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Abstract: Red flag warnings (RFWs) are issued by the U.S. National Weather Service to alert fire and emergency response agencies of weather conditions that are conducive to extreme wildfire growth. Distinct from most weather warnings that aim to reduce exposure to anticipated hazards, RFWs may also mitigate hazards by reducing the occurrence of new ignitions. We examined the efficacy of RFWs as a means of limiting human-caused wildfire ignitions. From 2006 to 2020, approximately 8% of wildfires across the western United States and 19% of large wildfires (≥40 ha) occurred on days with RFWs. Although the occurrence of both lightning- and human-caused wildfires was elevated on RFW days compared to adjacent days without RFWs, we found evidence that modification of short-term behavioral choices on RFW days may reduce the number of certain human-caused ignitions (e.g., debris burning). By contrast, there is limited historical evidence that RFWs reduce the number of ignitions caused by habitual behaviors (e.g., smoking) or infrastructure (e.g., power lines). Furthermore, the conditional probability of a human-caused wildfire becoming a large wildfire was 33% greater on days with RFWs, underscoring the value of wildfire prevention on these days. While RFWs are helpful in certain cases, our results suggest that their efficacy as a wildfire prevention measure has been somewhat limited in the western United States. As the biophysical wildfire potential and the density of people living in wildfire-prone areas increase, so do the benefits of improved wildfire early warning systems that complement other wildfire mitigation and adaptation efforts.

Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA.

The flow duration curve is a signature catchment characteristic that depicts graphically the relationship between the exceedance probability of streamflow and its magnitude. This curve is relatively easy to create and interpret, and is used widely for hydrologic analysis, water quality management, and the design of hydroelectric power plants (among others). Several mathematical expressions have been proposed to mimic the FDC. Yet, these efforts have not been particularly successful, in large part because available functions are not flexible enough to portray accurately the functional shape of the FDC for a large range of catchments and contrasting hydrologic behaviors. Here, we extend the work of Vrugt and Sadegh (2013) and introduce several commonly used models of the soil water characteristic as new class of closed-form parametric expressions for the flow duration curve. These soil water retention functions are relatively simple to use, contain between two to three parameters, and mimic closely the empirical FDCs of 430 catchments of the MOPEX data set. We then relate the calibrated parameter values of these models to physical and climatological characteristics of the watershed using multivariate linear regression analysis, and evaluate the regionalization potential of our proposed models against those of the literature. If quality of fit is of main importance then the 3-parameter van Genuchten model is preferred, whereas the 2-parameter lognormal, 3-parameter GEV and generalized Pareto models show greater promise for regionalization.

A strategy is presented to incorporate prior information from conceptual geological models in probabilistic inversion of geophysical data. The conceptual geological models are represented by multiple-point statistics training images (TIs) featuring the expected lithological units and structural patterns. Information from an ensemble of TI realizations is used in two different ways. First, dominant modes are identified by analysis of the frequency content in the realizations, which drastically reduces the model parameter space in the frequency-amplitude domain. Second, the distributions of global, summary metrics (e.g. model roughness) are used to formulate a prior probability density function. The inverse problem is formulated in a Bayesian framework and the posterior pdf is sampled using Markov chain Monte Carlo simulation. The usefulness and applicability of this method is demonstrated on two case studies in which synthetic crosshole ground-penetrating radar traveltime data are inverted to recover 2-D porosity fields. The use of prior information from TIs significantly enhances the reliability of the posterior models by removing inversion artefacts and improving individual parameter estimates. The proposed methodology reduces the ambiguity inherent in the inversion of high-dimensional parameter spaces, accommodates a wide range of summary statistics and geophysical forward problems.

The quest for a more powerful method for model evaluation has inspired Vrugt and Sadegh (2013) to introduce ‘‘likelihood-free’’ inference as vehicle for diagnostic model evaluation. This class of methods is also referred to as Approximate Bayesian Computation (ABC) and relaxes the need for a residual based likelihood function in favor of one or multiple different summary statistics that exhibit superior diagnostic power. Here we propose several methodological improvements over commonly used ABC sampling methods to permit inference of complex system models. Our methodology entitled DREAM(ABC) uses the DiffeRential Evolution Adaptive Metropolis algorithm as its main building block and takes advantage of a continuous fitness function to efficiently explore the behavioral model space. Three case studies demonstrate that DREAM(ABC) is at least an order of magnitude more efficient than commonly used ABC sampling methods for more complex models. DREAM(ABC) is also more amenable to distributed, multi-processor, implementation, a prerequisite to diagnostic inference of CPU-intensive system models.

