Effective Factors Research Articles

Explain of the effective marketing factors in the electronic banking service in Iraq and the intention to use it

Explain of the effective marketing factors in the electronic banking service in Iraq and the intention to use it

Occlusal disharmony promotes anxiety-like behaviours by suppressing Sirt1.

Previous studies have indicated that occlusal disharmony (OD) can promote anxiety-like behaviours. However, the specific molecules involved in the development of anxiety-like behaviours and their underlying mechanisms remain unknown. OD was produced by anterior crossbite of female mice. We measured the anxiety levels of mice in each group and screened the hippocampal mRNA expression profiles of mice in the control group and OD group. The role of target mRNA in OD-induced anxiety-like behaviours was evaluated and we preliminarily explored the possible downstream pathways. The results suggested that OD can induce and promote anxiety-like behaviours with/without chronic unpredictable mild stress. We found that Sirt1 was significantly downregulated within the hippocampus in OD mice. In addition, the downregulation of Sirt1 within the hippocampus in OD and control mice promoted anxiety-like behaviours, increased acetylated histone H3 expression and decreased Dnah12 transcription levels. In contrast, in OD mice subjected to an injection of resveratrol, there was a remission of anxiety-like behaviours and an upregulation of Sirt1 in the hippocampus, the effects of which were accompanied by decreased acetylated histone H3 expression and increased Dnah12 transcription levels. OD leads to increased sensitivity to chronic stress in mice, resulting in anxiety-like behaviours. During this process, Sirt1 acts as an effective factor in the regulation of OD-induced anxiety-like behaviours. OD, as a stressor, could induce anxiety-like behaviours. It investigates the impact of OD (a stressor) on the molecular genetic of the pathophysiology of major neuropsychiatric disorders.

NeoaPred: a deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-human leukocyte antigen complexes.

Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen. Identifying immunogenic neoantigens is crucial for cancer immunotherapy development. However, the accuracy of current bioinformatic methods remains unsatisfactory. Surface and structural features of peptide-HLA class I (pHLA-I) complexes offer valuable insight into the immunogenicity of neoantigens. We present NeoaPred, a deep-learning framework for neoantigen prediction. NeoaPred accurately constructs pHLA-I complex structures, with 82.37% of the predicted structures showing an RMSD of < 1 Å. Using these structures, NeoaPred integrates differences in surface, structural, and atom group features between the mutant peptide and its wild-type counterpart to predict a foreignness score. This foreignness score is an effective factor for neoantigen prediction, achieving an AUROC (Area Under the Receiver Operating Characteristic Curve) of 0.81 and an AUPRC (Area Under the Precision-Recall Curve) of 0.54 in the test set, outperforming existing methods. The source code is released under an Apache v2.0 license and is available at the GitHub repository (https://github.com/Dulab2020/NeoaPred).

Thermal effectiveness analysis of heat exchange tube with staggered louver-punched V-baffles

An experiment has been conducted to ascertain the optimal approach for enhancing the convective transmission of heat in a heat exchanger tube by periodically positioning staggered louver-punched V-baffle (SLVB) vortex generators on a perforated tape. The louver-punched V-baffles were staggered and set up with two patterns: V-apex directing upstream (VU) and downstream (VD), and each featured a louver-punched aperture to minimize friction loss. The aim of this work was to rise the thermal effectiveness as well as the Nusselt number ratio (NuR) at optimal effectiveness in order to cut down on overall dimension of heat exchangers. Consequently, the research findings focused on behaviors of heat transmission and frictional loss, incorporating generated entropy, through a variety of Reynolds numbers (Re) spanning 29,270 to 4762. The SLVB elements were designed and placed in VU and VD forms using three relative pitches (PR = 0.5, 1.0, and 1.5) as well as six louver-flapped angles (θ = 0°, 10°, 20°, 30°, 45°, and 90°), all at the same attack angle (α = 52°), a baffle height ratio (BR = 0.3) and a louver size ratio (LR = 0.73). As demonstrated by the experiment, the θ = 20° generated the best heat transfer of all PR values, up to 6.55 times greater than the smooth tube, despite having a lower friction loss than the θ = 0° (solid baffle). At PR = 0.5, θ = 20° and smaller Re, the optimum thermal effectiveness factor (TEF) and NuR values were, respectively, about 2.65 and 6.55 for the VU SLVB and 2.52 and 6.04 for the VD one. It implied that the TEF strategies can be utilized to anticipate the best effectiveness under identical scenarios. Also, correlations for the critical parameters, namely, Nu, f, and TEF, were estimated and documented.

