Al-Balqa Applied University

Metacognitive competency is a key factor in the success of hybrid learning; however, fewstudies have focused on developing comprehensive tools to assess this concept in suchcontexts. The current paper aimed to develop and validate the Metacognitive Compe-tency Scale for College Students in Hybrid Learning (MCS-HL). A sequential exploratorymixed-methods design was used to conduct the study in three phases: item development,content validation, and psychometric testing. The study also utilized Exploratory GraphAnalysis (EGA) from a network perspective. The MCS-HL consists of 34 items across sixdimensions: Metacognitive Awareness of Hybrid Learning, Strategic Goal Formulation inDual Contexts, Reflexive Adaptation to Learning Challenges, Flexible Problem-Solving inHybrid Contexts, Technology-Driven Self-Regulation, and Collaborative Metacognition inLearning Networks. The results indicated that the scale had suitable psychometric prop-erties, including construct validity, divergent validity, suitable internal consistency (Cron-bach’s alpha > 0.7), and test–retest reliability (ICC > 0.85). Furthermore, the measurementinvariance of the MCS-HL across gender was confirmed. Additionally, network analysisfurther supported the six-factor structure of the MCS-HL. In conclusion, the MCS-HL canhelp instructors identify students’ strengths and weaknesses across both genders, therebyenhancing teaching strategies and improving learning outcomes in hybrid settings. (PDF) Development and validation of the metacognitive competency scale for college students in hybrid learning (MCS-HL): insights from the network analysis perspective. Available from: https://www.researchgate.net/publication/390842847_Development_and_validation_of_the_metacognitive_competency_scale_for_college_students_in_hybrid_learning_MCS-HL_insights_from_the_network_analysis_perspective [accessed Apr 17 2025].

In this research, we study numerical solutions for a deterministic fractional seasonal influenza model. Using seasonal fluctuations and population dynamics, the model, which was formulated using the definition of Caputo fractional calculus, provides a nuanced view of the dynamics of the influenza transmission. We also demonstrate the existence and uniqueness of solutions by applying the fixed‐point theorem, which ensures the stability of our model. In addition, we employ a powerful approach to efficiently generate numerical solutions by the Laplace residual power series method. Further, we validate the precision and efficacy of our proposed approach by employing extensive numerical simulations and comparative analyses. This work advances our knowledge of fractional epidemiological models and aids in the management and containment of seasonal influenza outbreaks.

Inula viscosa (L.) Aiton [Dittrichia viscosa (L.) Greuter] (Asteraceae) is an evergreen perennial herb that grows in different regions of the Mediterranean Basin. It has been particularly used for the treatment of hypertension and diabetes in the Eastern and South-East regions of Morocco. To assess the cardiovascular effects of total aqueous extract and various fractions of Inula viscosa leaves in rat-isolated hearts and aortic rings, and to investigate the potential mechanisms of action of the most active extract(s). In Langendorff's isolated heart system, heart rate (HR) and left ventricular developed pressure (LVDP) were measured for three increasing concentrations of TAE, DCMF, EAF, BF, and AF (0.003, 0.03, and 0.3 mg/mL). Propranolol (1.5 × 10⁻⁵ M) and Verapamil (2 × 10⁻⁷ M) were used to investigate the potential mechanisms of action of both EAF and BF. In isolated intact aortic rings, four cumulative concentrations of EAF and BF (0.0001, 0.001, 0.01, and 1 mg/mL) were tested for their vasorelaxant effects. The role of the endothelium in the vasorelaxant effect of EAF was examined by denuding aortic rings. To explore the involvement of the nitric oxide (NO) pathway, β-adrenergic receptors, calcium channels, and the sarco/endoplasmic reticulum Ca²⁺-ATPase (SERCA) pump, intact aortic rings were preincubated with L-NAME (10⁻⁴ M), Propranolol hydrochloride (10⁻⁶ M), Verapamil hydrochloride (10⁻⁵ M), and Thapsigargin (10⁻⁷ M), respectively. The hypotensive effects of both BF (125 mg/kg) and EAF (125 mg/kg) were evaluated indirectly using the tail-cuff method in normotensive rats. Additionally, the antioxidant activity, as well as the total phenolic and flavonoid contents of all prepared extracts, were determined. To further investigate the antioxidant properties, computational analysis was conducted to determine the bond dissociation energies of the hydroxyl groups on the B-ring of luteolin and quercetin, which are present in EAF and BF, respectively. Finally, an UHPLC analysis was performed for BF. In isolated perfused hearts, TAE induced a dose-dependent positive inotropic effect, accompanied by mild bradycardia. EAF exhibited both positive inotropic and chronotropic effects in a concentration-dependent manner. BF demonstrated a highly dose-dependent, selective positive inotropic effect (LVDP = 76.5 ± 19.2% vs. control at 0.3 mg/mL) with no significant impact on HR. Our findings suggest that BF acts independently of β-adrenoreceptor-dependent pathways, whereas EAF may exert its effects through β-agonistic activity. Additionally, Ca²⁺ channels may play a role in the effects of both fractions. In phenylephrine-precontracted thoracic arteries, both BF and EAF induced concentration-dependent vasorelaxation, with EAF producing the most potent vasorelaxant effect (Emax = 84.16 ± 3.68%). EAF mediates an endothelium-independent vasodilatory response through inhibition of voltage-dependent Ca²⁺ channels and activation of the SERCA pump. BF also demonstrated a significant hypotensive effect in vivo. Among the various extracts, BF contained the highest total phenolic and flavonoid contents and exhibited the strongest DPPH scavenging activity (IC50 = 7.13 µg/mL). Molecular docking studies supported these findings, indicating that quercetin is more effective at scavenging free radicals than luteolin. Phytochemical study of BF revealed the presence of phenolic compounds such as chlorogenic acid, three isochlorogenic acids (A, B and C), tri-caffeoylhexaric acid, methyl 3,5-dicaffeoylquinic acid, quercetin-3-glucuronide and the new molecule 1,3,4,5-tetracaffeoylaltraric acid. This study revealed a novel and potent selective inotropic effect of the BF fraction from I. viscosa leaves, characterized by the absence of tachycardia and independence from β-adrenergic receptors in isolated rat hearts.