The ever increasing pace of computational power, along with continued advances in measurement technologies and improvements in process understanding has stimulated the development of increasingly complex hydrologic models that simulate soil moisture flow, groundwater recharge, surface runoff, root water uptake, and river discharge at different spatial and temporal scales. Reconciling these high-order system models with perpetually larger volumes of field data is becoming more and more difficult, particularly because classical likelihood-based fitting methods lack the power to detect and pinpoint deficiencies in the model structure. Gupta et al. (2008) has recently proposed steps (amongst others) toward the development of a more robust and powerful method of model evaluation. Their diagnostic approach uses signature behaviors and patterns observed in the input-output data to illuminate to what degree a representation of the real world has been adequately achieved and how the model should be improved for the purpose of learning and scientific discovery. In this paper, we introduce approximate Bayesian computation (ABC) as a vehicle for diagnostic model evaluation. This statistical methodology relaxes the need for an explicit likelihood function in favor of one or multiple different summary statistics rooted in hydrologic theory that together have a clearer and more compelling diagnostic power than some average measure of the size of the error residuals. Two illustrative case studies are used to demonstrate that ABC is relatively easy to implement, and readily employs signature based indices to analyze and pinpoint which part of the model is malfunctioning and in need of further improvement. ©2013. American Geophysical Union. All Rights Reserved.

Abstract Background The significant burden of cardiovascular diseases underscores the necessity for identifying novel predictive markers that can forecast both cardiovascular diseases and mortality. In recent years, TyG-obesity-related parameters have gained special attention in this regard. This study aimed to assess the association between TyG-waist circumference (TyG-WC) and cardiovascular diseases and mortality. Methods A comprehensive search was performed in databases including PubMed, Scopus, and Web of Science from their inception until October 6, 2024. The key outcomes of interest included all-cause mortality, cardiovascular mortality, cardiovascular diseases, myocardial infarction, stroke, coronary artery diseases, peripheral artery diseases, and heart failure. The pooled risk ratio (RR) with corresponding 95% confidence intervals (CI) was calculated. Meta-analysis was carried out using StataMP 14.0. Results A total of 17 studies were included in the analysis. The number of participants ranged between 2,224 and 95,342. The meta-analysis revealed that TyG-WC is significantly associated with an increased risk of all-cause mortality, cardiovascular mortality, cardiovascular diseases, myocardial infarction, stroke, coronary artery diseases, and peripheral artery diseases. However, only one study addressed the relationship between TyG-WC and heart failure with a positive correlation. Conclusion This study indicates that TyG-WC could serve as a promising predictor of cardiovascular diseases, along with cardiovascular and all-cause mortality. Given its accessibility, TyG-WC may be a practical tool for screening purposes.

In this research, a modern method for making foam patterns, which is one of the most important steps of casting with a lost foam pattern, has been used. A semi-automatic hot wire machine was built to cut and create foam patterns. Two types of foam patterns were prepared by manual method and cutting method with semi-automatic machine. The sizes of the obtained foam patterns were evaluated and the accuracy of each method in the production of foam patterns was compared. According to the density of the foam used in the experiments, the volume of the prepared patterns was obtained by manual and semi-automatic methods. The additional volume in the prepared patterns was calculated by both methods. The amount of economic losses caused by excess volumes was investigated. The results of the research determined that the accuracy of the sizes of the patterns prepared by the semi-automatic method was more than twice the accuracy of the patterns prepared by the manual method. The patterns prepared by the semi-automatic method had an additional volume of 1.9% compared to the volume of the original part. While the mentioned amount for foam patterns cut by the manual method was 9%. Therefore, the economic efficiency of casting parts using foam patterns made by semi-automatic method in terms of melting consumption for aluminum parts was 8% more than production parts using foam patterns made by manual method.