Quantification of Color Variation of Various Esthetic Restorative Materials in Pediatric Dentistry.

The goal of this paper is to find an association between the staining capacity of dental restorations used in pediatric patients and food items and to develop an optimum model to predict the most informative factor that causes the highest amount of color change through machine learning algorithms. Color changes in restorative materials occur as a result of intrinsic and extrinsic factors, such as the type of restorative material, food items used, polished status of the material, and time interval. This was an "in vitro study" conducted at Aligarh Muslim University, Aligarh, Uttar Pradesh, India. The study included 200 specimens, that is, 40 in each group A (orange juice), group B (Amul Kool Café), group C (Pepsi), group D (Amul Kesar Milk), and group E (artificial saliva). The materials were glass ionomer cement (GIC), resin-modified glass ionomer cement (RMGIC), microhybrid composite resin, and nanohybrid composite resin. These were further divided into polished and unpolished groups. The optimum modeling of the prediction of color change in materials by different effective factors was done by machine learning decision tree. We applied two algorithms: Chi-square automatic interaction detector (CHAID) and classification and regression tree (CART). In prediction modeling in the decision tree by CHAID and CART, color change is taken as the dependent variable, and group (type of restorative material), food items, time interval, and polished status are taken as independent variables. The various beverages caused significant color variation due to different pigmentation agents. The agent that caused the highest color change was Kool Café. The Kesar Milk had the lowest pigmentation capacity. The greatest color variation was found on Glasionomer FX-II submerged in Pepsi and the least on Ivoclar Te-Econom Plus in Kesar Milk. The mean absolute error for the training dataset in the CART model and CHAID model is 0.379 and 0.332, and for the testing data set, it is 0.398 and 0.333, respectively. Therefore, the prediction of color change by the CHAID model is optimum, and we found that the restorative materials have a maximum predictor importance of 0.86 (86%), time interval 0.07 (7%), food items 0.04 (4%), and polished status has the least importance, that is, 0.03 (3%). The staining capacity of restorative material highly depends on the material itself, the initial time interval, and least on the food items used. The clinical performance of dental restorations could be affected by various beverages consumed by children. This study thus provides important clinical insights into esthetic dentistry by offering valuable information on long-term color stability and the effect of polishing on common esthetic restorative materials used in pediatric dentistry. Varshney P, Khan SY, Jindal MK, et al. Quantification of Color Variation of Various Esthetic Restorative Materials in Pediatric Dentistry. Int J Clin Pediatr Dent 2024;17(7):754-765.

Dust Source Susceptibility in the Lower Mesopotamian Floodplain of Iraq

The identification of susceptible dust sources (SDSs) based on the analysis of effective factors (i.e. dust drivers) is considered to be one of the primary and cost-effective solutions to deal with this phenomenon. Accordingly, this study aimed to identify SDSs and delineate their drivers using remote sensing data and machine learning (ML) algorithms in a hotspot area in the Lower Mesopotamian floodplain in southern Iraq. To model SDSs, a total of 15 environmental features based on remote sensing data such as topographic, climatic, land use/cover, and soil properties were considered as dust drivers and fed into the four well-known ML algorithms, including linear discriminant analysis (LDA), logistic model tree (LMT), extreme gradient boosting (XGB)-Linear, and XGB-Tree-based. Dust emission hotspots were identified by visual interpretation of sub-daily MODIS-Terra/Aqua true color composite imagery (2000-2021) to train (70%) and validate (30%) ML algorithms. Considering the variability of the spatial-temporal patterns of SDSs as a result of changes in dust drivers, the modeling process was carried out in four periods, including 2000-2004, 2005-2007, 2008-2012, and 2013-2021. Our results show that dust events in the study area occur most frequently in April, June, July, and August. Overall, all ML algorithms performed well and provided reliable results for identifying SDSs. However, the XGB-Linear provided the most reliable results with an average area under curve (AUC) of 0.79 for the study periods. Precipitation was determined as the most important dust driver. The SDS maps produced can be used as a basis for the development of rehabilitation plans in the study area to mitigate the adverse effects of dust storms.

