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Abstract This study investigates a carbon-based all-perovskite tandem solar cell (AP-TSC) with the structure ITO, SnO₂, Cs₀.₂FA₀.₈Pb(I₀.₇Br₀.₃)₃, WS₂, MoO₃, ITO, C₆₀, MAPb₀.₅Sn₀.₅I₃, PEDOT: PSS, Carbon. The bandgap configuration of the cell is 1.75 eV/1.17 eV, which is theoretically limited to 36% efficiency. The effectiveness of embedding cubic plasmonic metallic nanoparticles (NPs) made of Gold (Au) and Silver (Ag) within the absorber layers to eliminate the requirement for thicker absorber layers, decrease manufacturing costs and Pb toxicity is demonstrated in our analysis. This analysis was conducted using 3D Finite Element Method (FEM) simulations for both optical and electrical calculations. Prior to delving into the primary investigation of the tandem structure, a validation simulation was conducted to demonstrate the accuracy and reliability of the simulations. Notably, the efficiency mismatch observed during the validation simulation, specifically in relation to the incorporation of metallic nanoparticles (NPs), amounted to a mere 0.01%. To mitigate the potential issues of direct contact between metallic NPs and perovskite materials, such as increased thermal and chemical instability and recombination at the NP surface, a 5 nm dielectric shell was applied to the NPs. The incorporation of cubic core-shell Ag NPs resulted in a 15.32% enhancement in short-circuit current density, from 16.39 mA/cm² to 18.90 mA/cm², and a 15.68% increase in overall efficiency, from 26.9 to 31.12%. This research paves the way for the integration of core-shell metallic NPs in AP-TSCs, highlighting a significant potential for efficiency and stability improvements. In a dedicated section the band alignment of the sub-cell was addressed. Additionally, a thermal investigation of the proposed tandem structure was conducted, demonstrating the robustness of the proposed AP-TSC. Finally, the sensitivity analyses related to input parameters and the challenges associated with large-scale fabrication of the proposed AP-TSC were extensively discussed.

We explicitly determine all connected graphs whose Laplacian matrices have at most four eigenvalues different from 1 and 2.

Abstract The presence of antibiotic pollutants in water and wastewater can cause significant risks to the environment in different aspects. Therefore, antibiotics need to be removed from water. This study investigates the adsorption of nalidixic acid (NA), a common antibiotic, using bovine serum albumin nanoparticles (BSA NPs). These NPs were synthesized via desolvation technique and characterized using SEM, DLS, FT-IR, and UV-Vis spectroscopy. The effects of adsorbent dosage (0.02–0.9 mg), initial NA concentration (30–80 mg L− 1) and contact time (0.5–24 h) on adsorption efficiency were considered. Adsorption isotherms and kinetics were determined experimentally. The Freundlich isotherm best described the adsorption equilibrium, while the pseudo-second-order kinetic model accurately represented the adsorption process. Thermodynamic parameters confirmed the spontaneous and exothermic nature of NA adsorption onto BSA NPs. Under optimal conditions, BSA NPs achieved a removal efficiency of 75% for NA with a maximum adsorption capacity of 240 mg g− 1. These results demonstrate the potential of BSA NPs as an effective adsorbent for removing NA from aqueous solutions.

