.The dosage of gonadotropin, stimulation duration, number of retrieved oocytes, oocyte maturation rate, and oocyte recoveryratio did not significantly differ between the groups. No significant differences were found in terms of the numberof blastocyst and cleavage embryos, nor the quality of obtained embryos between the groups. The mild to moderateOHSS rate was significantly lower in the study group (P=0.038).Conclusion: A second dose of GnRH agonist 12 hours after the first dose did not improve the number and maturity ofoocytes, or pregnancy outcomes in PCOS patients (registration number: NCT04600986).

The purpose of this study is to investigate the performance, in-cylinder combustion, and emissions of the MTE660E heavy-duty diesel engine using 3D CFD simulation. The CFD simulation model based on AVL FIRE software was developed to numerically investigate the performance, in-cylinder combustion, and emissions of the MTE660E engine. The AVL model was validated against empirical data. The 6-cylinder MTE660E engine was operated under a constant excess air ratio of 1.75 and at 1600 rpm engine speed. The geometry and lattice design of the MTE660E engine were simulated to provide a computationally efficient approach. The AVL model was validated against experimental data and the error between the measured and calculated value of combustion characteristics and emissions was acceptable (R2> 90 %), error

There are several methods for simulating internal combustion engines. The computational fluid dynamics method is the best way to simulate these engines because it can simulate the combustion process, which is a microscopic process. In this study, the simulation of the combustion process in a closed cycle in a diesel engine with a mixture of diesel and hydrogen is done by AVL Fire software. In order to simulate the combustion in the Species and chemical transmission section, a chemkin mechanism is coupled with AVL Fire software. In this study, the effect of 10 % hydrogen fuel and 90 % diesel fuel as well as the effect of nozzle holes (1, 3 and 6 holes) on the engine performance were directly investigated. In order to validate the results of the pressure simulation and the temperature inside the cylinder in the diesel fuel combustion mode, at 2800 rpm and 100 % load, the data were compared with the experimental data. The research also included verification of the heat transfer coefficient (HTC) results with theoretical data obtained by Woschni and Hohenberg. To accurately simulate the combustion process, the simulation data was validated by comparing the pressure and temperature inside the cylinder at a specific operating condition with experimental data. The results indicate that the maximum heat transfer coefficient is achieved at the angle of maximum pressure, with the exhaust valve having the highest coefficient. The addition of hydrogen to diesel fuel results in a 1.72 % increase in the heat transfer coefficient due to increased collisions. In addition, the introduction of hydrogen fuel increases cylinder pressure and engine power, while increasing the number of fuel nozzle holes decreases the coefficient and pressure, which affects fuel penetration and evaporation rate.

Abstract Background The present study aimed to respond to clinical question, can prolonged P-R interval predict clinical outcomes in non-ST elevation acute coronary syndrome patients? Methods This descriptive-analytical study was conducted on cardiac patients. All of the non-ST elevation acute coronary syndrome (NSTEACS) including non-ST elevation myocardial infarction (NSTEMI) and unstable angina patients included in the study. Then they divided into two groups: prolonged P-R interval and normal P-R interval. The patients who had a history of digoxin and calcium channel blocker use, using antiarrhythmic drugs, known valvular or congenital heart disease and connective tissue, unreadable P-R interval and cardiac block were excluded. Data were collected using the questionnaire consisted demographic data and clinical outcomes and a follow-up part was completed by one of the researchers. Results Finally, 248 patients completed the study. The results showed both of the two groups had significant differences in terms of the history of myocardial infarction (MI) (p = 0.018), the level of high-density lipoprotein (HDL) (p = 0.004), heart rate (p = 0.042), inverted T wave (p = 0.017), anterior ST- segment depression (p = 0.008), normal report of coronary angiography (CAG) (p = 0.003), three vessels disease (p = 0.043), left main lesion (p = 0.045) and SYNTAX score (p = 0.032) based on the CAG report. The results of six-month follow-up showed although, the frequency of ischemic stroke, coronary artery disease (CAD) and cardiovascular death were higher in prolonged P-R interval groups. The chi-square test showed this difference was statistically non-significant (p > 0.05). The multivariate logistic regression model revealed non-significant relationships between prolonged P-R interval and SYNTAX score, significant CAD, three-vessel disease, inverted T wave, anterior ST depression, heart rate and HDL. Conclusions Based on the results of our study the six-month follow-up showed non-significant outcomes. Further studies are recommended to assess the long-term outcomes.

