This mixed-methods research aimed to identify essential digital-age skills for logistics leaders, develop an executive potential model, and create a practical development manual. Data were collected via a three-round Delphi method and focus groups, then analyzed using content analysis, median, and inter-quartile range. The finalized model comprises four main components: knowledge, skills, attributes, and management, encompassing 21 sub-parts and 94 competency items. Notably, sustainable logistics and green supply chain management were integrated to address international ESG requirements. The resulting development manual is structured into five sections: Introduction, Terminology, Executive Potential Model, Practical Guidelines, and Evaluation. Five experts unanimously validated the manual (100% agreement), confirming its appropriateness and applicability for developing logistics business executives in Thailand.
In this study, a hybrid deep learning technique is proposed as the foundation for a real-time network intrusion detection system (i.e., NIDS) for Internet of Things (i.e., IoT) networks. (i.e., NIDS) stands for network intrusion detection system. Learning spatial information is accomplished using convolutional neural networks (i.e., CNNs), while modeling temporal information in network traffic patterns with long short-term memory (i.e., LSTMs) is also used. It is possible to create the architecture in such a way that it is both lightweight and efficient, taking into consideration the limitations of Internet of Things devices, such as the restricted processing power and communication protocols that they must support. Using the NSL-KDD benchmark dataset, the proposed model achieves a detection accuracy of 98.7%, while using the UNSW-NB15 benchmark dataset, it achieves 97.3%, and using the CIC-IDS-2017 benchmark dataset, it achieves 98.1%. Furthermore, the inference time for each packet is less than 2.5 milliseconds. It has been demonstrated through the findings that the CNN-LSTM hybrid model possesses superior detection performance in comparison to conventional machine learning classifiers and single-architecture deep learning models when it comes to identifying both known and novel attacks for various Internet of Things network topologies.
This study aims to analyze the role of Perceived Credibility (PC) in increasing the influence of Effort Expectancy (EE) and Price Value (PV) on fintech digital payment transactions in MSMEs. The data processed were from 110 respondents, and SMART PLS 4.0 was applied as an analysis tool. The results showed that effort expectancy (EE) had a positive and significant influence on the adoption of fintech digital payment systems for transactions, as observed through the path coefficient of 0.230 with a t-statistic of 2.436 and a p-value of 0.007. Meanwhile, the price value variable did not significantly affect the adoption of fintech digital payment systems, as shown by the path coefficient value of 0.088, t-statistic of 0.846, and p-value of 0.199. The SEM-PLS data analysis further showed that the perceived credibility (PC) could strengthen the influence of price value on the adoption of fintech digital payment systems for transactions, with a significance level of 0.043. Meanwhile, the p-value of 0.324 showed that the perceived credibility does not moderate the effect of effort expectation on the adoption of fintech digital payment systems for transactions in MSMEs.
This study examines how artificial intelligence (AI) tools are incorporated into university leadership practice and whether the intensity of AI use is associated with leaders' perceived effectiveness. A convergent mixed-methods design was integrated into a quantitative survey of 69 administrative leaders, faculty in leadership roles, and professional staff at the University of Sharjah, with 60 qualitative narratives. Descriptives and ordinary least squares regression assessed associations, and inductive thematic analysis elaborated mechanisms and boundary conditions. Respondents reported moderate familiarity (M=3.30, SD=1.26) and moderate use of AI in leadership tasks (M=3.49, SD=1.41); 57.9% used AI at least weekly. Agreement that AI improves decision making was high (M=3.70, SD=1.09). The dominant barrier was lack of training (60.8%), followed by concerns about recommendation reliability (39.1%), data privacy (33.3%), and ethics (30.4%). AI use was positively associated with perceived leadership effectiveness (R=.492, R²=.242, p<.001). Some narratives emphasized time savings and increased clarity of communication, while others described ethical expectations regarding privacy, bias, accessibility, and academic integrity. Realization of value relies on (capability; role-specific AI literacy), (governance; privacy, assurance, bias, integrity), and (technology; reliable, low-friction tools). Our findings offer unique institution-level evidence from the Gulf context around the use of AI in relation to perceived leadership effectiveness and distil a pragmatic agenda which higher education leaders may find useful.