Objective: The present study was undertaken with the objective of developing a theoretical framework predicated upon the determinants influencing the decentralization of education in Iran. Methods: In the course of this investigation, both internal and external determinants pertinent to the decentralization of educational systems were delineated. This study employed a qualitative methodological approach, specifically utilizing inductive content analysis. The qualitative segment of the study encompassed a statistical population comprised of experts specializing in educational management, as well as education administrators from the provinces of East Azerbaijan and West Azerbaijan. From this cohort, a selection of 20 knowledgeable experts with substantial experience in educational management was made through purposeful sampling. These individuals engaged in research interviews that were conducted in a semi-structured format. The dependability of the interviews was substantiated by soliciting evaluations from professionals in the domain of educational management. The data obtained from the interviews were meticulously analyzed and systematically coded. Results: The outcomes of the final conceptual framework revealed that the factors identified within the qualitative component encompassed both internal and external variables influencing the decentralization of education in Iran. Out of the 858 identified indicators, 28 primary components were discerned, and these dimensions and components were subsequently designated in accordance with the prevailing literature, context, and theoretical frameworks. Conclusions: By leveraging the insights garnered from these findings, a model was articulated, emphasizing the critical factors underlying the decentralization of education in Iran.

AbstractThe aim of the current research is to provide a model of emotional and rational appeals in video marketing based on social networks. According to its purpose, the research method is applicable, and descriptive in terms of data collection. The statistical population of the research includes 200 male and female students between the ages of 25 and 35, selected as a sample and exposed to two types of short-term video advertisement stimuli. The sampling method is random. Data was collected through a questionnaire. SPSS and PLS software were used in data analysis. In this study, through the structural equation approach, it was determined that there is a significant difference between emotional and rational appeals in "attracting" the attention of the audience to video advertisements, which depends on the type and intensity of the emotional or rational load used in content production, which have different effect in creating "behavioral conflict" in the target audience. Also, the effect of the attention-attracting variable was identified as a mediating variable in creating behavioral engagement.Extended Abstract IntroductionVideo marketing or what is known as "Video Marketing" has occupied a major and important part of the world of content marketing and, accordingly, digital marketing (Stringfellow, 2017), and for this reason, it has attracted the attention of many researchers. The emergence of the Internet, as the top technology of the 80s, has significantly affected marketing and its sub-branches, especially video marketing (Sheldon, 2013), and one of these effects is the intersection of video marketing and social networks or Social Media (Luo et al, 2013).Social media, which are equivalent to the term social networks in this study, are one of the most important known components that increasingly involve almost all aspects of human life, from education to business (Alalwan et al, 2017); despite the differences that Wikipedia has made between the network and social media (Bennett, 2013).Many things have been mentioned about the importance of the impact of social media on marketing; for example, it has been stated that if the customer cannot first get help from the information available on social media to make a decision, then in the second step, he will go to search engines, and this shows the position of social media in the behavior leading to customer purchase (Jameel, 2023). For this reason, the function of various types of emotional and rational appeals as two key parameters in the effectiveness of video marketing in social networks will be the subject of this study, and we will try to find the answer to the question: Is there any significant difference between emotional and rational appeals in the effectiveness of video marketing or not?Theoretical FrameworkAttention in video marketing"Attracting attention" is so important that researchers consider the goal of video marketing only to attract the attention of the audience and change his decision to continue watching (Arantes et al, 2018), and consider it as the main factor of competition in this type of marketing (Sheldon, 2013). For this reason, in this research, attracting attention was considered as the beginning of the process of creating effectiveness in video marketing.Emotional and intellectual appealsSeveral factors have been introduced in marketing as the factor to attract the attention of the audience, among which the role of emotional appeals can be mentioned