This study aims to examine the effect of digital transformation on the quality of accounting information in Jordanian insurance companies through the dimensions of strategic planning, leadership preparation, institutional environment, and human skills attraction. The study measures the effect of those dimensions on the quality of accounting information as a measure of relevance and faithful representation. To that purpose, a quantitative approach was used and data was collected from several Jordanian insurance companies. The data was tested using multiple regression models. The findings showed a statistically significant positive effect of digital transformation on the quality of accounting information with human skills attraction as the most significant dimension. The study also indicated that other dimensions such as strategic planning, leadership development, and institutional environment contributed to the improvement of the quality of accounting information. The findings could help the insurance companies to improve their accounting practices in the context of digital transformation. The literature has contributed to the line of research by examining the effect of digital transformation on the quality of accounting information. Using an empirical model, the study provided recommendations to industry stakeholders in that line of research.

Background Valsartan, an angiotensin receptor antagonist widely used in hypertension and heart failure management, exhibits noticeable interindividual variation in response among hypertensive patients at the University of Jordan Hospital. The angiotensinogen (AGT) gene variant M235T, a functional genetic variant, influences the renin‐angiotensin system. Aims This study aims to explore interindividual variations in the valsartan response, considering genetics, particularly the AGT M235T variant, and other nongenetic factors. Methods This cohort study involved 95 unrelated Arabic Jordanians diagnosed with essential hypertension. Systolic (SBP) and diastolic blood pressure (DBP) measurements were taken at the initiation of 160 mg valsartan and after 1 month of treatment, assessing the valsartan response for each patient. Genetic analysis of AGT M235T was done using the polymerase chain reaction‐restriction fragment length polymorphism genotyping method. Anthropometric data were collected from University of Jordan Hospital computer records. Results Valsartan response assessment revealed diverse individual responses, the response to valsartan varied, with SBP reductions from < 10 to > 70 mmHg and DBP from < 2 to 30 mmHg. Patients with homozygous AGT M235T genotypes showed a less significant response (p < 0.05) to valsartan than heterozygous and reference genotypes. Additionally, results indicated a positive correlation of age (p = 0.03) and a negative correlation of height (p = 0.02–0.04) with the valsartan response. Regression analysis demonstrated that the patients' sex significantly influenced the valsartan response (p < 0.05). Conclusions This study identifies the AGT M235T genotype as a potential genetic contributor to variability in the valsartan response. Associations with age, height, and sex underscore the importance of considering genetic and demographic factors in tailoring valsartan therapy, for advancing personalized hypertension management.

