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The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology and its operational efficiency. In response, this study introduces a novel application of the k-Nearest Neighbor (k-NN) algorithm to assess the reliability and applicability of solar panel simulation data which aimed to classify current states of partial shading, open, and short circuit conditions, alongside regression-based analysis for predicting specific operating parameters. In this research, a published dataset that involved various PV module configurations under different environmental conditions was tested and evaluated. The k-NN technique was applied to both classify the operational status and predict performance metrics of the modules. The diagnosis model demonstrated an accuracy of 99.2 % and an F1 score of 99.2 %, indicating a high degree of reliability in identifying the operational states of PV modules. Concurrently, the regression model exhibited a Root Mean Square Error (RMSE) of 0.036 and an R2 value of unity that showcased its effectiveness in predicting the operational parameters based on the simulation data. The concluded results are further enriching the effectiveness of simulation-based data generation to be endorsed and implemented before jumping into real experimental applications, in addition to highlighting the potential applicability of k-NN and machine learning for PV cells productivity statistical analysis.

In terms of battery design and evaluation, Electric Vehicles (EVs) are receiving a great deal of attention as a modern, eco-friendly, sustainable transportation method. In this paper, a novel battery pack is designed to maintain a uniform temperature distribution, allowing the battery to operate within its optimal temperature range. The proposed battery design is part of a main channel where a portion of cool air will pass from an inlet then exit from an outlet where a uniform temperature distribution is maintained. First, a 3-D model of a battery cell was created, followed by thermal simulation for 15C, 25C, and 35C ambient temperatures. The simulation results reveal that the temperature distribution is nearly uniform, with slightly higher values in the middle portion of the cell height. Second, using finite element analysis (FEA), it was determined that the heat flux per unit area is nearly uniform with a slight increase at the edges. Third, a machine learning model is proposed by utilizing a neural network (NN). Lastly, the heat flux values were predicted using the NN model that was proposed. The model was assessed based on statistical measures where a root mean square error (RMSE) value of 0.87% was achieved. The NN outperformed FEA in terms of time consumption with a high prediction accuracy, leveraging the potential of adopting machine learning over FEA in related operational assessments.

This study deals with the evaluation of traffic performance at the road network around the old city of Karbala from the southern side, at Al-Tarbia Street and Fatima Al-Zahraa Street, through the evaluation of intersections performance. This is followed by suggestions of some improvement proposals, which vary from changing timing plan, geometric improvement, to change intersection type completely. The study area composed of seven intersections, three intersections are four legs signalized, and the other four are roundabouts. The video recording technique is used to collect the traffic data for 27 approaches. These data are abstracted from video films using EVENT program, and processed by prepared EXCEL sheets. While, the spot speed data for each entire link in the network are collected using pavement marking method. SYNCHRO software was used for evaluation and analysis of signalized intersections, suggestion of best timing plan, and coordination. SIDRA INTERSECTION software was used for evaluation and analysis of both signalized intersections and roundabouts. The best proposal was also evaluated for the target year.

Wind tunnels are essential for examining aircraft model aerodynamics, accurately simulating real-world conditions, and enhancing design and performance evaluations. This study introduces a novel technique to improve the time and accuracy of stress distribution forecasts in wind tunnel simulations. This method combines Finite Element Analysis (FEA) with two regression models: Support Vector Machine (SVM) and k-Nearest Neighbors (kNN). The investigation begins with a thorough analysis of ANSYS fluent flow data, which reveals intricate fluid dynamics details within the wind tunnel. A comparative analysis of stress projections, supplemented by Root Mean Square Error (RMSE) metric, demonstrates the proposed methodology’s viability. High accuracy is noted in the SVM-based model, as evidenced by its 2.1% RMSE, which surpasses the kNN model's 5.6% RMSE. Notably, the stress distribution calculation took almost 2 hours in ANSYS.In contrast, it required only 10 seconds in SVM and 3 seconds in kNN, showcasing the time-efficient attributes of these models where they solely depend on the trained data. Moreover, the computational efficacy of the SVM and kNN models is highlighted, emphasizing their flexibility in stress analysis. This integrative approach introduces a promising potential in engineering simulations, yielding precise stress distribution forecasts that have the potential to advance aircraft design methodologies and wind tunnel evaluations.