L-carnitine cause to increase cell proliferation of C-Kit+ hematopoietic progenitor cells via decreasing the PI3K and FOXO-1 protein expression

BackgroundStem cell-based therapy has emerged as an attractive approach for regenerative medicine. Poor survival and maintenance of the cells used in regenerative medicine are considered as serious barriers to enhance the efficacy of the cell therapy. Using some antioxidants has been reported to prevent the aging of stem cells, and finding effective factors to reduce the senescence of these cells has impressive potential in cell therapy. The PI3K pathway adversely regulates the transcription factors known as FOXO, which are thought to have an inhibitory influence on cell proliferation. By downregulating FOXO and other targets, PI3K signaling controls the growth of cells. For this reason, the aim of the present study is to investigate the effect of L-carnitine (LC) as antioxidant on the cell proliferation and the protein expression of PI3K and FOXO. MethodsFor understanding the in vitro effect of LC on the PI3K and FOXO-1 expression of C-kit+ hematopoietic progenitor cells, the bone marrow mononuclear cells were isolated, and C-kit+ cells was enriched by the magnetic-activated cell sorting (MACS). Next, the identification of enriched C-kit+ cells were done by flowcytometry and immunocytochemistry. Then, C-kit+ cells were treated with 0.2 mM LC, the cells were collected at the end of the treatment period (48 h), and the proteins were extracted. In the following, the protein expression of PI3K and FOXO-1 was measured by western blotting. In addition, flowcytometry was done to assess the Ki-67 expression as a key marker for cell proliferation investigation. Results0.2 mM LC cause to significantly decrease in the protein expression of PI3K and FOXO-1 (*P<0.05 and **P<0.01, respectively). Also, the expression of Ki-67 was significantly increased in the presence of 0.2 mM LC (***P<0.001). ConclusionBriefly, LC can be considered an effective factor in increasing the proliferation of C-kit+ cells via some signaling pathways.

Association between composite dietary antioxidant index and migraine in American young women: insights from NHANES 1999-2004 cross-sectional data.

Excessive oxidative stress is one of the key pathophysiological mechanisms underlying migraine, and increasing antioxidant intake has proven to be an effective strategy for the prevention and improvement of migraine symptoms. To explore the relationship between the composite dietary antioxidant index (CDAI) and the occurrence of migraine attacks. Cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) spanning 1999-2004 were utilized. Logistic regression, stratified analysis, and restricted cubic spline (RCS) models were employed to investigate the association between CDAI and migraine attacks. A total of 8,137 adults aged ≥20 were enrolled, comprising 1,610 patients with migraine and 6,527 non-migraine individuals. After adjusting for all covariates, CDAI was negatively correlated with migraine. In the overall participants, compared with the CDAI Q1 (-5.83 to -2.14) group, the adjusted odds ratio (OR) for migraine in Q3 (-0.59 to 1.53) and Q4 (1.53-44.63) groups were 0.71 [95% confidence interval (95% CI): 0.54-0.92, p = 0.011] and 0.64 (95% CI: 0.47-0.87, p = 0.005), respectively. After stratifying by age and gender, the protective effect was more pronounced in females aged 20-50, with adjusted OR for Q3 (-0.59 to 1.53) and Q4 (1.53-44.63) groups of 0.60 (95% CI: 0.40-0.90, p = 0.013) and 0.48 (95% CI: 0.30-0.78, p = 0.003), respectively. The RCS curve indicated a nonlinear relationship between CDAI and migraine in females aged 20-50, with a threshold of 0.006. CDAI is negatively correlated with migraine attacks, and a higher CDAI may be an effective protective factor in preventing migraine attacks, especially in women aged 20-50.