Introduction: Precise acid etching of the tooth surface is a significant factor in the strength of the bracket bond. The current study aimed to determine the effect of different application times of acid etching on the shear bond strength of metallic orthodontic brackets under controlled laboratory conditions. Methods: Sixty intact premolar teeth were selected for this in-vitro study. The samples were divided into four groups based on the different acid etching times: 20, 25, 30, and 35 seconds. The teeth surfaces were etched with 37% phosphoric acid according to the specified times, and brackets were bonded to the enamel surface using GC Ortho connect composite. Shear bond strength values of the brackets was measured using a Universal Testing Machine. The amount of Adhesive Remnant Index was also observed using a stereomicroscope. Additionally, the surface roughness of the tooth enamel after bracket bonding was examined using SEM. The results related to shear bond strength were analyzed with one-way ANOVA statistical test and using SPSS version 16 software. The non-parametric Kruskal Wallis (Wallis-Kruskal) test was also used to check the frequency of ARI index. Results: The highest shear bond strength was observed at 25 seconds, and the lowest at 30 seconds. However, there was no significant difference in the shear bond strength of brackets was observed among the four groups (p=0.186). The highest ARI score was observed at 35 seconds, and the lowest at 20 seconds. No statistically significant difference was observed in the ARI scores in different groups. SEM images revealed that the enamel surface porosity was highest at 20 seconds. Conclusion: The results indicate that the shear bond strength and ARI at different acid etching times do not significantly differ. Therefore, orthodontic bracket bonding can be performed with less acid etching time.

Abstract Introduction Children presenting to the emergency department (ED) often require procedural sedation and analgesia (PSA) prior to procedures. Although ketamine is used widely for PSA safely, there is a risk of adverse effects. Among them, vomiting is significant as it occurs in about 10% of patients and can potentially endanger the airway. Because there is evidence that post-operative complications might be due to anxiety prior to the operation, this study aims to investigate the association between pre-procedural anxiety and vomiting in the ED. Methods In this cohort study, a convenient sample of children aged 2 to 14 years who were a candidate for PSA with ketamine in the ED were enrolled. Anxiety was evaluated using the short version of the modified Yale preoperative anxiety scale (mYAS). Vomiting was recorded during the period of hospitalization in the ED and 24 h after discharge by a phone call. Association between anxiety level and vomiting was analyzed using the independent samples t-test and multivariable logistic regression was used to control for covariates. Results 102 children were enrolled and 93 were included in final analysis. The mean age of participants was 3.95 ± 1.79 years and 55.9% were male. According to the mYAS, the mean score of anxiety was 48.67 ± 21.78 in the waiting room and 59.10 ± 23.86 in the operating room. The mean score of anxiety was 58.3±25.3 and 51.0±20.7 in the vomiting and non-vomiting groups, respectively. At least one episode of vomiting was reported in 23 children of which, 19 took place in the hospital and 4 after discharge. No significant association was observed between pre-procedural anxiety and the occurrence of vomiting. On univariate regression model, the odds ratio of the association between mean anxiety and vomiting was 1.02 (CI 95%: 0.99–1.04) (P-value: 0.16). On the multivariable logistic regression model, after adjusting for all the covariates, the odds ratio was 1.03 (CI 95%: 1.0–1.05) (P-value: 0.05). Conclusion The present study showed that anxiety before procedural sedation and analgesia with ketamine in children was not associated with the incidence of vomiting.

Background: The rapid advancement of digital technology and information has revolutionized teaching approaches, research practices, and evidence retrieval in medical academic settings, highlighting the necessity for adequate digital literacy (DL) among students. This research delved into examining digital literacy and its various components within the medical sciences student population.Method: Surveys about medical sciences students’ DL and related topics were identified by searching of the Web of Science, Scopus, Embase, and PubMed databases. Meta-analysis was done using CMA V.3.3.Results: Out of the 6773 initially identified articles, 54 studies were selected for the final synthesis. The studies showed that the DL of medical science students has improved over the past two decades. However, there were differences in the components of DL, with computer literacy ranking the highest and search literacy ranking the lowest. Result of meta-analysis of 28 studies for estimation of the rate of skilled students in performing tasks using digital devices showed that most students were skilled in using Word Processing (78%) and Presentation (68%) software, while fewer were skilled in using Spreadsheets (49%) and Email (34%).Conclusion: While in recent years, medical science students have made significant progress in DL, a gap remains between their expected competency and the current situation. The COVID-19 pandemic accelerated the adoption of digital technologies for online learning, positively impacting students’ DL. This valuable experience should guide future educational practices, emphasizing continued online and blended learning, and focusing on integrating ICT courses into the medical science curriculum.