Abstract Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs. However, time-independent problems pose the challenge of requiring long-range exchange of information across the computational domain for obtaining accurate predictions. In the context of graph neural networks (GNNs), this calls for deeper networks, which, in turn, may compromise or slow down the training process. In this work, we present two GNN architectures to overcome this challenge—the edge augmented GNN and the multi-GNN. We show that both these networks perform significantly better than baseline methods, such as MeshGraphNets, when applied to time-independent solid mechanics problems. Furthermore, the proposed architectures generalize well to unseen domains, boundary conditions, and materials. Here, the treatment of variable domains is facilitated by a novel coordinate transformation that enables rotation and translation invariance. By broadening the range of problems that neural operators based on graph neural networks can tackle, this paper provides the groundwork for their application to complex scientific and industrial settings.

Several factors affect engine performance, including fuel injection pressure, injection angle, and injector orifice diameter. Any deviation from normal conditions in any of these aspects can disrupt optimal engine performance, resulting in inefficient combustion and increased exhaust emissions. To investigate the effect of injector hole number, injection hole angle, and injection pressure on the performance and emissions of a diesel engine operating on a diesel/hydrogen blend (10 % hydrogen and 90 % diesel), a single-cylinder direct injection diesel engine was used. Three injector nozzle configurations just for diesel injector with different hole diameters (0.6, 0.3, and 0.2 mm) were used at injection angles of 0, 15, and 30 degrees, respectively. Three injection pressures (200, 400, and 600 bar) were tested, with results monitored for brake-specific fuel consumption (BSFC), brake thermal efficiency (BTE), smoke, and NOx emissions.Optimal results were achieved with a maximum injection pressure of 400 bar and a nozzle angle of 15 degrees, resulting in improved engine performance and BTE, along with a 6.5 % reduction in BSFC. Increasing the number of injector holes, injection pressure, and injection angle resulted in reduced BSFC and smoke emissions, but with a significant increase in NOx emissions. Notably, this study deviates from traditional combustion methods by introducing air from a 1.1-atmosphere tank instead of relying solely on natural intake. In addition, hydrogen fuel is introduced into the air manifold via a separate injector with an injection pressure of 20 bar, while diesel fuel is injected directly into the combustion chamber.

Heavy metals can cause serious environmental and human health problems, and their removal from wastewater is critical to protect our planet and communities. This study investigated the ability of crushed pomegranate peel to remove mercury and cadmium ions from contaminated water as a function of different experimental parameters. The experimental results showed that the pH of the solution influenced the adsorptive removal of heavy metals, with the best performance observed at pH 4.8. Optimization studies and process balance modeling were performed to optimize the process for commercial use. The performance of pomegranate peel was compared with that of other materials, and the highest adsorption capacities for both cadmium (Ca (II)) and mercury (Hg (II)) ions were observed to be 89.59 and 42.125 mg/g, respectively. The results were interpreted using the Langmuir model, which provided the best fit to describe the behavior of the process.

BackgroundThe use of honey as an eye treatment encounters challenges due to its high osmolarity, low pH, and difficulties in sterilization. This study addresses these issues by employing a low concentration of honey, focusing on both in-vitro experiments and clinical trials for treating dry eye disease in corneal cells.MethodsIn the in-vitro experiment, we investigated the impact of a 1% honey-supplemented medium (HSM) on limbal stem cells (LSCs) and keratocytes using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay and real-time polymerase chain reaction (PCR) for BCL-2, BAX, and IL-1β gene expression. Simultaneously, in the clinical trial, 80 participants were divided into two groups, receiving either a 1% w/v honey ophthalmic formulation or a placebo for 3 months. Study outcomes included subjective improvement in dry eye symptoms, tear break-up time (TBUT), and Schirmer’s test results.ResultsMTT results indicated that 1% HSM did not compromise the survival of corneal cells and significantly reduced the expression of the IL-1β gene. Additionally, participants in the honey group demonstrated a higher rate of improvement in dry eye symptoms and a significant enhancement in TBUT values at the three-month follow-up. However, there was no significant difference between the study groups in terms of Schirmer’s test values. No adverse events were observed or reported.ConclusionIn conclusion, 1% honey exhibits anti-inflammatory and anti-infective properties, proving effective in ameliorating dry eye symptoms and enhancing tear film stability in patients with dry eye disease.Clinical Trial Registration: https://irct.behdasht.gov.ir/trial/63800.