Knee osteoarthritis (KOA) is a common musculoskeletal disorder causing pain and stiffness. Kinesio tape (KT) is a flexible tape used for various musculoskeletal conditions, including KOA. This study systematically evaluates the effectiveness of KT without conventional physical therapy for KOA. A comprehensive search was conducted through PubMed, Scopus, Cochrane Library, Embase, and Web of Science from inception to March 2025 for randomized trials evaluating KT without physical therapy for KOA (Prospero: CRD42024615432). The risk of bias was assessed using the ROB-2 tool, and the data analysis was conducted using Review Manager V5.4. A total of 16 randomized trials were included. KT significantly reduced the post-treatment pain at rest (MD: -0.75, 95% CI: -1.15, -0.34) and during movement (MD: -0.92, 95% CI: -1.65, -0.20) compared to sham KT. However, KT did not demonstrate a significant effect on long-term pain reduction. Additionally, KT significantly improved the WOMAC total score (MD: -0.60, 95% CI: -1.19, -0.01) and increased knee flexion range of motion (FROM) (MD: 6.04, 95% CI: 3.13, 8.96). However, KT showed no significant effect on knee extension range of motion (MD: -0.23, 95% CI: -1.70, 1.25). No risk of publication bias observed. KT reduces pain, improves function, and enhances knee FROM in KOA patients even without physical therapy. However, its long-term effects remain uncertain. Future studies should evaluate the long-term application of KT and its integration with other KOA management strategies. Prospero ID CRD42024615432.

Recurrent spontaneous miscarriage (RSM) is a gynecological complication has multifactorial etiologies including genetic factors. However, role of thrombophilic gene polymorphisms in RSA among Jordanian women is limited. This study explores the association between polymorphisms in SERPINC1, PROC, PROS1, PROZ, F5, F13A1, and CPB2 and RSA risk in Jordanian pregnant women. Blood samples were taken from 188 women with recurrent spontaneous miscarriage (RSM) and 193 control subjects without a history of miscarriage. Genomic DNA was extracted and analyzed for polymorphisms of thrombophilic genes using Kompetitive Allele Specific Polymerase Chain Reaction. The SNPStats tool was used to assess haplotype, genotype, and allele frequencies, with chi-square (χ²) tests employed to evaluate statistical significance. A total of seven thrombophilic genes were analyzed. The rs8178607 polymorphism in PROS1 was significantly associated with RSA in Jordanian women under the allelic (OR = 2.06, p = .014), codominant (OR = 2.05, p = .021), dominant (OR = 1.27, p = .015), and overdominant (OR = 1.91, p = .03) genetic models. Additionally, significant associations in the recessive model were observed for the rs1799810 and the rs1926447 polymorphisms in PROC (OR = 1.66, p = .038) and in CPB2 (OR = 1.89, p = .046), respectively. Our data preliminary demonstrates that the rs8178607, rs1799810, and rs1926447 genotypes of PROS1, PROC, and CPB2 respectively, are associated with an increased risk of RSA among Jordanian pregnant women. Further investigations with larger cohorts and family-based analyses are essential to elucidate the genetic variation of biochemical pathways and mechanisms influences recurrent miscarriage susceptibility.