Purpose: With the growing complexity of organizational environments in both the private and public sectors, the need for inter-organizational cooperation has become increasingly important. Consequently, in recent decades, the concept of an ecosystem has been introduced to facilitate connections between organizations across various fields of management science. With the rapid development of Industry 4.0, various ecosystem combinations—such as innovation ecosystems and entrepreneurship ecosystems—have emerged. Among these, digital ecosystems represent a newer and rapidly evolving field that has garnered significant attention from researchers in recent years. The purpose of this study is to conduct a scientometric analysis and visualize a map of the knowledge structure, co-authorship network, and thematic clusters related to digital ecosystems within the field of management, as indexed in the Web of Science database.Methodology: This research is an applied study that employs common techniques in scientometrics, specifically co-occurrence analysis methods. To obtain the primary data, the keyword "digital ecosystem" was searched in the Web of Science database for the years 2000 to 2023. A total of 4,496 articles published in this database were analyzed as the statistical population. The data were analyzed using VOS Viewer and BibExcel software to extract the knowledge map and co-occurrence network. In this research, the authors' fields and indexers' keywords were utilized for synonym analysis. After retrieving the records, a total of 14,140 keywords were obtained from the 4,496 examined records for co-occurrence analysis. Of these, 1,173 frequent words, identified based on Bradford's law with a frequency of 5 or higher, were selected for co-word analysis.Findings: 14,140 keywords were identified in this analysis, with 1,173 instances exhibiting the same timing in the co-occurrence analysis of five words. The results of this six-cluster analysis indicate that the most recent emerging topics in 2021 are associated with a range of innovative information technologies, intelligent relationships, entrepreneurial ecosystems, digitalization, digital servitization, ecosystem architecture, and their impacts. The co-occurrence of the keywords shows the highest frequency in research related to the digital ecosystem from a management perspective. The United States, the United Kingdom, and China have made the most significant contributions to research on this topic. The findings reveal that Parida V. is the most active author, and the fields of computer science and information systems are recognized as the most prolific areas of study. Additionally, terms such as identified as the most frequently used keywords, which were categorized into six clusters based on their co-occurrence. An analysis of international collaboration revealed eight clusters, with the highest level of collaboration occurring in the first cluster, which includes 20 countries, while the least collaboration is observed in cluster eight, which comprises only three members.Conclusion: The scientometric study and analysis of the co-authorship network and thematic clusters related to the concept of the digital ecosystem from a management perspective, as sourced from the Web of Science database, revealed that 99.13% of the scientific documents in this field were published in English. The highest frequency of articles on this topic occurred in 2022, marking the peak of research in this area, particularly concerning the Fourth Industrial Revolution and emerging technologies. Based on the time series analysis, three distinct historical periods can be identified. The first period focuses on the design and initial planning of the ecosystem, while the second period encompasses themes such as management, innovation, dynamics, platforms, value creation, strategy, competition, and performance. In the third stage, the focus shifts to emerging information technologies, intelligent relationships, digitization, services, computer architecture, and the entrepreneurial ecosystem. The examination of the knowledge structure of the digital ecosystem from a managerial perspective indicates that it can serve as a roadmap for future research. Additionally, the analysis of the synonym network for the topic under investigation in the Web of Science database identified six thematic clusters: management of emerging technologies, innovation and entrepreneurship, co-creation, strategy, digital transformation, and networks. In countries' collaboration network, the United Kingdom has the highest number of collaborative links, followed by the United States, France, Spain, China, and Finland, which exhibit greater international collaboration than other nations. Meanwhile, the volume of documents published by Iran and its level of international collaboration warrant careful consideration. To enhance the country's development in the Fourth Industrial Revolution and leverage digital capabilities, Iran needs to reassess its international collaboration policies and actively participate in international scientific arenas to foster scientific growth and produce high-quality research publications.