Effective factors on the development of bodybuilding and fitness clubs in Mashhad, Iran

The aim of this study was to investigate the effective factors in the development of bodybuilding and fitness clubs in Mashhad, Iran. This study was practical in terms of its goals, with an exploratory and analytical implementation method, classifying it as qualitative research. A total of 27 individuals were targeted and selected as samples until theoretical saturation was reached, including athletes, coaches, managers of fitness and bodybuilding clubs, members of the academic faculty of sports sciences at universities, experts from sports departments, and seniors in the field of bodybuilding in Mashhad. For data analysis, the data coding method was employed within a grounded theory framework. Based on the results, the factors affecting the development of bodybuilding and fitness clubs in Mashhad were identified in four main categories, ten sub-categories, and seventy-two components, according to expert opinions and a literature review in this field. These factors included: 1) Facility and infrastructure factors, 2) Financial and economic factors, 3) Professionalism factors, 4) Human resource factors, 5) Event factors, 6) Scientific and educational factors, 7) Organizational and managerial factors, 8) Motivational factors, 9) Media factors, and 10) Cultural-social factors. By creating and facilitating this model, the development steps for Mashhad's fitness clubs will be implemented to better align with future implementation conditions.

Effective Factors for Promoting Student Engagement in Educational Activities

This study investigates the Effective Factors for Promoting Student Engagement in Educational Activities at the Faculty of Education, Helmand University. The research employed a quantitative approach and utilized a sample of (278) students from a total population of (995), selected using the Rao Soft Sample Size Calculator. The study had a (95%) confidence interval and a (5%) error level. The primary objective of this research was to explore the effective factors for promoting students engagement in educational activities. Two questionnaires were used, one focusing on students' educational activities and the other on teachers' emotional behavior. Simple linear regression was applied to analyze the effects of the independent variable (teachers' emotional behavior) on the dependent variable (students' educational activities), with data analyzed using SPSS and Excel. The findings indicate that teachers' emotional behavior significantly influences students' educational performance and activities. Specifically, the study found that teachers' strict and serious behavior (83%), establishing communication with students' parents (80%), and friendly and sincere relationships between students and teachers (76%) have notable effects on students' educational activities. This suggests that when teachers exhibit a gentle, sincere, friendly, and supportive attitude towards students during the teaching process, students are more likely to attend classes enthusiastically and actively participate in their lessons.

Beyond conventional models: integer and fractional order analysis of nonlinear Michaelis-Menten kinetics in immobilised enzyme reactors

PurposeThis study explores the immobilisation of enzymes within porous catalysts of various geometries, including spheres, cylinders and flat pellets. The objective is to understand the irreversible Michaelis-Menten kinetic process within immobilised enzymes through advanced mathematical modelling.Design/methodology/approachMathematical models were developed based on reaction-diffusion equations incorporating nonlinear variables associated with Michaelis-Menten kinetics. This research introduces fractional derivatives to investigate enzyme reaction kinetics, addressing a significant gap in the existing literature. A novel approximation method, based on the independent polynomials of the complete bipartite graph, is employed to explore solutions for substrate concentration and effectiveness factor across a spectrum of parameter values. The analytical solutions generated through the bipartite polynomial approximation method (BPAM) are rigorously tested against established methods, including the Bernoulli wavelet method (BWM), Taylor series method (TSM), Adomian decomposition method (ADM) and fourth-order Runge-Kutta method (RKM).FindingsThe study identifies two main findings. Firstly, the behaviour of dimensionless substrate concentration with distance is analysed for planar, cylindrical and spherical catalysts using both integer and fractional order Michaelis-Menten modelling. Secondly, the research investigates the variability of the dimensionless effectiveness factor with the Thiele modulus.Research limitations/implicationsThe study primarily focuses on mathematical modelling and theoretical analysis, with limited experimental validation. Future research should involve more extensive experimental verification to corroborate the findings. Additionally, the study assumes ideal conditions and uniform catalyst properties, which may not fully reflect real-world complexities. Incorporating factors such as mass transfer limitations, non-uniform catalyst structures and enzyme deactivation kinetics could enhance the model’s accuracy and broaden its applicability. Furthermore, extending the analysis to include multi-enzyme systems and complex reaction networks would provide a more comprehensive understanding of biocatalytic processes.Practical implicationsThe validated bipartite polynomial approximation method presents a practical tool for optimizing enzyme reactor design and operation in industrial settings. By accurately predicting substrate concentration and effectiveness factor, this approach enables efficient utilization of immobilised enzymes within porous catalysts. Implementation of these findings can lead to enhanced process efficiency, reduced operating costs and improved product yields in various biocatalytic applications such as pharmaceuticals, food processing and biofuel production. Additionally, this research fosters innovation in enzyme immobilisation techniques, offering practical insights for engineers and researchers striving to develop sustainable and economically viable bioprocesses.Social implicationsThe advancement of enzyme immobilisation techniques holds promise for addressing societal challenges such as sustainable production, environmental protection and healthcare. By enabling more efficient biocatalytic processes, this research contributes to reducing industrial waste, minimizing energy consumption and enhancing access to pharmaceuticals and bio-based products. Moreover, the development of eco-friendly manufacturing practices through biocatalysis aligns with global efforts towards sustainability and mitigating climate change. The widespread adoption of these technologies can foster a more environmentally conscious society while stimulating economic growth and innovation in biotechnology and related industries.Originality/valueThis study offers a pioneering approximation method using the independent polynomials of the complete bipartite graph to investigate enzyme reaction kinetics. The comprehensive validation of this method through comparison with established solution techniques ensures its reliability and accuracy. The findings hold promise for advancing the field of biocatalysts and provide valuable insights for designing efficient enzyme reactors.