The incidental detection of pancreatic cysts, an opportunity for the early detection of pancreatic cancer, is increasing, owing to an aging population and improvements in imaging technology. The classification of pancreatic cystic precursors currently relies on imaging and cyst fluid evaluations, including cytology and protein and genomic analyses. However, there are persistent limitations that obstruct the accuracy and quality of information for clinicians, including the limited volume of the complex, often acellular, and proteinaceous milieu that comprises pancreatic cyst fluid. The constraints of currently available clinical assays lead clinicians to the subjective and inconsistent application of diagnostic tools, which can contribute to unnecessary surgery and missed pancreatic cancers. Herein, we describe the pathway toward pancreatic cyst classification and diagnosis, the volume requirements for several clinically available diagnostic tools, and some analytical and diagnostic limitations for each assay. We then discuss current and future work on novel markers and methods, and how to expand the utility of clinical pancreatic cyst fluid samples. Results of ongoing studies applying SERS as a detection mode suggest that 50 μL of pancreatic cyst fluid is more than sufficient to accurately rule out non-mucinous pancreatic cysts with no malignant potential from further evaluation. This process is expected to leave sufficient fluid to analyze a follow-up, rule-in panel of markers currently in development that can stratify grades of dysplasia in mucinous pancreatic cysts and improve clinical decision-making.

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500–2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley. Five advanced statistics were calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, and the signal's Energy. The Taguchi method was used to test the five selected features on the basis of Signal-to-Noise (S/N) ratio. For classifications, an expert system was used based on artificial intelligence where a Random Forest (RF) model was trained on untraditional parameters for optimizing the accuracy. The resulted 0.990 and 0.999, accuracy and AUC, demonstrate the RF model's high dependability. Evidently, the methodology highlights the features potential when progressed into expert systems, which advances predictive maintenance strategies for belt-drive systems.

Abstract Objectives To perform a descriptive analysis of a series of patients with recurrent macroscopic haematuria after a primary standard evaluation including computed tomography urography (CTU) and cystoscopy negative for urinary bladder cancer (UBC) and upper tract urothelial cancer (UTUC) and to identify potential factors associated with occurrence of recurrent macroscopic haematuria. Methods All patients older than 50 years who underwent urological investigation for macroscopic haematuria with both cystoscopy and CTU 2015–2017 were retrospectively reviewed. A descriptive analysis of the primary and later investigations for recurrent macroscopic haematuria was performed. To investigate the association between explanatory variables and the occurrence of recurrent macroscopic haematuria, a Poisson regression analysis was performed. Results A total of 1395 eligible individuals with primary standard investigation negative for UBC and UTUC were included. During a median follow‐up of 6.2 (IQR 5.3–7) years, 248 (18%) patients had recurrent macroscopic haematuria, of whom six patients were diagnosed with UBC, two with prostate cancer, one with renal cell carcinoma and one had a suspected UTUC at the repeated investigation. Within 3 years, 148 patients (11%) experienced recurrent macroscopic haematuria, of whom two patients were diagnosed with low‐grade UBC (TaG1–2), one with T2G3 UBC and one with low‐risk prostate cancer. The presence of an indwelling catheter, use of antithrombotic medication, pathological findings at CTU or cystoscopy or history of pelvic radiotherapy were all statistically significant independent predictors for increased risk for recurrent macroscopic haematuria. Conclusion In the case of recurrent macroscopic haematuria within 3 years of primary standard evaluation for urinary tract cancer, there was a low risk of later urological malignancies in patients initially negative for UBC and UTUC. Therefore, waiting 3 years before conducting another complete investigation in cases of recurrent macroscopic haematuria might be appropriate.

The availability and cost of fossil fuels, natural disasters, aging infrastructure, climate change, and rising electricity consumption have affected today’s power grids. One of the most practical solutions for achieving green and reliable energy is the use of microgrids. The stability of microgrids dominated by electronic converters presents several challenges. Among the problems encountered are the absence of physical inertia, delay in detecting islanding, and loss of stability associated with the transition between operating modes and variations of the load power. To overcome these challenges, this study presents a new robust control strategy based on active disturbance rejection control (ADRC). It is suitable for both islanded and connected operation modes with a single control, without an islanding detection algorithm or Phase-Locked Loop (PLL). The effectiveness of the control strategy is demonstrated through simulations and a comparative analysis with conventional droop control. Flexibility of the transition is also ensured. The proposed control strategy is successfully validated using a TI C2000 DSP TMS320F28335 microcontroller.