Abstract Budesonide (BDS) a steroid-based anti-inflammatory drug widely prescribed for various diseases, has a low aqueous solubility. In this study, we investigated cosolvency approach to study the thermodynamic specifications related to the solubility of BDS at the temperature range of 293.2–313.2 K in (1-propanol + water) mixtures applying the shaking flask method. The predictive power of different mathematical models for experimental data in the cosolvency systems was evaluated. For this purpose, the linear and nonlinear mathematical equations such as van’t Hoff model (as a linear model), Buchowski-Ksiazczak equation (as a non-linear), CNIBS/R–K and MRS models (as a linear model for solvent composition at an isothermal condition), modified Wilson model (as a non-linear model for isothermal condition), the Jouyban-Acree model (as a model that considers temperature and solvent composition), and Jouyban-Acree-van’t Hoff model (as a model with no further input data) were studied. Also, the Williams-Amidon excess Gibbs energy model was investigated. In addition, the related apparent thermodynamics of the BDS dissolution process in the desired temperature such as Gibbs free energy, enthalpy, and entropy, were computed by the corresponding equations. Moreover, based on the inverse Kirkwood-Buff integrals, it is demonstrated that BDS is preferentially solvated by water in water-rich mixtures. The accuracy of the fitness was evaluated with mean relative deviations (MRDs%) for back-calculated molar BDS solubility data. The result showed that the maximum solubility of BDS was obtained at 0.7 mass fraction of 1-propanol at all temperatures. Thermodynamic studies demonstrated that BDS dissolution procedures were obtained as endothermic and entropy-driven in almost all cases. The overall MRDs% values for the back-computed BDS solubility in the aqueous mixture of 1-propanol based on van’t Hoff model, Buchowski-Ksiazczak equation, CNIBS/R–K model, modified Wilson model, Jouyban-Acree model, Jouyban-Acree-van’t Hoff model, MRS model, and Williams-Amidon excess Gibbs energy model were found 1.93%, 1.80%, 11.68%, 33.32%, 12.30%, 9.24%, 10.70%, and 6.57%, respectively. Graphical Abstract

Background and Objective: Extensive research is underway to find effective treatments for clinical manifestations of COVID-19. Given the proven effectiveness of certain micronutrients, such as vitamins, in antimicrobial activity and immune system modulation for treating or preventing infectious diseases, the potential role of vitamins in the treatment or prevention of COVID-19 should be considered. The present study aimed to assess the correlation between serum folate levels in patients hospitalized with COVID-19 and the severity of the disease, emphasizing the importance of relevant awareness regarding different folate levels. Materials and Methods: In this cross-sectional study, folate levels were measured in 117 COVID-19-infected patients hospitalized from January 2020 to November 2020 at Sina Hospital, Hamadan. Patients were categorized based on disease severity into three groups: mild to moderate, severe, and critical. Group differences were statistically analyzed using appropriate tests. Results: Our study revealed a statistically significant difference in folate levels among the three examined groups. Logistic regression analyses demonstrated that for every one microgram per milliliter increase in folate, the probability of severe COVID-19 symptoms decreased by 23%. The large area under the ROC curve (0.864) indicates that mortality in COVID-19 patients can be well predicted based on folate levels. Conclusion: As evidenced by the obtained results, it can be stated that a reduction in serum folate levels among hospitalized patients with COVID-19 is common and is recognized as a predictive factor for COVID-19-related mortality. Consequently, monitoring folate levels and considering supplementation, if necessary, should be considered for hospitalized COVID-19 patients.