Surface material identification using hyperspectral imaging (HSI) analysis is a crucial and challenging issue in remote sensing. Researchers widely recognize that exploiting spectral-spatial data outperforms using spectral pixel-wise methods. Most of the work focuses on machine learning techniques to address issues with HSI categorization. The semi-supervised learning model, for example, was utilized in early research to address the issue of the expenses associated with training samples during HSI classification. Nevertheless, these approaches and techniques have certain drawbacks. The suggested framework must be improved in terms of hardware and computational capacity to enhance security. While integrated patch-based convolutional neural network (CNN) can help overcome these issues, due to the complicated geographical distribution of many targets, there are some downsides, such as using spectra as odorless vectors. The supervised learning techniques categorize the inputs by using a collection of appropriate examples for each class, referred to as training samples. For this purpose, samples must be obtained, however, the process is expensive and time-consuming. Deep learning techniques for evaluating spatial spectrum data have significantly advanced, yet security challenges remain in remote sensing with HSI. In this article, we introduce a novel HSI classification method, termed Deep-Detect, which leverages deep learning models with attention mechanisms to tackle these challenges. We use two weighting factors that may be obtained flexibly during the training phase to integrate both spectral and spatial properties, whose importance differs for various objects and scenarios. Additionally, we proposed a spatial and spectral fully convolutional networks (Spe-FCNs) attention mechanism that focuses attention on the input patches’ most informative regions. To verify performance, we used the northern Italy dataset from Pavia University and compared our proposed model with state-of-the-art HSI classification models. Our findings show that the proposed Deep-Detect model enhances classification accuracy while addressing key security concerns in HSIs.

Artificial intelligence (AI) is one of the key technologies emerging in the Industrial Revolution that could protect against cybersecurity threats. AI is a key component of big data analytics and enables accurate real-time data analysis. AI can analyze big data, but it has some issues with security, privacy, and centralization of data. Moreover, cybercriminals continue to advance, so law enforcement faces more threats. With traditional cybersecurity solutions, sophisticated cyber-attacks are harder to detect and defend against. In complex cyberspaces, AI algorithms mine valuable features from data. However, the data on the Internet is scattered and controlled by different parties, making it challenging to authorize and validate its use. The AICyber-Chain model is presented in this paper for securely storing, calculating, and distributing data on the Internet at an enterprise scale. In a large-scale Internet environment, our proposed AICyber-Chain model integrates three key components to ensure a more secure cyberspace, enhancing AI, namely: Firstly, blockchain-based data sharing guarantees ownership at a large scale, enabling real-time data sharing. Secondly, a platform powered by AI makes cyberspace more trustworthy. Thirdly, sharing data or services rewards participants financially, which promotes sharing. We also discuss a typical use scenario, an alternative deployment method, and its security and commercial efficacy. Also, we simulated our model on Ethereum’s official test network, called Rinkeby, to demonstrate its practicality and efficiency. This model speeds up authentication by 1.8 times compared to the centralized model. In addition, our proposed solution reduces gas consumption by 20 to 25%. Our paper aims to serve as a guide and reference point for cybersecurity researchers and industry practitioners, especially from an intelligent computing or AI-based technical standpoint.

The global surge in population, coupled with the continuous emergence of new digital devices, has significantly increased the worldwide demand for electricity, intensifying the existing energy crisis. Microgrids are pivotal in addressing the current needs of utility industries and driving innovation. However, numerous challenges remain, particularly in developing countries where inadequate infrastructure hampers electricity generation, transmission, and distribution. A potential solution lies in energy sharing, which offers mutual benefits to both microgrid operators and consumers. The integration of Internet of Things (IoT) technology within microgrids enhances real-time monitoring and management of energy resources, thereby improving both efficiency and reliability. Microgrids have the capability to share electricity resources with other microgrids, local communities, or contribute surplus energy to the national grid. However, maintaining the integrity of participants in these energy exchanges is crucial. It is necessary to distinguish between trustworthy and potentially malicious microgrids or consumers. To address this issue, the implementation of a MicroTrust mechanism is proposed. The results demonstrate that MicroTrust effectively evaluates the trustworthiness of CCs and various requesting consumer nodes, providing a robust solution for secure and reliable energy sharing.