Use of artificial intelligence in the detection of coffee rust: An exploratory systematic review

Coffee rust, caused by the fungus Hemileia vastatrix, is a fungal disease that affects coffee production and quality, so its early detection is crucial to prevent massive outbreaks and protect production. This article analyzes the most effective factors, the algorithms used, the accuracy of the models, and the challenges in the detection of coffee rust, through an exploratory systematic review of 35 empirical articles obtained from Scopus, IEEE Xplore and SciELO. The review identifies that the most determinant factors for detection include humidity, temperature and the presence of shade. The most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM) and Random Forest, highlighting CNN for its ability to process and analyze images with an accuracy of 99.57%, followed by Artificial Neural Networks (ANN) with 98% and SVM with 96%. However, it is concluded that challenges remain such as the need for high quality labeled datasets, variability in environmental conditions and implementation costs. This study provides a comprehensive overview of recent advances and areas for improvement in coffee rust detection, providing information for researchers, practitioners and decision makers in the agricultural sector.

Stakeholder Perspectives and Experiences of Implementation of Accreditation and the ISO 9001 Programs in Hospitals of Iran: A Qualitative Study

Background: Accreditation and ISO 9001 programs in hospitals are widely adopted for quality control and quality improvement in healthcare. Objectives: The objective of this study is to identify stakeholders’ perspectives and experiences regarding the implementation of accreditation and ISO 9001 programs in hospitals affiliated with the Social Security Organization in Alborz Province, Iran. Methods: In this qualitative study, participants were selected based on purposive sampling. For data collection, semi-structured one-to-one interviews were conducted with hospital staff in different positions (n = 30). All interviews were digitally recorded, transcribed verbatim, and analyzed using thematic analysis. Results: Based on the analysis, 10 primary themes and 88 subthemes were identified. The 10 primary themes were categorized as experiences, strengths, weaknesses, effective factors, and recommendations for the implementation of accreditation and ISO 9001 programs in hospitals. Proper planning, benchmarking, physician involvement, increased resources, and more training are suggested for the successful implementation of accreditation in hospitals. Additionally, fostering a positive culture, focusing on the executive dimension, changing managerial attitudes, training, and supporting and motivating staff are the most important recommendations for the implementation of the ISO 9001 program. Conclusions: The results provide important insights into the dimensions of implementing accreditation and ISO programs in hospitals, which can be used by health policymakers and managers to improve the implementation of these programs in Iranian hospitals.

Internet of Thing (IOT) Based Sensor Technologies and Smart Irrigation System: An Analysis of Critical Success Factors in Emerging Markets