This study explores the factors affecting CliQ payment service adoption in Jordan as it represents a major shift in the Jordanian market to Fintech and mobile marketing. This study presented a distinctive model by integrating the “unified theory of acceptance” and the “technology acceptance model” TAM with the “use of technology UTAUT” to measure the behavioral intention of consumers and the satisfaction of the users towards utilizing the CliQ service. The study measured the linkage between four predictors (perceived usefulness, social influence, perceived ease of use, and financial risk) as well as both utilizers’ intention to CliQ service usage and utilizers’ satisfaction with using the service, along with observing the association between these predictors to determine their contribution to the users’ satisfaction mediated by the users’ intention to utilize the service mentioned above. A survey instrument was distributed to 604 respondents, and it was developed and validated by academics, as a pilot test. As well as a pre-test. Hypotheses were tested with multiple linear regression analysis using SmartPLS software to interpret path coefficients, and reliability and validity within the outer model were tested through correlation analysis. Additionally, internal consistency was explored by applying Cronbach’s alpha. The outcomes proved that the predictors significantly affect both utilizers’ behavioral attitudes and satisfaction with using the CliQ service, except for financial risk. The data analysis also indicated a significant indirect influence of all the independent variables on utilizers’ satisfaction through behavioral intention to utilize the CliQ service, except for financial risk, which had an insignificant indirect influence on utilizers’ satisfaction through behavioral intention for service usage. The findings of the current article close the gap found in the literature concerning measuring usability to enhance user satisfaction and adoption of Fintech services. Therefore, bank managers and policy planners can find insights for developing and improving mobile banking apps.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are promises through which AI-driven marketing will meet business goals and be huge at societies' values.

The current research aims to expose the value of Instagram's features and content and investigate how cognitive functions mediate the relationship between Instagram's content-related elements (informative material, user-generated content, augmented reality content, entertainment, trustworthiness, sociability) and consumer affection and behavior. This study employed a random sample strategy and gathered 292 responses. The tool AMOS 22 (Analysis of a Moment Structure) examined the data efficiently. Results show that all Instagram content marketing elements affect cognitive engagement, where augmented reality content has rated the highest effect, then user-generated content, trustworthiness, informative material, entertainment, and sociability, consequently. Then cognitive engagement affects affection and behavior.

This study investigates the effect of marketing via Instagram on Generation Z’s preference for gyms in Jordan, so it would be helpful for marketers of gyms to be aware of using Instagram to attract Generation Z as their potential customers. Moreover, a sample questionnaire was carried out with 138 respondents, which were mostly female respondents (74%) and male respondents (26%). Additionally, 51% of the total number of respondents were 21-24 years old. Therefore, the data was analyzed by applying various statistical techniques such as Cronbach’s alpha for testing the reliability of the data, and multiple regression using SPSS version 22 for examining the hypotheses. Likewise, the results showed that Instagram and these variables (entertainment, interaction, trendiness, and customization) have an effect on customers' brand choice, but trendiness did not have a significant effect on the consumer's brand choice, but after testing the effect of the brand image, it became clear that it has significant effect, and therefore it was concluded that brand image has an important effect on customer’s brand choice.

Social media influencers have become important motivators in shaping tourist attitudes and behaviors. This study analyzed how exposure to influencer content impacts key outcomes for the destination Jordan. A survey of tourists who visited Jordan in the past 3 years measured their perceptions of influencer credibility, content quality, awareness/interest, trust/engagement, destination image, general tourism behavior, and intentions to revisit. Results of SEM analysis found significant positive effects of influencer marketing on both destination image and visit intentions. Awareness/interest and trust/engagement were most impactful, highlighting influencers' role in sparking early motivation. Content quality additionally predicted visit intentions by informing decisions. Perceived credibility made recommendations more persuasive. Furthermore, usage intensity positively moderated the mediated relationships, amplifying effects among heavy social media users. Findings provide theoretical validation of how influencers act as digital opinion leaders. By enhancing destination image through compelling portrayals, influencers shape audience travel interests and behaviors. Managerial implications suggest destinations should invest in influencer campaigns for reach and inspiration while ensuring content quality. Performance tracking informs optimal platform and demographic targeting. Overall, influencer marketing demonstrated significant persuasive appeal for potential tourists. This quantitative study pioneer’s measurement of influencer marketing's tangible impacts on key tourist metrics. The results empirically substantiate the ability of strategically leverage influencers to motivate visitation and guide decision-making. As practitioners refine partnerships for audience growth and branding, academic research must also advance a nuanced understanding of this emerging phenomenon at the confluence of social media and tourism consumer behavior.