Model-driven development (MDD) in the Artificial Intelligence of Things (AIoT) domain faces significant challenges in ensuring the consistency and preservation of model properties during transformations, often leading to system inconsistencies. This research introduces the Property Preservation Framework (PPF), a novel approach fortified with a Markov chain methodology, specifically designed to address these challenges. The PPF integrates formal procedures and constraint-checking methods to systematically validate and preserve model properties, thereby enhancing the reliability of AIoT systems. Through empirical evaluations, the framework has demonstrated its ability to efficiently determine and verify model characteristics at various transformation stages, significantly reducing the incidence of property violations. The results indicate that the PPF not only ensures overall consistency and reliability but also optimizes resource allocation, thereby enhancing data efficiency during the property validation and preservation processes. These advancements make a substantial contribution to the domain of MDD, providing developers with the methodology to execute model transformations that accurately reflect system requirements and behaviors in AIoT ecosystems. The findings underscore the potential of the PPF to revolutionize AIoT development by ensuring high-quality, dependable, and efficient modeling outcomes.

In order to improve cybersecurity in newly developed network infrastructures, this research investigates the integration of blockchain technology with zero-trust security concepts. The zero-trust paradigm ensures continuous authentication across entities, in contrast to standard security models that often presuppose trust based on a network environment. Blockchain is used to decentralize and impose authentication intensity of communication clarity and honesty. The study compares the performance of the zero trust model enhanced by blockchain to traditional security systems in a number of parameters, such as intrusion detection rates and security breach reaction times, using extensive simulations. The findings demonstrate that the blockchain-enhanced zero-trust architecture performs better than conventional systems in both identifying and countering threats and methodically handling a large volume of transactions when under pressure. These conclusions, which emphasize significant advancements in security applications and system resilience, are predicated on the use of blockchain in zero-trust systems. Subsequent investigations will endeavor to enhance these technologies and investigate their utilization in networks across diverse intricate scenarios.

Mexico exhibits one of the highest rates of identity theft in relation to bank accounts, with 56% of cardholders encountering fraudulent processes. Consequently, financial institutions have elevated security measures in their authentication protocols and implemented more robust procedures. To address this issue, this study advocates a multifactor authentication approach wherein an algorithm incorporates the user’s location as a transparent authentication factor. Traditional multifactor authentication techniques typically necessitate intricate combinations of factors, including passwords, biometrics, external hardware, or time-based one-time passwords, to enhance security levels, albeit inadvertently introducing cumbersome steps. This research proposes a simple authentication implementation that retains the benefits of complex multifactor procedures -such as precision, accuracy, and security-, with the additional feature of utilizing an imperceptible location factor. The experimental phase encompasses the development of two functional prototypes: a software implementation on the Android environment -tested in 79 distinct locations-, and an implementation on Arduino hardware -tested in four locations-. These experiments were conducted in real-world scenarios, spanning a 21-day period, and involving data collection from different participants. The most significant result of this research is intricately linked to the runtime of each authentication process, where the average time elapsed from the user input stage to the completion of the validation section is 1.76 seconds. This time optimization is primarily attributed to the integration of native Android libraries. The findings demonstrate that it is possible to attain 100% accuracy for all secure locations with a 12-meter radius in 1.76 seconds in the case of the Android app. As for the Arduino-based security box, a 100% accuracy rate in all opening attempts is possible by employing a radius of 30 meters. The research endeavors to emphasize the versatility and applicability of the proposed solution for integration into diverse real-world scenarios where the preservation of data integrity holds utmost importance.

Text emotion detection is a pivotal aspect of natural language processing, with wide-ranging applications involving human-computer interactions. Machine learning agents have been trained with supervised methods, thus relying on labeled datasets. However, the arbitrary selection of emotion models while labeling such datasets poses significant challenges in the performance and generalizability of the produced machine learning predictors, primarily when evaluated against unseen data, as it effectively introduces bias to the process. This study investigates the impact of emotion model selection on the efficacy of machine learning systems for text emotion detection. Eight labeled datasets were employed to train linear regression, feedforward neural network, and BERT-based deep learning models. Results demonstrated a notable decrease in accuracy when models trained on one dataset were tested on others, underscoring the inherent incompatibilities in labeling across datasets. To prove that the emotion model significantly impacts predictors’ performance, we propose a standardized emotion label mapping utilizing James Russell’s circumplex model of affect that turns the emotion model into a parameter rather than a fixed element. Cross-dataset testing with this shared emotion mapping yielded significant, non-negligible changes in accuracy (both improvement and degradation). This fact highlights the impact of the emotion model (traditionally arbitrarily selected) during machine learning training and performance, arguing that improvements in accuracy reported in related research literature might be due to differences in the used emotion model rather than the new algorithms introduced.