Background Warfarin therapy is commonly used to prevent thromboembolic events and cardiovascular disorders, but its effectiveness can be influenced by interactions with drugs and foods. Nurses play a crucial role in managing warfarin therapy and counseling patients on these interactions. This study aimed to assess the predictors of nurses’ knowledge regarding warfarin-nutrient and drug interactions and their competence in counseling patients on warfarin therapy. Methodology : A cross-sectional study design was used to evaluate nurses’ knowledge and counseling practices related to warfarin therapy across various healthcare institutions in Amman, Jordan. Participants included 176 registered nurses with at least one year of experience, recruited through convenience sampling from governmental, private, and educational hospitals. Data were collected through a self-administered questionnaire that assessed demographic characteristics, work-related factors, exposure to health education, and knowledge of warfarin-drug and food interactions, as well as counseling practices. Results The study found that most participants were female (58.6%) and held a bachelor’s degree (72.7%). Nurses demonstrated moderate knowledge of warfarin–drug interactions, with a mean score of 8.76 ± 2.26 out of 26. Knowledge was better for cardiac agents like atenolol (53.4%) and gastrointestinal agents (53.4%), but gaps were observed for anti-inflammatory and CNS drugs. The mean score for knowledge of warfarin–food interactions (out of 18) was 12.27 ± 3.84, with strong knowledge of non-interfering foods, but gaps in understanding foods like leafy greens, high in vitamin K. Nurses’ knowledge of counseling practices for warfarin therapy was moderate, with a mean score of 8.07 ± 2.31 out of 15. While knowledgeable about diet and adherence, gaps existed in counseling patients on missed doses and dietary restrictions. Regression analysis identified key predictors of knowledge, including education, work experience, direct patient care, self-confidence, exposure to health education, and anticoagulant training, explaining 35% of the variance in knowledge scores. A postgraduate degree, work experience, and confidence in warfarin care positively impacted knowledge, while demographic factors like age, gender, and job position had no significant effect. The findings highlight the need for educational programs and confidence-building initiatives. Conclusion The study highlights significant gaps in nurses’ knowledge of warfarin interactions, particularly with certain drugs and foods, and underscores the need for targeted education and training. However, the study is limited by its reliance on self-reported data and a convenience sampling approach, which may impact generalizability. Strengthening nurses’ understanding of warfarin management, especially regarding high-risk interactions, is essential for improving patient safety and the efficacy of anticoagulant therapy. Future initiatives should focus on structured educational programs, introducing interactive e-learning modules, regular workshops, and case-based training, as well as promoting multidisciplinary teamwork to enhance nurses’ competency in warfarin counseling and patient care.

Machine learning prediction of the mechanical properties of self-compacting concrete (SCC) reinforced with hybrid fibers, incorporating industrial wastes like fly ash and blast furnace slag, and cured under elevated heat provides a reliable and efficient alternative to traditional laboratory experiments. In this work, extensive literature review leading to the collection, sorting and curation of a global database representative of the mechanical properties of self-compacting concrete reinforced with hybrid fiber mixed with industrial wastes for sustainable construction was conducted. The collected database constituted traditional concrete components and admixtures such as Cement (C), Fly ash (FA), Slag (BFS), Fine Aggregate (FAg), Coarse Aggregate (CAg), Water (W), Superplasticizer (PL), Fiber (Fi), and Temperature (Temp.) studied under the mechanical properties such as the Compressive Strength (Fc), Tensile Strength (Fsp), and Flexural Strength (Ff). The collected 114 records were divided into training set (90 records = 80%) and validation set (24 records = 20%) following the guidelines for data partitioning for optimal performance in machine learning predictions. Different advanced machine learning methods created using “Weka Data Mining” software version 3.8.6 were applied such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)” to predict the output. The Hoffman/Gardener and SHAP techniques are used to estimate the sensitivity of the input parameter on the output. Finally, various performance metrics are used to evaluate the reliability of the models. The results show that the machine learning models show varying degrees of predictive accuracy, with the Kstar and XNV models consistently outperforming others across all mechanical properties. However, Kstar with accuracies of 96.5%, 96.0%, and 97.0% for Fc, Fsp, and Ff predictions, respectively proposed the most decisive model. Also, the Hoffman and Gardener method highlights the role of the binders, chemical additives, and curing, whereas SHAP attributes greater importance to aggregates and binder interactions.

Individual variations in the response to thiopurine-based anticancer drugs are influenced by genetic and environmental factors, making it challenging to optimize dosing and minimize toxicity. Among the key genes involved, genetic variations in the nudix hydrolase 15 (NUDT15) gene affect on thiopurine metabolism, thus influencing drug efficacy and the risk of severe adverse effects, such as myelosuppression, These variations also contribute to inter-individual differences in drug tolerance and clinical outcomes. Despite the recognized impact of NUDT15 variations, there has been limited comprehensive exploration of these variants and their clinical significance in thiopurine therapy. This review provides a thorough analysis of NUDT15 genetic variants by synthesizing findings from prior clinical studies and employing in silico analyses to predict the functional effects of variants with uncertain significance. Comprehensive analysis of NUDT15 variants and their interactions with other metabolic pathways could offer valuable insights for advancing person-alized medicine in cancer treatment. This review aims to establish a foundation for integrating NUDT15 genetic information into the clinical practice, reducing toxicity, and improved therapeutic outcomes in patients undergoing thiopurine-based chemotherapy.