as an important factor in attention (Melin, 2014). Excitement is one of the necessary elements to achieve attention in video marketing in such a way that "The customer's decision-making journey in video marketing begins with a video ad" (Maenhout, 2023), and only does positive emotions that affect the audience's attention, but negative stimuli also play an important role in attracting the viewer's attention (Vaish et al, 2008) (Rozin & Royzman, 2001).The engagementCrane & Sornette (2008) were the first people who introduced the term "engagement" in today's sense as a valuable tool for measuring the effectiveness of video marketing, in their definition: "audience engagement in video is a chance to share video and increase the number of visits" (Crane & Sornette, 2008).Dong et al, (2023) investigated the four main factors of short branded video content (content matching, informational relevance, storytelling, and emotionality), customer engagement (likes, comments, and shares) as well as the moderating effect of publication time (morning). The experimental results show that the content characteristics of short videos significantly affect consumer engagement. In addition, the time of release of videos significantly moderated the relationship between emotionality of short videos and consumer engagement. Content posted in the morning increased the positive effect of warmth, excitement, and happiness on consumer engagement compared to content posted in the afternoon. The practical implications of the findings provide new insights for the dissemination of products and brand culture through short videos. The authors suggest that companies using brand videos consider content matching, informational relevance, storytelling, and emotionality in their design. Authenticity/value from a broader perspective; this study develops a new method to comprehensively evaluate short-brand video content based on four dimensions (content matching, informational relevance, storytelling, and emotional), examines and the value of these dimensions to create social media marketing.Hosseini (2023) investigated the impact of video marketing and its various strategies on consumer behavior. The results showed that the experience of video marketing strategies does not have a significant effect on the behavior of Ramak dairy products consumers. Also, brand identity, advertising, usefulness of information, and service quality of video marketing strategies have a significant effect on the behavior of consumers of dairy products of the studied company. After presenting the results, suggestions and strategies are presented in the field of findings related to the research.Research methodologyThe research method is applicable in terms of purpose, and descriptive in terms of data collection. The statistical population of the research includes 200 male and female students between the ages of 25 and 35, selected as a sample and exposed to two types of short-term video advertisement stimuli. The sampling method is random. Data was collected through a questionnaire.Research findingsSPSS and PLS software were used in data analysis. In this study, through the structural equation approach, it was determined that there is a significant difference between emotional and rational appeals in "attracting" the attention of the audience to video advertisements, which depends on the type and intensity of the emotional or rational load used in content production, which have different effect in creating "behavioral conflict" in the target audience. Also, the effect of the attention-attracting variable was identified as a mediating variable in creating behavioral engagement.ConclusionThe present research was conducted with the aim of presenting the model of emotional and rational appeals in video marketing based on social networks with the approach of structural equations. The results of this research is aligned with the results of Dong et al, (2023), Asgharzadeh et al, (2023), Hosseini (2023), Zhuang (2022), Yazdani Kachuei et al, (2022), Taleghani et al, (2022), Sasanpour & Amini Lari (2021), Song et al, (2021), and Casais & Pereira (2021). Zhuang (2022) showed that screen mode, subtitles, and video title length significantly affect consumer engagement by influencing the central information processing pathway; and music and narration positively influence consumer engagement by functioning in the peripheral information processing pathway. This study contributes to the social media marketing and consumer engagement literature by uncovering the influencers for consumer engagement in the context of a brand's short-form video. More importantly, this study provides practical tactics for brands to create compelling videos to attract engagement and achieve marketing goals.Thus, based on the findings of this study, it is suggested:Firstly, to increase the effectiveness of video marketing programs, one should always be optimistic about the use of emotional appeals, although this appeal has a special relationship with many other factors, including demographics.Secondly, although the use of emotional appeals is considered to be a kind of anchor and support point for the effectiveness of video advertisements, the abilities of rational appeals should not be neglected.