Purpose: The purpose of this research was to compare the effect of video games, training with catalogs, and face-to-face training of the FIFA +11 warm-up program on the level of acceptance and belief of football players. Methods: The current semi-experimental research was done on 240 male players and coaches present in the premier soccer league of Khorasan Province who were randomly divided into four groups: 11+ face-to-face exercises, catalog, video games, and control (60 people in each group). Electronic questionnaires on acceptance and knowledge were filled by players and coaches. Then, the subjects of the training groups performed the exercises in three 15-minute sessions per week for eight weeks. Kruskal-Wallis test and marginal mean were used to check differences between the groups. Results: After eight weeks of training, there was a significant difference in the knowledge and awareness of the players between the four groups (P

Background & aim: Limited studies have been conducted on the effectiveness, applicability, and satisfaction of mind-body interventions as short-term methods that affect body conditions like Polycystic Ovary Syndrome (PCOS). Therefore, this study aimed to compare the efficacy of online mind-body interventions versus face-to-face counseling on the quality of life (QOL) of women with PCOS.Methods: This parallel randomized clinical trial was implemented in Yazd in 2020. Sixty eligible women with PCOS were randomly allocated to the online group (n=30) and face-to-face counseling (n=30) groups. Eight 120-minute sessions of mind-body interventions were held for both groups, either as online or face-to face. Data was collected using the PCOS Health-Related Quality of Life (HRQOL) questionnaire (at baseline, week 8 and 12), counseling satisfaction scale (at week 8 and 12), and FBS (at baseline and week 8) in both groups. Data were analyzed by SPSS (version22) using T-test, and repeated measures ANOVA.Results: HRQOL was 94.87±11.75 in online and 90.50±9.76 in face-to-face group at baseline, which increased significantly to 108.53±4.5 in the online and face-to-face groups at week 12, with a greater increase in the online group (P

Abstract Glycogen synthase kinase 3-beta (GSK3-β) is a serine-threonine protease expressed in the brain, and its hyperactivity is considered the underlying cause of Alzheimer’s disease. This enzyme requires an ATP molecule in its N-terminal lobe to phosphorylate its substrates, with the most important substrate being the Tau protein. This study focuses on the inhibitory mechanism of four naturally occurring compounds—apigenin, luteolin, rosmarinic acid, and salvianolic acid—from the Laminaceae family against GSK3-β. The orientation of the ligands within the ATP-binding pocket of GSK3-β and their binding energy were determined through molecular docking. Additionally, molecular dynamics simulations was conducted to study the conformational changes induced by the ligands in the protein structure. The results showed that apigenin and salvianolic acid achieved deeper parts of the cavity compared to luteolin and rosmarinic acid and formed stable complexes with the enzyme. In the rosmarinic acid complex, the enzyme exhibited the most exposed conformation. On the other hand, luteolin binding caused a small closure of the opening, suggesting a potentially ATP-competitive role. Our results suggest these compounds as lead candidates for the design of GSK3-β inhibitors.

Abstract Objective To investigate the effect of L-carnitine supplementation during the controlled ovarian stimulation (COS) cycle with antagonist protocol in patients with polycystic ovary syndrome (PCOS) diagnosis undergoing IVF/ICSI treatment. Methods and materials This was a double-blind clinical trial study including 110 patients with PCOS attended to Royan Institute between March 2020 and February 2023. At the beginning of the COS cycle, the eligible patients were allocated into two groups randomly according to the coding list of the drugs prepared by the statistical consultant. In the experimental group, patients received 3 tablets daily (L-carnitine 1000 mg) from the second day of menstruation of the previous cycle until the puncture day in the cases of freeze-all embryos (6 weeks) or until the day of the pregnancy test (8 weeks) in fresh embryo transfer cycle. In the control group, patients received 3 placebo tablets for the same period of time. Weight assessment and fasting blood sugar and insulin tests, as well as serum lipid profile were also measured at the baseline and ovum pick-up day. The results of the COS cycle as well as the implantation and pregnancy rates were compared between groups. Results Finally, 45 cases in L-carnitine group versus 47 cases in the placebo group were completed study per protocol. Data analysis showed that the two groups were homogeneous in terms of demographic characteristics and baseline laboratory tests and severity of PCOS. There is no statistically significant difference in terms of the oocyte recovery ratio and oocyte maturity rate, and the number and quality of embryos, as well as the rates of the fertilization, chemical and clinical pregnancy between groups. However, the means of weight (P