Vehicle emissions of carbon dioxide significantly contribute to greenhouse gases. Recycling this CO₂ into valuable hydrocarbon products enhances vehicle mileage while reducing environmental impact, adding value to fuel consumption. This research aims to utilize the electrochemical method for conversion of CO2 to alcohols using nano-copper (Cu), silver (Ag), graphite (C), and their composites. Pure graphite, nano-copper, silver, and their composites were evaluated for the performance in CO₂ reduction. The working solution was 0.1 M potassium bicarbonate (KHCO₃) saturated with CO2 in H-type cell. The produced alcohols were continuously monitored to assess the efficiency of the electrochemical conversion process. Nano-copper electrodes showed high Faradaic efficiencies for methanol (~ 100%), ethanol (~ 100%), and hydrogen reduction. The Cu/graphite composites revealed enhanced performance, benefiting from the synergy between the CO₂ adsorption properties of graphite and the catalytic activity of copper. Mixed Cu-Ag systems, on the other hand, showed distinct electrochemical behavior through the CO-pathway reaction steps. The electrodes of graphite, copper, and their composites were characterized for their surface morphologies, crystallinity, and functional groups. X-ray diffraction (XRD) confirmed the presence of copper phases in the E21Cu79C composite, and scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) analyses provided an uneven amorphous surface with major -OH groups that can enhance the adsorption of CO2 to these electrodes. The addition of copper to graphite in E21Cu79C electrode indicated a positive increase of 4% in reduction potential from pure graphite. Pure graphite electrode provided a current density of 27.8 mA/cm², whereas Cu/graphite (E21Cu79C) demonstrated an increase of 12%. This study highlights the importance of electrode composition in optimizing CO₂ electro-reduction, offering insights into the development of more efficient catalysts for the sustainable production of alcohols from CO₂. Graphical Abstract

Magnetic resonance spectroscopy (MRS) provides a non‐invasive method for examining metabolic alterations associated with diseases. While ¹H‐based MRS is commonly employed, its effectiveness is often limited by signal interference from water, reducing the accuracy of metabolite differentiation. In contrast, X‐nuclei MRS leverages the broader chemical shift dispersion of non‐hydrogen nuclei to enhance the ability to distinguish between metabolites. This article presents the design and analysis of a dual‐resonant meandered coil for 7 Tesla magnetic resonance imaging (MRI), to simultaneously help in image hydrogen protons (¹H) and detect Phosphorus (³¹P) atomic nuclei at 298 MHz and 120.6 MHz, respectively. Both single‐channel and four‐channel configurations were designed and analyzed. The single‐channel coil integrates an LC network for dual resonance, achieving excellent impedance matching (S11 < −10 dB) and a homogeneous magnetic field distribution within the region of interest. A transmission‐line‐based matching network was implemented to optimize performance at both frequencies. The four‐channel coil was simulated using CST Microwave Studio and experimentally validated. Simulations demonstrated impedance matching and minimal mutual coupling of −38 dB at 298 MHz and −24 dB at 120.6 MHz. The measured S‐parameters confirmed these results, showing high decoupling and robust performance across all channels. The prototype featured integrated LC networks and optimized meander structures, ensuring efficient power transmission and uniform field distribution. This work highlights the effectiveness of the proposed dual‐resonant coil designs for MRS applications, offering promising potential for advanced clinical diagnostics.