Radiation detection at high count rate suffers from pulse pile-up, where the counting data and energy information of the system are affected by the overlapping of the system output pulses. There exist various pile-up correction strategies to recover the true information of the pulses, among which pulse-tail extrapolation is a well-known method focused on in this study.Present work aims to use a mono-exponential model for extrapolating the pileup-distorted trailing edge of a pulse, to provide a reference line for calculating the true amplitude of its subsequent overlapping pulse. To this goal, the auto-regression on linear operations (ARLO) method is examined and compared with two integration-based methods (the Foss and the Matheson methods), as well as the non-linear least squares (NLS) method.Despite a higher sensitivity to noise, the ARLO method was able to provide a simple, non-iterative solution with a performance over 400 times faster than the NLS algorithm, according to the analysis of a high count rate set of experimental pulses from a NaI(Tl) detection system. Foss and Matheson methods also provided solutions reasonably faster than NLS (but not surpassing ARLO), performing exactly the same as each other with results very close to NLS, benefiting from their non-iterative nature.

This mixed-methods study, adopting a sequential explanatory design, aimed to (a) investigate the effect of situation comedies (sitcoms) on Iranian EFL learners’ critical reasoning as well as action and (b) explore learners’ attitudes about the way sitcoms could influence their critical reasoning and action. In this mixed-methods study, sixty learners were randomly selected and assigned to a control and an experimental group. While the control group participated in regular classes, the experimental group watched sitcoms. Before and after the treatment, the Persian version of the California Critical Thinking Skills Test, Form B (CCTST) (Khalili & Hossein Zadeh, 2003) was utilized to measure participants’ reasoning skills. To assess critical action, the Activism Orientation Scale (Corning & Myers, 2002) was used. Quantitative data were analyzed using Analysis of Covariance (ANCOVA). Some of the participants in the experimental group were interviewed about the way sitcoms affected their criticality. Qualitative data were analyzed using content analysis. The result of ANCOVA tests showed a significant difference between the two groups regarding their criticality. In support of quantitative results, the analysis of the interviews confirmed the effectiveness of sitcoms in improving learners’ criticality. Qualitative findings showed that sitcoms enhanced learners’ critical reasoning through inference and evaluation and raised their awareness of social issues and their eagerness to participate in life-changing activities. Integrating findings from the two phases confirmed that sitcoms increased learners’ awareness of the world and deepened their understanding of diversities.

The Restricted Mean Survival Time (RMST) serves as a valuable and extensively utilized metric in clinical trials. However, its application becomes intricate when dealing with data affected by length-biased sampling, rendering traditional inference strategies inadequate. To overcome this challenge, we advocate for the adoption of nonparametric techniques. One notably promising approach is the Empirical Likelihood (EL) method, which furnishes robust results without the need for stringent parametric assumptions. In practical scenarios, the underlying sampling distributions often remain elusive, necessitating adjustments in the case of parametric methodologies. The EL method has demonstrated its efficacy in addressing such complexities. Consequently, this paper introduces the EL method for computing RMST in situations involving both length-biased and right-censored data. Additionally, we introduce the concept of adjusted empirical likelihood (AEL) to further enhance the coverage probability, particularly when dealing with smaller sample sizes. To gauge the performance of the EL and AEL methods, we conduct simulations and rigorously compare their results. The findings unequivocally demonstrate that AEL-based confidence intervals consistently provide superior coverage probability when juxtaposed with EL-based intervals. Lastly, we substantiate the practical applicability of our proposed method by employing it in the analysis of a real dataset.