AbstractThe purpose of this research is to compile and validate the curriculum of religious education in the preschool period. The research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of the type of data, and is a sequential exploratory combined approach. The statistical population in the qualitative section includes 16 curriculum studies experts, Islamic education experts, and instructors of this course; and in the quantitative section, it includes 200 preschool children. Sampling was done purposefully and according to the theoretical saturation of the data. The tools of data collection are semi-structured interviews and questionnaires. Theme analysis was used to collect information in the qualitative stage, and structural equation modeling in the quantitative stage. The data obtained from the interviews were analyzed using Nvivo version 8 software in the form of thematic analysis-based, inductively on the "theme network" technique, and during the stages of coding (open, central and selected), and the proposed model of religious education for preschool children was compiled and its assumptions were used to prepare a survey questionnaire in the quantitative stage and evaluate its validity. To ensure the validity of the instrument, expert consensus was used using Delphi method and Cronbach's alpha method was used to determine reliability (α = 0.83). The data were collected with convergent validity (AVE > 0.5) and composite reliability (CR > 0.7), and were analyzed by PLS software to validate the model from structural equation modeling using partial least squares. The results showed that the identified characteristics can predict religious education curriculum components in a favorable way (R2=0.552). Finally, the fit of the overall research model was confirmed (GOF=0.438).Extended abstractIntroductionEducation is the most important and basic aspect of human life. In addition to the role and function of the family as an effective and important factor in children's issues and problems (Abbass Ghorbani & Naghdi, 2020), today the role of educational systems, especially education, is not limited only in learning science, developing skills, cognitive abilities, and preparing children and teenagers to entering higher education degrees, but its cultural and educational function is very important in the progress and development of societies (Nasiri et al, 2022). Considering the role of education and the duties of the school in achieving religious goals and responding to the needs of society, religious education has been considered as the most important goal in the current educational and official systems (Vaghari Zamharir et al, 2017). Religious education or divine education is the most important part of Islamic education and the philosophy of the mission of the prophets, and the most basic duty of the divine parents and guardians and educators of the Islamic human society. Religious education, so to speak, is an aspect of the educational process that oversees the development of cognitive, emotional, and practical (experiential) dimensions in terms of commitment to religious standards; in other words, religious education is the development and strengthening of religious beliefs, religious moods and emotions, and adherence to rituals, and it considers religious customs to realize a civilized personality (Keyhan, 2021). In the current research, an attempt is made to answer the question: what is the design of the religious education curriculum model at the preschool level in Iran's education?Theoretical frameworkDesired Religious education has always been one of the main concerns of education systems. Religious education or divine education is the most important part of Islamic education and the philosophy of the mission of prophets and the most basic duty of divine guardians and guardians and educators of Islamic human society (Ashrafi Soltan-Ahmadi et al, 2021). Religious education is to provide natural and introspective opportunities for people to discover the sublime brilliance of religion through personal experience and inspiration from their nature (Sharifi Darvaze et al, 2021). In another definition, religious education considers the growth and strengthening of religious beliefs, religious moods and emotions, and adherence to religious rites and customs to realize a civilized personality (Kiyomarthi, 2013).Samadi (2022) in his research on the causes of the vulnerability of the educational system in developing students' religious identity, came to the conclusion that the policy weakness of the educational system in the training of human resources, religious education curriculum, and lack of attention to developmental requirements of students and poor performance of religious institutions, media and peer groups are among the vulnerability factors in developing religious identity.Zare et al, (2022) investigated the analysis of religious education in students. The results indicated that special attention should be paid to the exemplary role of the teacher, scientific and religious competence, acceptability and popularity of the teacher for the students, and they also emphasized on expressing religious issues in a simple and understandable way and in the form of specific behaviors and using various educational tools such as books as well as holding attractive ceremonies and competitions.Research methodologyThe research method is applicable in terms of purpose, mixed (qualitative-quantitative) in terms of the type of data, and is a sequential exploratory combined approach. The statistical population in the qualitative section includes 16 curriculum studies experts, Islamic education experts, and instructors of this course; and in the quantitative section, it includes 200 preschool children. Sampling was done purposefully and according to the theoretical saturation of the data. The tools of data collection are semi-structured interviews and questionnaires. Theme analysis was used to collect information in the qualitative stage, and structural equation modeling in the quantitative stage.Research findingsThe data obtained from the interviews were analyzed using Nvivo version 8 software in the form of thematic analysis-based, inductively on the "theme network" technique, and during the stages of coding (open, central and selected), and the proposed model of religious education for preschool children was compiled and its assumptions were used to prepare a survey questionnaire in the quantitative stage and evaluate its validity. To ensure the validity of the instrument, expert consensus was used using Delphi method and Cronbach's alpha method was used to determine reliability (α = 0.83). The data were collected with convergent validity (AVE > 0.5) and composite reliability (CR > 0.7), and were analyzed by PLS software to validate the model from structural equation modeling using partial least squares. The results showed that the identified characteristics can predict religious education curriculum components in a favorable way (R2=0.552). Finally, the fit of the overall research model was confirmed (GOF=0.438).ConclusionThe present research was conducted with the aim of compiling and validating the religious education curriculum in the preschool period. The results of this research is aligned with the results of Aghili et al, (2019), Naderi et al, (2019), Rahayu & Munadi (2019), Mansoorian (2022), Shamshiri & Nowzari (2018), Placklé et al, (2014), Maleki (2013), and Mikailo & Sultan Al-Goraei (2015). Mansoorian (2022) has emphasized in his research that by using the problem solving method, students' understanding and perception can be brought to the level of abstract perception. It means that they should not consider what they learn as reserved; but they understand and touch those contents with all their being. Shamshiri & Nowzari (2018) in presenting the local model of religious education for preschool children emphasized and necessitated the method of grounding by providing practical learning activities for the active participation of children in the process of religious education.