Basalt fiber-reinforced concrete (BFRC) mixed with fly ash, combined with advanced machine learning techniques, offers a practical, cost-effective, and less time-consuming alternative to traditional experimental methods. Conventional approaches to evaluating mechanical properties, such as compressive and splitting tensile strengths, typically require sophisticated equipment, meticulous sample preparation, and extended testing periods. These methods demand substantial financial resources, specialized labor, and considerable time for data collection and analysis. The integration of machine learning provides a transformative solution by enabling accurate prediction of concrete properties with minimal experimental data. The methods of data collection from literature and analysis were used and 121 records were collected from experimentally tested basalt fiber reinforced concrete samples measuring the compressive and splitting tensile strengths of the concrete. Eleven (11) critical factors have been considered as constituents of the studied concrete to predict the Fc-Compressive strength (MPa) and Fsp-Splitting tensile strength (MPa), which are the output parameters. The collected records were divided into training set (96 records = 80%) and validation set (25 records = 20%) following the requirements for data partitioning for sustainable machine learning application. Seven (7) selected machine learning techniques are applied in the prediction. Further, performance evaluation indices were used to compare the models’ abilities and lastly, the Hoffman and Gardener’s technique was used to evaluate the sensitivity of the parameters on the concrete strengths. At the end of the exercise, results were collated. In predicting the compressive strength (Fc), AdaBoost similarly excels, matching XGBoosting’s validation performance with R² of 0.98 and the same MAE values. This shows the effectiveness of boosting techniques for predictive modeling in concrete strength estimation. For splitting tensile strength (Fsp), AdaBoost also outperforms most models, achieving an R² of 0.96 for training and validation phases. Its exceptionally low validation MAE of 0.124 MPa underscores its excellent generalization capabilities. Overall, XGBoosting and AdaBoost consistently demonstrate superior performance for both compressive and splitting tensile strength predictions, followed closely by KNN. These models benefit from advanced ensemble techniques that efficiently handle non-linear patterns and noise. SVR also performs admirably, whereas GEP and GMDHNN exhibit weaker predictive capabilities due to limitations in handling complex data dynamics. For the sensitivity analysis, the Hoffman and Gardener’s method of sensitivity analysis proves instrumental in identifying key drivers of strength in fiber-reinforced concrete, guiding informed decision-making for material optimization and sustainable construction practices.

Improving the energy conservation through regenerative braking enhances the overall efficiency of the electric transportation system. In order to accomplish this purpose, the most significant function is played by bidirectional power electronics converters. This research proposes a nonisolated bidirectional DC–DC converter (BDC) for electric trains that is capable of converting energy from storage devices to the traction motor side and vice versa in an efficient manner. Furthermore, the simplicity structure, low volume and weight, low cost, and the straightforward switching strategy make it an excellent choice for many high-power applications such as electric trains. An analysis of the converter and a comprehensive discussion of the proposed scheme are presented. Additionally, a 100 W experimental prototype is developed and tested. Simulation and experimental results are highly compatible with the theoretical analysis that validates the proper functionality of the converter.

Objectives Evaluating self-reported mindfulness in teaching is necessary to understand teachers’ classroom awareness and presence. Mindfulness could improve their professional practices across different educational levels. Therefore, we aimed to translate the Mindfulness in Teaching Scale (MTS) into Arabic and evaluate its psychometric properties among Arab teachers, creating the Mindfulness in Teaching Scale for Arab Teachers (MTS-AT). Mindfulness in teaching encompasses two key dimensions: interpersonal mindfulness, which involves presence and awareness in interactions with students, and intrapersonal mindfulness, reflecting self-awareness and emotional regulation in the teaching context. Method A cross-sectional study was conducted with 700 teachers from various Arab-speaking countries, utilizing an online data collection method. The construct validity of the MTS-AT was evaluated through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The reliability of the MTS-AT was assessed by examining internal consistency and stability, as well as convergent and discriminant validity. Furthermore, measurement invariance was analyzed across gender, years of teaching experience, and educational levels taught. Measurement stability was also assessed. Finally, Exploratory Graph Analysis (EGA) was employed to explore the dimensional structure of the MTS-AT. Results The construct validity and network analysis confirmed the two-factor structure of the MTS-AT among Arab educators. This model demonstrated invariance across several factors, including gender, years of teaching experience, and educational levels taught. The reliability and convergent and discriminant validity metrics were satisfactory in both dimensions. Conclusions The MTS-AT can offer insightful information about mindfulness-related topics and practical indicators for creating and assessing teacher mindfulness-based interventions. It can also identify teachers needing support, help design targeted mindfulness interventions, and evaluate their effectiveness in improving teaching practices.

Recently, deep learning and image segmentation have helped to enhance agricultural quality evaluation, material research, and disease diagnosis. Biospeckle pictures are challenging to process using traditional image processing techniques because of their complexity and blurriness. This is due to the widely recognized challenges associated with comprehending these photographs. Our technique uses convolutional neural networks to identify and distinguish biospeckle patterns. Via models generated on big datasets such as ImageNet, we can speed up the process of building and training models via transfer learning. Stochastic gradient descent, batch normalization, and dropout regularization improve the models, prevent overfitting, and stabilize the training. We employ several strategies to enhance the model. To enhance the model, data augmentation techniques use elastic deformations, flip, scale, and rotate the data. These strategies work together to increase the dataset's variety. The recommended technique yielded dice = 0.95, IoU = 0.94, accuracy = 0.96, precision = 0.95, recall = 0.92, and F1 = 0.93. These performance measures show tremendous improvement. The computational complexity of our method (152 billion floating-point operations, 3.5 GB of CPU memory, 20 ms for inference, and 31 million parameters) ensures constant performance. This approach improves the accuracy and reliability of biospeckle image.