ABSTRACT: This study explores how smart irrigation systems can be improved by introducing advanced technologies and reducing water consumption to fight climate change. This study aims to identify the factors affecting the success of smart irrigation systems based on the Internet of Things with the mediation of optimal consumption management. This is an applied research employing mixed methodologies (qualitative-quantitative). Initially, the semi-structured interviews were carried out to obtain the effective success factors of the smart irrigation system. Analysis in the qualitative section has been conducted using the Maxqda software. For the quantitative study, a 25-statement questionnaire was applied according to the selected codes obtained with a five-point Likert scale to validate the obtained model. The questionnaire was distributed online in January 2023, and analysis was conducted using SmartPLS3 software. The technical aspects of the questionnaire were assessed using validity and reliability criteria, which can guarantee the precision of the research findings. Construct and content validity were employed in this study to examine the questionnaire’s validity. The researchers employed Cronbach’s alpha coefficient to evaluate the reliability of the measurement instrument. Besides, it can be said that the research instrument has acceptable reliability because obtained Cronbach’s alpha for all variables was greater than 0.70 and the questionnaire’s overall alpha was determined to be 0.810. Furthermore, the t-statistics for all research hypotheses is higher than 1.96 at the 95% confidence level. In this sense, the hypotheses of the research were confirmed. The Internet of Things and will change the nature of activities and services in the market. In fact, for emerging markets, the untapped and growing investment opportunities and the economic leap against the Internet of Things are so enticing and facilitating that it is impossible to resist. and neglecting it will cause irreparable damage to agility. Therefore, the success of the Internet of Things for emerging markets depends on the preconditions of integration of activities carried out by decision-making organizations, facilitation of regulations, and communication and interaction between relevant sectors, which should be considered a priority through cross-sectoral research.

Developing a Scale for Home-Visit Nurses to Start End-of-life Discussions with Cancer Patients

ContextHome-visit nurses find it challenging to determine the appropriate time to initiate end-of-life discussions with cancer patients. ObjectiveThis study aimed to develop the Timing of End-of-Life Discussions (T-EOLD) scale to help home-visit nurses determine the appropriate time to initiate end-of-life discussions with cancer patients and to test its reliability and validity. MethodsThe scale items were developed based on qualitative data extracted from interviews, literature reviews, and expert panel discussions. We conducted a preliminary study involving 93 home-visit nurses and evaluated the construct validity, consistency, and test-retest reliability of the scale. Finally, using a sample of 234 home-visit nurses, we conducted the primary study and assessed the construct validity and scale consistency. ResultsA total of 41 items were initially developed. Floor effect, item-total correlation, good-poor, and exploratory factor analysis in the preliminary and primary studies yielded a three-factor, 16-item model. The model's goodness-of-fit was CFI = 0.94, GFI = 0.90, AGFI = 0.87, and RMSEA = 0.06. Cronbach's alpha for the overall scale was 0.91. ConclusionsThe reliability and validity of the T-EOLD is acceptable, as it is an appropriate scale that home-visit nurses can use to determine the time to initiate end-of-life discussions with cancer patients. However, further study is required to examine T-EOLD's clinical utility, both nationally and internationally.

Mitigating rim bending in fused silica micro shell resonators by a chamfered mold

In this work, we addressed the bending of a micro shell resonator’s rim toward the stem during glass-blowing process. This phenomenon occurs due to increased heat loss at the mold’s edge and reduced heat flux at the side of the Gaussian heat source. To mitigate rim bending and the subsequent reduction in capacitance between the rim and 3D electrodes, which can degrade performance in micro glass-blown shell resonators for gyroscopes, we propose two potential mold solutions, using short-stem mold and chamfered mold. To quantitatively compare resonators created by these two types of molds, we calculated the noise factor for predicting its gyroscope performance. From these calculations, we find out that using the chamfered mold enables us to increase in the resonator’s effective mass, eigenfrequency, angular gain, and quality factor, consequently reducing the noise factor compared to using the short-stem mold. We also demonstrated these results by manufacturing shell resonators and measuring its vibrational characteristics. After metalization of the glass shell resonator, Q factor and eigen frequency of n = 2 vibration mode along the primary (secondary) axis were measured to 622,466 ± 6,427 (619,793 ± 6,402) and 4,343 ± 40 Hz (4,350 ± 40 Hz), respectively. This simulation process will assist us in getting insight into understanding a shell resonator as a gyroscope, designing a mold and determining experimental conditions.

Assessment Of Effective Factors In E-Learning at Islamic Azad University

The rapid growth of information and communication technology has brought significant changes to the performance of education globally through the use of learning management systems facilitated by e-learning in higher education institutions. Despite perceived advantages such as flexible and widespread access, e-learning still faces challenges like identifying the needs of students, faculty, and staff, as well as delivering quality courses. Furthermore, there is a lack of assessment of existing e-learning initiatives and the determination of critical success factors for quality. This necessitates further research into e-learning practices to fill the identified gaps. This study presents a model for assessing the quality of e-learning systems by modifying and expanding existing frameworks for quality learning with other models and frameworks. A descriptive and exploratory research approach was employed using a cross-sectional survey of 180 respondents from Islamic Azad University, Science and Research Branch. Data were collected through questionnaires and interviews using stratified sampling methods. The validity of the conceptual model was assessed using structural equation modeling and regression analysis. This study revealed that course design, content support, administrative support, user characteristics, organizational factors, and social support impact quality. This research has contributed to the body of knowledge from theoretical, methodological, and practical perspectives.