Abstract Background Understanding the factors influencing disease progression and severity in pediatric COVID-19 cases is essential for effective management and intervention strategies. This study aimed to evaluate the discriminative ability of clinical and laboratory parameters to identify predictors of COVID-19 severity and mortality in hospitalized children. Methods In this multicenter retrospective cohort study, we included 468 pediatric patients with COVID-19. We developed a predictive model using their demographic, clinical, and laboratory data. The performance of the model was assessed using various metrics including sensitivity, specificity, positive predictive value rates, and receiver operating characteristics (ROC). Results Our findings demonstrated strong discriminatory power, with an area under the curve (AUC) of 0.818 for severity and 0.873 for mortality prediction. Key risk factors for severe COVID-19 in children include low albumin levels, elevated C-reactive protein (CRP), lactate dehydrogenase (LDH), and underlying medical conditions. Furthermore, ROC curve analysis highlights the predictive value of CRP, LDH, and albumin, with AUC values of 0.789, 0.752, and 0.758, respectively. Conclusion Our study indicates that laboratory values are valuable in predicting COVID-19 severity in children. Various factors, including CRP, LDH, and albumin levels, demonstrated statistically significant differences between patient groups, suggesting their potential as predictive markers for disease severity. Implementing predictive analyses based on these markers could aid clinicians in making informed decisions regarding patient management.

Abstract Background Colorectal cancer (CRC) is a significant malignancy with widespread implications. Despite progress in surgical interventions for rectal cancer, improvements in overall prognosis remain disproportionate. Standard preoperative chemoradiation, while established as the standard treatment for the majority of rectal cancers, exhibits limited effectiveness in enhancing disease-free survival (DFS) and mitigating distant metastases, particularly in cases of locally advanced rectal cancer (LARC). Methods This randomised clinical trial assessed 286 patients with LARC in two paralleled groups. Group A underwent six courses of neoadjuvant MFOLFOX chemotherapy, chemoradiation, surgery, and six adjuvant chemotherapy cycles. Group B received concurrent chemoradiation, surgery, and twelve adjuvant chemotherapy cycles. Patient evaluations were achieved at multiple stages of treatment and follow-up. Results Group A had significantly lower local recurrence (11.64%) than Group B (21.74%, P = 0.025). The distant metastasis rate in Group A (8.90%) was lower than in Group B (20.29%) but was not significant (p = 0.143). More patients in Group A experienced downstaging (80.82% vs. 60.87%, p

Abstract Background Infertility in men causes problems in various aspects of their lives, including personal, family and social life. One of the most important of these problems is anxiety. Anxiety in infertile men can affect their health, quality of life, and response to treatment, highlighting the significance of anxiety in these men. Thus, this systematic review and meta-analysis was conducted to investigate the prevalence of anxiety symptoms in infertile men. Methods To conduct this review study, two researchers independently searched international databases such as PubMed, Cochrane Library, Web of sciences, Scopus, PsyINFO, and the Google scholar search engine in English without considering any time limit until January 2, 2024. Keywords such as "anxiety," "infertility," "prevalence," and "epidemiology" were used, taking into account the specific search method of each database. Using the Newcastle–Ottawa Scale (NOS), the quality of the articles was evaluated by two researchers independently. Results In the systematic part of the study, 27 studies were included, and given the variety of measurement tools (8 different tools) used to investigate anxiety symptoms in infertile men, 24 studies were analyzed in five subgroups of tools. The pooled prevalence of anxiety symptoms in infertile men was 21.37% (95% CI: 15.73–27.02). The lowest and highest prevalence of anxiety in infertile men were related to the Beck anxiety inventory (BAI) and Depression Anxiety Stress Scales (DASS), accounting for 7.08% (95% CI: 3.27–10.90) and 34.90% (95%CI: 28.90–40.90) values respectively. This prevalence was 19.80% (95%CI: 9.01–30.59) for the Hospital Anxiety and Depression Scale (HADS), 30.06% (95%CI: 18.59–41.52) for the Spielberger Trait Anxiety Inventory (STAI-T), and 18.52% (95%CI: 7.76–29.29) for the Self-Rating Anxiety Scale (SAS). Conclusion The results of this systematic review and meta-analysis indicated that the prevalence of anxiety symptoms in infertile men requires special attention to healthcare planning. The healthcare system of different countries should evaluate the symptoms of anxiety in infertile men and take appropriate measures to reduce them according to the culture of the countries. It is recommended that all infertile couples be assessed for anxiety symptoms using a standardized tool during their initial evaluation.