This review examines the literature for recent studies on STREAM (Science, Technology, Reading/Writing, Engineering, Art, Math) education in higher education, with an emphasis on the period between 2020 and 2025. It analyzes publications that meet these selection criteria and considers the approaches, years of publication, scope, methodologies, theoretical frameworks, main findings, implications of work, focus, subject area and the profile of teachers and learners. Our findings reveal that the integration of literacy in STREAM extends STEM and STEAM by encompassing cognitive higher-order skills, affective engagement, and values-based frameworks. Teacher preparation is represented in the literature as a strategic approach for effective future interdisciplinary education. Finally, limitations and prospects are discussed as notable perspectives for curricula transformations.
Diabetes that only occurs during pregnancy when blood sugar levels become too high is known as gestational diabetes. In low-contrast and noisy computed tomography (CT) scans, traditional networks frequently fail to accurately recognize tumor boundaries, resulting in inaccurate localization and classification. To overcome these limitations, a novel DiaBeatNet-TCL (Diabetes heartbeat signal using Time domain and CNN-LSTM) has been proposed for identifying diabetes in pregnant women in its early stages using ECG signals. The ECG signals are taken as the input, then the signals are pre-processed using the low-pass and high-pass filters to remove noise in the signal. The feature extraction is done in the Time domain, which is used to find the heart rate of pregnant women. A DL technique that uses CNN and LSTM combined to improve prediction accuracy. The CNN is used to extract the important patterns like QRS and T-wave, while LSTM is used as a memory to store the heartbeat intervals. This combined method efficiently classify the diabetics of the pregnancy women with diabetes. From the experimental results, the DiaBeatNet-TCL approach achieves an accuracy of 99.61% and an F1 Score of 93.23% for diabetes detection. The DiaBeatNet-TCL approach increases the overall accuracy of 9.27%, 6.72%, 4.52%, and 2.05% over LeNet, AlexNet, DenseNet and ResNet, respectively. The DiaBeatNet-TCL approach enhances the total accuracy of 5.63%, 0.37%, 4.85%, 5.96% better than SVM-CNN+LSTM, Attention R2W-Net, ECG-HbA1c, and ProWSy, respectively.
The study's objective is to determine the effect of the Accounting Information System (AIS) adoption on the financial sustainability of logistics companies in Jordan and to explore the mediation role of Top Management Support (TMS). The study uses the Technology Acceptance Model, Diffusion of Innovation and Contingency Theory to assess the influence of perceived usefulness, perceived ease of use, organisational readiness, system compatibility, and system complexity. The method used was quantitative research with data collection from 412 accountants in the logistics companies in Jordan. Structural Equation Modelling (SEM) software SmartPLS 4 was used to analyse the data. The results indicate that the perceived usefulness, perceived ease of use, organisational readiness, system compatibility, AIS adoption, and financial sustainability have positive direct effects on financial sustainability, while system complexity does not have a significant direct impact. TMS has a significant mediating effect on some of the relationships between AIS-related factors and financial sustainability. The research emphasizes the need for the management's support and technological preparedness in increasing financial sustainability of logistics companies in the process of adopting AIS.
Technological progress has resulted in a growing boom of applications that have left aside the art of folding, cutting, molding, or gluing physical materials that allow the development of motor skills and strengthen imagination, creativity, and recursion. This research proposes a structured methodology of three phases: pre-production, production, and post-production, which, through 3D modeling techniques, the use of various software such as Autodesk Maya and Adobe Substance Painter, and the support of other technological tools, allows the creation of paper toys of characters inspired by the narrative cultural manifestations of Riobamba, Ecuador. The results were evaluated through the analysis of 15 variables to assess the 3D modeling for its subsequent construction on paper. The proposed methodology, combined with the analysis of coding, proved to be effective in the creation of precise templates for any character, architecture, or object, thus facilitating the design and manufacture of paper toys for their application as educational material to promote the cultural manifestations of the region.
This particular research addresses and highlights the intersection and importance of SBEF and financial literacy, as well as the SML performance gap, which is a particularly lacking segment of the economy that underperforms as a result of financial literacy and capital deficiency, SMLs – and SBEF in particular – being a crucial yet economically sidelined part of the world. This gap in the literature, and the aim of this study, is to explore the relationship between SBEF and financial literacy performance, and the moderating effects of advocacy and accounting information. This research is predominantly qualitative in nature, with a Primary Sampling Frame drawn from a pool of 150 managers of the targeted SMEs in Indonesia. The Moderated Regression Model was used to explore financial literacy, SBEF, and SML performance; MMA, SBEF, and advocacy-accounting SME performance moderation; and confirmatory factor financial literacy relationships. The study found that SBEF financial literacy and SBEF and SME growth financial performance increase when SBEF and SME growth increase. This study suggests advanced advocacy and accounting information in addition to the developed financial literacy and entrepreneurial finance. This study identifies the impact that multiple parties, including institutional leaders, investors, and the state, can have on the area's continued development for the purposeful, sustained growth of SMEs. It also merges finance with entrepreneurship, pinpointing the important entrepreneurial approach needed to finance SMEs. It thus addresses the practical and theoretical gaps in entrepreneurial finance, as well as the role of financial literacy in SME performance.