Intermediate statistics: Addressing the thermoelectric properties of solids.

We study the thermodynamics of a crystalline solid by applying intermediate statistics obtained by deforming known solid state models using the mathematics of qanalogs. We apply the resulting qdeformation to both the Einstein and Debye models and study the deformed thermal and electrical conductivities and the deformed Debye specific heat. We find that the qdeformation acts in two different ways-but not necessarily as independent mechanisms. First, it acts as an effective factor of disorder or impurity, modifying the characteristics of a crystalline structure, which are phenomena described by qbosons. Second, it also manifests intermediate statistics, namely, the Banyons (or B-type systems). For the latter case we have identified the Schottky effect, normally associated with high-T_{c} superconductors in the presence of rare-earth-ion impurities. We also find that it increases the specific heat of the solids beyond the Dulong-Petit limit at high temperature. Such an effect is usually related to anharmonicity of interatomic interactions. Alternatively, since in the q-boson's case the statistics are in principle maintained, the effect of the deformation acts more slowly due to a small change in the crystal lattice. On the other hand, Banyons that belong to modified statistics are more sensitive to the deformation. The results reported here may be verified experimentally, for instance, in experimental samples by inserting impurities, or changes in pressure or temperature if one assumes these tuning quantities are related with the q-deformation parameter.

Characterization of Taguchi optimized fiber reinforced copper slag concrete composites with strength properties

Establishing the ideal design has always been a difficulty for concrete engineers because of the inclusion of many alternative construction materials particularly when dealing with the incorporation of slag elements and fibers in concrete mix design. In this study, the Taguchi optimization method is implemented to predict the optimal mix design of fiber reinforced copper slag aggregate concrete (FRCSC) with mechanical strength properties. For this purpose, different parameters influencing mechanical strength were defined based on the available literature. The Taguchi approach was subsequently applied to develop an orthogonal array of parameters, and experimental tests were carried out in accordance with the suggested design array to acquire experimental data for each and every strength property. Three major influencing parameters like fiber length (FL), fiber percentage (FP), and copper slag content (CS) were considered in order to get optimal mix design for FRCSC. Analysis of variance was also employed to evaluate the effective factors on compressive, split-tensile, flexural, water absorption and void content properties for FRCSC. It was found that the proposed method was effective in finding the optimal mixture design from strength point of FRCSC and the experimental results indicated a significant improvement in five selected strength properties of FRCSC. From the results of Taguchi analysis, it can be concluded that, for five selected strength properties, 60% CS + 1% of fiber + 20 mm of fiber length is the optimal mix design combination for FRCSC. Finally, physical and microstructural characterization through FESEM and EDX were carried out to have a closer justification for the test results.

Advancing Fixed-Source Reactor Analysis Through OpenNode Implementation

OpenNode, a new open-source Fortran code, enables the simulation of fixed-source reactors. Powered by the nodal expansion method (NEM) and seamless Python integration, OpenNode competes with existing nuclear reactor simulation tools. The fixed-source problem plays a crucial role in radioprotection, providing flux calculations and detailed insights into heating effects and dose rates. Users can define and configure reactor models via a JSON input file, specifying critical parameters like geometry, materials, cross-section data, boundary conditions, and fixed sources. OpenNode further empowers users with a Python interface for preprocessing and postprocessing, streamlining result analysis. Distinguishing itself from conventional NEM codes, OpenNode supports three-dimensional (3D) Cartesian geometries, facilitating intricate reactor design simulations. Customization options, including mesh sizes, polynomial orders, and calculation modes, enhance precision and efficiency. In our comprehensive study, we verified OpenNode’s fixed-source mode using the 3D-IAEA reactor, comparing it with the SANM code KOMODO. The results underscore OpenNode’s exceptional accuracy in computing critical parameters, like the effective multiplication factor, power distribution, and nodal flux, within fixed-source reactors. OpenNode stands as a reliable, user-friendly tool poised to advance nuclear reactor simulations.