Abstract Introduction An imaginary companion is an invisible or personified entity created by children for themselves. An imaginary companion typically serves as a companion to the child and plays a significant role in their life, especially for only children who may experience more loneliness compared to other children. This research was conducted to investigate the role of an imaginary companion in the lives of only children. Method The present study was conducted using a qualitative method and a content analysis approach. Through purposeful sampling, a total of 34 preschool and primary school children, aged 6 to 9 years, from schools in Mashhad city, were selected until saturation was reached. They were subjected to semi-structured interviews. After data collection, the data were coded, and then the main and sub-themes were extracted. Results The research findings were represented in the form of 196 statements, 28 sub-themes, and 9 main themes. The main themes included the role of an imaginary companion in alleviating loneliness, the role of an imaginary companion in amusement, the role of an imaginary companion in emotional regulation, conversations with an imaginary companion, guidance from an imaginary companion for good and bad behaviors, the assistance of an imaginary companion in tasks, helping to generate new scenarios, the advantages of having an imaginary companion, and the disadvantages of having one. Conclusion Based on the aforementioned findings, it can be concluded that the presence of an imaginary companion can not only support children but also promote creativity and distance them from the virtual space and realities of the real world. Parental awareness of this matter can aid in the child’s growth, fulfill their needs, and, on the other hand, prevent potential harm to children.

This study focuses on the development of eco and user-friendly mechanochemically-activated geopolymeric stabilizers, surpassing the limitations inherent in traditional geopolymerization methods. A comparative analysis was undertaken with conventionally activated geopolymer stabilizers to establish benchmarks for effectiveness in soil stabilization applications. Additionally, the research delves into the impact of granulated blast-furnace slag (GGBS) content on the mechanical and durability properties of stabilized soil samples. In addition, the investigation focuses on the influence of the activation method on soil effectiveness and strength post-exposure to sulfate attack. The durability performance is rigorously assessed through the immersion of specimens in a 1 % magnesium sulfate (MgSO4) solution for 60 and 120 days. The comprehensive evaluation includes visual appearance, mass changes, Ultrasonic Pulse Velocity (UPV), Unconfined Compressive Strength (UCS), and Fourier-Transform Infrared (FTIR) spectra of geopolymer-stabilized soil specimens. The results showed that before the exposure to the MgSO4 solution, the UCS of mechanochemically activated geopolymer (MAG) samples was higher (12–45 %) than that of conventionally activated geopolymer (CAG)-stabilized soil. Furthermore, the strength of the geopolymer-stabilized soil improved by 114 %, 247 %, and 361 %, at 50, 75, and 100 % GGBS content, respectively. On the other hand, after exposure to the MgSO4 solution, the results showed that the mechanochemically activated geopolymer-stabilized soil has better resistance to sulfate erosion than the conventionally activated geopolymer-stabilized soil. The residual UCS for MAG and CAG samples were 93 % and 89 % when exposed to 1 % magnesium sulfate solution for 60 days, whereas they declined to 70 % and 58 %, respectively, after 120 days of immersion.