This study aims to understand the influence of the critical drivers of the digital economy on Jordan’s economic growth using a cointegration model between the periods from 2005 to 2022. The essential drivers include the number of fixed-line users, mobile phone subscribers, Internet users, fixed-line purchasers, and mobile subscribers. The model was estimated using the Vector Error Correction Model (VECM), revealing a unique and consistent integrating relationship between variables. This indicates that the digital economy helps Jordan grow faster and creates job opportunities, which also helps improve the quality of many sectors. The study suggested that the business environment can be improved by enhancing trust and increasing transparency. The government should focus on the digital economy as a sector that works to increase growth rates in Jordan. It must also work to develop infrastructure, which requires more investment in this area, and there is a need to enhance investments in the Jordanian digital economy and enhance the effectiveness of the industry to enhance the country’s Gross Domestic Product.

This research investigates the compressive strength behavior of basalt fiber-reinforced concrete (BFRC) using machine learning models to optimize predictions and enhance its practical applications. The study incorporates various modeling techniques, including Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees, and Random Forest (RF), to evaluate their predictive capabilities. Basalt Fiber Reinforced Concrete (BFRC) is a composite material that incorporates basalt fibers into traditional concrete to enhance its mechanical and durability properties. The use of basalt fibers, derived from natural volcanic rocks, aligns with sustainability goals due to their eco-friendliness, cost-effectiveness, and high performance. BFRC combines structural excellence with sustainability, making it an ideal material for modern construction practices. Its ability to enhance performance, reduce environmental impact, and ensure long-term durability positions it as a pivotal solution for sustainable infrastructure development. The developed models were used to predict compressive strength of basalt fiber concrete (Cs_bf) using the concrete mixture contents, age, and fiber dimensions. All the developed models were created using “Orange Data Mining” software version 3.36. A total of three hundred and nine (309) records were collected from literature for compressive strength for different mixing ratios of basalt fiber concrete with concrete at different ages. Each record contains the following data: C-Cement content (Kg/m³), FA-Fly ash content (Kg/m³), W-Water content (Kg/m³), SP-Super-plasticizer content (Kg/m³), CAg-Coarse aggregates content (Kg/m³), FAg-Fine aggregates content (Kg/m³), Age-The concrete age at testing (days), L_b-length of basalt fibers (mm), d_bf-Diameter of basalt fibers (µm), V_bf-Volume content of basalt fibers (%) and Cs_bf-Compressive strength of basalt fibre concrete (MPa). The collected records were divided into training set (249 records≈80%) and validation set (60 records≈ 20%). At the end of the process, it can be shown that the present research work outclassed other ML techniques applied in the previous research paper, which reported the utilization of the same size of data entries and basalt reinforced concrete constituents. Taylor chart for measured compressive strength of basalt fiber reinforced concrete predicted with ANN, KNN, SVM, Tree and RF is presented for comparing the performance of predictive models by illustrating three key statistical measures simultaneously: the correlation coefficient (R), the normalized standard deviation (σ), and the root-mean-square error (RMSE). Finally, it can be deduced that after considering the performance indices of the selected ensemble and classification models utilized in this present research paper, all the developed modes have almost the same excellent level of accuracy 95%, but ANN, KNN, and SVR produced R2 of 0.98 each with KNN producing MAE of 1.4 MPa, and MSE of 2.5 MPa to outperform ANN and SVR which produced MAE of 1.55 MPa/MSE of 4.1 MPa and MAE of 1.6 MPa/MSE of 3.85 MPa, respectively. Three techniques were used to estimate the impact of each input on the compressive strength, namely correlation matrix, sensitivity analysis and relative importance chart.

In this article, we introduce three inclusive subclasses YΓ(κ, η, σ), WΓ(α, φ) and KΓ(α, φ) of the class of bi-univalent functions utilizing Gregory numbers. For each of these subclasses of analytic functions, we examine the Fekete-Szeg¨o functional as well as the estimations of the Taylor-Maclaurin coefficients, |s2| and |s3|. Such these subclasses may be the subject of future study due to the novelty of their characterizations and the proofs.