This paper examines the role of entrepreneurship in energy engineering education, emphasizing the importance of incorporating entrepreneurial skills into the curriculum. Given the global challenges associated with sustainable energy, entrepreneurship is identified as a crucial driver for developing innovative technologies in the energy sector. The paper addresses the obstacles and challenges of integrating entrepreneurship into energy engineering education programs and highlights the need for curriculum reform, enhanced industry collaboration, and the utilization of appropriate resources to foster entrepreneurship. Additionally, the paper underscores the importance of balancing technical and entrepreneurial content within energy engineering courses, and the necessity of providing suitable incentives and opportunities for students to engage in entrepreneurial activities. The societal demand for sustainable energy solutions and the pressing global challenges underscore the need for energy engineering students to develop entrepreneurial skills. This development is presented as a vital strategy for driving economic growth and fostering innovation in the energy industry. In conclusion, the paper argues that the integration of entrepreneurship into energy engineering education is essential for preparing students to contribute effectively to the energy sector’s evolution and to address the urgent need for sustainable and innovative energy solutions

Abstract Direct observation of tumor-immune interactions is unlikely in tumors with currently available technology, but computational simulations based on clinical data can provide insight to test hypotheses. It is hypothesized that patterns of collagen evolve as a mechanism of immune escape, but the exact nature of immune-collagen interactions is poorly understood. Spatial data quantifying collagen fiber alignment in squamous cell carcinomas indicates that late-stage disease is associated with highly aligned fibers. Our computational modeling framework discriminates between two hypotheses: immune cell migration that moves (1) parallel or (2) perpendicular to collagen fiber orientation. The modeling recapitulates immune-extracellular matrix interactions where collagen patterns provide immune protection, leading to an emergent inverse relationship between disease stage and immune coverage. Here, computational modeling provides important mechanistic insights by defining a kernel cell-cell interaction function that considers a spectrum of local (cell-scale) to global (tumor-scale) spatial interactions. Short-range interaction kernels provide a mechanism for tumor cell survival under conditions with strong Allee effects, while asymmetric tumor-immune interaction kernels lead to poor immune response. Thus, the length scale of tumor-immune interaction kernels drives tumor growth and infiltration.

BackgroundOverweight and obesity are a global pandemic, contributing to death and disability-adjusted life-years. Obesity is a major factor in the onset of chronic inflammatory diseases (CIDs). Yet, several knowledge gaps remain: For several CIDs, inconsistent results have been reported, relating to their obesity-imposed risk, data on most rare CIDs remain unavailable, sex differences and racial disparities remain mostly unaddressed.MethodsA large-scale cohort study compared the risk of developing 46 CIDs in individuals with overweight/obesity (n=3,101,824) to an equal number of non-overweight/obese individuals. Propensity score matching optimized between-group comparability, and sensitivity analyses assessed study robustness.ResultsThe risk of developing any CID was 28.48% in overweight/obese individuals versus 17.55% in non-overweight/obese controls, with a hazard ratio (95%-confidence interval) of 1.52 (1.509-1.521, p