Abstract Lack of absolute treatment for premenstrual syndrome (PMS), its cyclic nature, considerable prevalence (70–90%), and its mental and physical burden imply necessity of effectiveness comparison of various treatments. Although antidepressant and hormonal drugs are well-known medications for PMS, in affected women who can’t tolerate, or don’t have compatibility or compliance with these drugs, other effective treatments have always been important concern. This study aimed to compare effectiveness of online positive-oriented counseling, taking vitamin D3 tablet, and online lifestyle modification training on alleviating PMS. 3-armed parallel randomized clinical trial was performed on 84 20–40-year-old eligible women with PMS. 84 women were randomly ( www.random.org/sequenc ) allocated into three groups, but data of 77 women (1, n = 25) online positive-oriented counseling group (6 sessions), (2, n = 27) vitamin D3 tablet group (one vitamin D3 tablet weekly for 6 weeks), and (3, n = 25) online lifestyle training group (6 sessions) were analyzed. Vitamin D3 was measured at baseline, week6 and fallow up week10. Primary outcome variable PMS was measured with Premenstrual Symptoms Screening Tool (PSST) at baseline, week 6, and follow-up week 10. Primary outcome satisfaction with intervention method was measured using satisfaction scale at week 6 and follow-up week 10. ANOVA, Repeated Measures, and Paired samples t-test were used for statistical analysis. There was no statistically significant difference in PMS at baseline between three groups respectively (33 ± 5.8, 34.1 ± 7.1, & 35.2 ± 6.4, P = 0.500). However, at follow-up week 10, there was statistically significant difference between three groups (22.3 ± 4.3, 25.4 ± 6.5, & 31.8 ± 6.5; P

Background: Alcohol consumption is widely recognized as a leading cause of premature mortality and a significant global health concern. This study aimed to investigate the trends in alcohol use disorders, including incidence, mortality, and disability-adjusted life years (DALYs), in Estonia from 1990 to 2019 using data from the Global Burden of Disease (GBD) dataset. Methods: The GBD study is a comprehensive epidemiological research effort that analyzes various causes of death, diseases, injuries, and risk factors across multiple countries and territories. In this study, we utilized the GBD dataset to estimate annual incidence, prevalence, mortality, years of life lost, years living with disabilities, and DALY rates by gender and age from 1990 to 2019. To assess the trends in these indices, including DALY, incidence, mortality, and 1-mortality-to-incidence ratio (1-MIR), joinpoint regression analysis was employed. This allowed for the identification of significant changes in trends at specific time points and the calculation of annual percent change between these points. Results: Our findings revealed an overall decreasing trend in the incidence rate of alcohol use disorder over the study period. However, the average DALY, mortality, and 1-MIR trends did not exhibit significant variation during this time. Moreover, we observed a more substantial decline in alcohol use disorders among men compared to women from 1990 to 2019. Specifically, the incidence of alcohol use disorders demonstrated a significant increase from 1990 to 2000, followed by a decline from 2010 to 2018, and continued to decrease from 2017 to 2019. Conclusion: This study provides important insights into the changing trends of alcohol use disorders, including incidence and mortality, in Estonia from 1990 to 2019. Our findings indicate a decreasing pattern over time, suggesting a positive shift in alcohol consumption behavior. Additionally, we observed that men had higher rates of MIR, DALY, mortality, and incidence of alcohol use disorders compared to women. These results emphasize the need for targeted health prevention programs to sustain and further promote the downward trend in alcohol-related disorders.

Abstract Background Changes that occur during pregnancy and after that during breastfeeding induce some symptoms similar to metabolic syndrome (MetS) risk factors. This study aims to determine the relationship between pregnancy, as well as the duration of breastfeeding and MetS controlling the effect of other risk factors like hypertension, glucose intolerance, triglyceride, central obesity, and reduction of high-density lipoprotein in women of Fasa Persian Cohort Study. Materials and methods In this cross-sectional study, 5015 women aged 35–70 years were investigated in the Sheshdeh region from 2016 to 2021, and the information related to the disease symptoms was collected through questionnaires, examinations, and laboratory tests. MetS was calculated based on two guidelines according to adult treatment panel III (ATP III) and international diabetes federation (IDF) methods. For reporting the data, the odds ratio with its 95% confidence interval was used. In order to eliminate the effect of confounders, logistic regression was used. Results Prevalence of MetS showed a descending trend in women with up to two pregnancies and it reached 22.6% and 22.4% using ATPIII and IDF methods respectively, while with an increase in the number of pregnancies of more than two, MetS prevalence was ascending. The prevalence of MetS did not have any specific trend across various breastfeeding duration groups. Multivariate analysis approved that the odds ratio of developing MetS in comparison with women who had two pregnancies was significantly increasing trend when the pregnancy counts increased. Conclusion The chance of developing MetS based on both IDF and ATP III methods after adjustment for confounding effects would grow with an increase in the number of pregnancies to more than two and breast-feeding of more than seven years. It is recommended that women with more than two pregnancies or the long duration of breast-feeding women undergo a specialized examination to investigate and control MetS problems so that future diseases could be prevented.