This paper focuses on enhancing the resilience of Power Park Modules (PPMs), connected to the grid via power electronic units, under asymmetrical voltage sag conditions. We particularly address three interconnected constraints crucial for Low-Voltage Ride-Through (LVRT) capability of PPMs: grid voltage support, overcurrent protection, and the DC-link voltage quality. Existing literature often overlooks the holistic consideration of these factors, prompting this study to introduce and compare three flexible LVRT strategies compliant with recent German grid codes. The proposed strategies use distinct approaches to reinforce the LVRT capability of the PPMs. Strategy A employs static gain factors to control positive- and negative-sequence reactive current (PSRC and NSRC) and the negative sequence of the active current (NSAC). Strategy B aims to enhance the DC-link voltage quality by eliminating real power oscillations through the flexible choice of NSAC and gain factors controlling PSRC and NSRC. Strategy C enhances DC-bus voltage quality by eliminating NSAC and employing flexible equal gain factors for PSRC and NSRC, mitigating both real and imaginary power oscillations. Testing on an inverter-based PPM, involving 400 kW PV module and 200 A, storage system, reveals the superior performance of strategy C. Additional tests evaluate Strategy C’s performance with var/voltage regulation equipment (On-Load-Tap-Changing transformer), demonstrating stable and promising performance. Through the introduced approaches, the authors believe that this research work is useful for developing future grid codes and improving the safety and reliability of grid-connected power systems.

In the field of electrical machines maintenance, accurate and timely diagnosis plays a crucial role in ensuring reliability and efficiency. Variational autoencoder (VAE) techniques have emerged as a promising tool for fault classification due to their robustness in handling complex data. However, the inherent nondeterministic aspect of the VAE creates a significant challenge as it leads to varying cluster locations for identical health states across different machines. This variability complicates the creation of a standardized applicable diagnostic tool and challenges for the implementation of effective real-time health monitoring and prognostics. Addressing this issue, a novel approach is proposed wherein a desirability function-based term is integrated into the cost function of the VAE. The enhancement achieved by this approach arises from the standardization of classification, guaranteeing that analogous faults are assigned to identical geolocations within a 2-D user-friendly space. This method's efficacy is validated through two separate case studies: one analyzing vibration data from two diverse designs of large existing hydrogenerators, and the other utilizing vibration data sourced from an open-access dataset focused on bearing fault. The findings of both studies show that the model can cluster 97% of similar faults into preset zones, compared with 40% when the desirability term is excluded.

Purpose: This systematic review is aimed to evaluate in fixed prosthodontics treated teeth, the effect of the BOPT on periodontal health compared to the horizontal preparation technique using a chamfer finishing line or to the baseline. Methods: The electronic databases Cochrane Central Library, PubMed, Scopus, and ScienceDirect were searched based on specific MeSH keywords. The Randomized controlled clinical trials (RCTs) and prospective clinical trials on BOPT printed in English up to July 2022 were selected. Screening, selection, and data extraction were done. The studies were assessed for risk of bias, and descriptive and meta-analyses were performed. Results: Eight studies were included in the systematic review and only three RCTs were involved in the meta-analysis. Two RCTs were assessed as low risk while one has some concerns. were grouped as good, fair, and poor based on NOS. Three of the prospective studies were considered to be of good quality and one study was fair. The meta-analysis revealed no statistically significant difference in the effect of BOPT and chamfer preparation on periodontal health status and success rate (P > 0.05). Conclusions: Considering the limitation of this review, the BOPT is comparable to chamfer preparation and it resulted in an acceptable effect to maintain periodontal health with a high success rate. However, more evidence is required to support these findings.

The current research paper aims to pinpoint the determinants that impact consumers' perceived ease and perceived usefulness of digital wallet usage in Jordan. To accomplish this, the Technology Acceptance Model (TAM) has been used, which encompasses additional dimensions like the perceived risk of COVID-19, social influence, government support, promotional benefits, perceived value, and personal innovativeness. A quantitative research approach was employed in this paper; an online survey was applied to gather data from a total of 401 participants. The gathered data underwent analysis using a two-step PLS-SEM method. The study outcomes show that digital wallet users' perception of promotional benefits and perceived value significantly and positively affect the digital wallet's perceived usefulness. However, governmental support and social influence do not have a significant impact on digital wallets' perceived usefulness.

Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain’s electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters—namely Activity, Mobility, and Complexity—is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics.

Purpose: This study examined the conceptions of assessment and its relationship to self-regulation and self-confidence among pre-service teachers. Design/methodology/approach: Participants were 278 pre-service students attending a teacher's college in Bahrain. This study employed the structural equation modeling (SEM) approach to investigate the relationships among the observed variables. Findings: The results revealed that the conceptions of assessment had positively impacted the academic achievements of students who were accountable. In addition, the conceptions that made schools accountable had a positive relationship with academic achievement, whereas students who ignored the assessment, received a negative effect on self-regulation. Research limitations/implications: The generalization of the results to other populations in Bahrain should be made with caution since this study involved a relatively small number of students from Bahrain. Policymakers might be encouraged to consider students' appropriate response to their conceptions of assessment when assessments are being developed. Originality/value: This study might encourage educators and policymakers in Bahrain to develop strategies to further improve students' self-confidence, self-regulation, and academic achievement.