Abstract Over the years, prediction techniques for the highly variable angular velocity of the Earth represented by Earth's rotation (UT1-UTC) and length-of-day (LOD) have been continuously improved. This is because many applications like navigation, astronomy, space exploration, climate studies, timekeeping, disaster monitoring, and geodynamic studies, all rely on predictions of these Earth rotation parameters. They provide early warning of changes in the Earth's rotation, allowing various industries and scientific fields to operate more precisely and efficiently. Thus, in our study, we focused on short-term prediction for UT1-UTC (dUT1) and LOD. Our prediction approach is to combine machine learning (ML) technique with efficient evolutionary computation (EC) algorithms to achieve reliable and improved predictions. Gaussian process regression (GPR) is used as the ML technique with genetic algorithm (GA) as the EC algorithm. GA is used for hyperparameter optimization of GPR model as selecting appropriate values for hyperparameter are essential to ensure that the prediction model can accurately capture the underlying patterns in the data. We conducted some experiments with our prediction approach to thoroughly test its capabilities. Moreover, two forecasting strategies were used to assess the performance in both hindcast and operational settings. In most of the experiments, the data used are the multi-technique combinations (C04) generated by International Earth Rotation and Reference Systems Service (IERS). In one of the experiments, we also investigated the performance of our prediction model on dUT1 and LOD from four different products obtained from IERS EOP 20 C04, DTRF20, JTRF20 and USNO. The prediction products are evaluated with real estimates of the EOP product with which the model is trained. The combined excitations of the atmosphere, oceans, hydrology, and sea level (AAM + OAM + HAM + SLAM) are used as predictors because they are highly correlated to the input data. The results depict the highest performance of 0.412 ms in dUT1 and 0.092 ms/day in LOD, on day 10 of predictions. It is worth noting that the later predictions were obtained by incorporating the uncertainty of the input data as weights in the prediction model, which was a novel approach tested in this study. Graphical Abstract

Background. Universities have established a system of periodic evaluation and accreditation in order to manage the quality of education, enhance their physical resources and educational equipment, and improve their human resources. This study aimed to conduct an analysis of second-generation institutional accreditation standards for medical sciences universities from the experts' perspective. Methods. The present research was a qualitative study with an inductive approach conducted at Tabriz University of Medical Sciences between the years 2019- 2022. The sampling method was purposeful, and data were collected through semi-structured interviews with 34 managers, officials of development offices, and experts involved in the accreditation programs of educational institutions. As for study validation, the four criteria of Guba and Lincoln (i.e., credibility, dependability, confirmability, and transferability) were used. As for data analysis, MAXQDA 20, a software program designed for computer-assisted qualitative and mixed-methods data analysis, was used. The interview texts were analyzed using the content analysis method. Results. The results of the content analysis identified 204 initial codes, 9 subcategories, and 2 main categories (the strengths and weaknesses of the content). The subcategories of strengths included standardization, improvement of educational processes, development of assessment areas, restructuring of educational structure, benchmarking global experiences, and transparency in criteria. The subcategories of weaknesses included structural weaknesses, lack of validity, and ambiguity in criteria. Conclusion. The process of analyzing the content of accreditation standards for educational institutions periodically and obtaining regular feedback will lead to the enhancement of the standards and the realization of activities, ultimately resulting in the improvement of educational quality and the dynamism of the community.

Abstract In 2021, the International Earth Rotation and Reference Systems Service (IERS) established a working group tasked with conducting the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) to assess the current accuracy of EOP forecasts. From September 2021 to December 2022, EOP predictions submitted by participants from various institutes worldwide were systematically collected and evaluated. This article summarizes the campaign's outcomes, concentrating on the forecasts of the dX, dY, and dψ, dε components of celestial pole offsets (CPO). After detailing the campaign participants and the methodologies employed, we conduct an in-depth analysis of the collected forecasts. We examine the discrepancies between observed and predicted CPO values and analyze their statistical characteristics such as mean, standard deviation, and range. To evaluate CPO forecasts, we computed the mean absolute error (MAE) using the IERS EOP 14 C04 solution as the reference dataset. We then compared the results obtained with forecasts provided by the IERS. The main goal of this study was to show the influence of different methods used on predictions accuracy. Depending on the evaluated prediction approach, the MAE values computed for day 10 of forecast were between 0.03 and 0.16 mas for dX, between 0.03 and 0.12 mas for dY, between 0.07 and 0.91 mas for dψ, and between 0.04 and 0.41 mas for dε. For day 30 of prediction, the corresponding MAE values ranged between 0.03 and 0.12 for dX, and between 0.03 and 0.14 mas for dY. This research shows that machine learning algorithms are the most promising approach in CPO forecasting and provide the highest prediction accuracy (0.06 mas for dX and 0.08 mas for dY for day 10 of prediction). Graphical abstract