The health sector is one of the most important sectors; because it affects people’s lives. The thyroid gland is located at the front of the neck. It is one of the most important glands; that is because it is responsibility on the metabolism. Thyroid Gland releases hormones that affect the human lifestyle, activity, and health. The thyroid diseases are hyperthyroidism, hypothyroidism, and thyroid cancer. The early detection of these diseases gives the patient better opportunity to get faster recovery. Machine learning (ML)--based models are used widely these days to predict several types of disease. However, the performance from one ML to another differs throughout different applications. This study performs several prediction models based on the most common ML algorithms including support vector machine (SVM), naïve Bayes (NB), artificial neural network (ANN), NB network, and Logistic classifiers. The models’ performances were assessed in predicting Thyroid disease using several evaluation metrics including accuracy, precision, recall, F-measure, ROC, and PRC. The results show that different classifiers could provide remarkable performance in detecting Thyroid disease, and the maximum accuracy is obtained at 97.664% by using the Logistic classifier.
With the development of technology, the development of smart campuses has become a key direction for the development of information technology in higher educational institutions. To achieve smart campus development, it is essential to identify the key factors influencing the acceptance of smart campus initiatives among students. In the study, the research model with social influence and facilitating conditions as the independent variables, behavioral intention as the mediating variable, and adoption of smart campus as the dependent variable was constructed, and the research model was analyzed empirically by using structural equation modeling with the students of nine higher educational institutions in the area of Nantong City, Jiangsu Province. The results of the study show that (1) There was a strong positive effect of social influence and facilitating conditions on behavioral intention, and the influence factors are respectively 0.449 and 0.265. (2) Social influence and facilitating conditions not only directly affect students' adoption behavior but also play an indirect role through behavioral intention, and the proportions of the indirect effect are 57.9% and 23.5%, respectively.
The Industrial Internet of Things (IIoT) revolutionizes industrial operations by integrating smart sensors, advanced analytics, and machine learning to optimize manufacturing and supply chain processes. However, the rapid growth of IIoT presents tremendous cybersecurity challenges, as the increasing number of connected devices makes these systems more susceptible to cyberattacks. Intrusion detection systems (IDS) are vital for monitoring network and system activity to prevent and identify malicious attacks in IIoT. This paper discusses the role of AI- and computational-intelligence-driven IDS in protecting IIoT networks, addressing crucial challenges such as device heterogeneity, real-time operational limitations, and the mission-critical nature of industrial systems. Traditional IDS solutions are challenged by high false positives and dynamic cyber threats, and AI-based methods are a necessary evolution. Machine learning (ML) and artificial intelligence (AI) improve the effectiveness of IDS by enhancing anomaly detection, minimizing false alarms, and learning new attack patterns in dynamic IIoT environments. The paper discusses current IDS technologies, emphasizing their strengths and weaknesses in IIoT security. AI-based IDS utilizes deep learning, hybrid models, and feature engineering to improve accuracy and efficiency. Furthermore, future directions in industrial cybersecurity include federated learning for decentralized security, lightweight AI models for resource-scarce IIoT devices, and blockchain for secure data exchange. AI-based IDS can protect critical infrastructure and provide reliable, safe, and resilient industrial processes in IIoT by addressing these issues. The present study highlights the growing role of AI and computational intelligence in making industrial cybersecurity resilient against evolving threats.
This article examines nomophobia, defined as the fear of being without a mobile phone, and its growing prevalence among university students. The literature shows a strong association between nomophobia and psychological factors, particularly anxiety, stress, overthinking, fear of missing out (FoMO), and compulsive non-clinical behaviors, with anxiety emerging as the most influential factor in smartphone overdependence. While several studies indicate higher prevalence among women and younger individuals, evidence regarding gender differences remains inconclusive. Nomophobia also presents significant psychosocial consequences, including reduced face-to-face interaction, increased social anxiety, and the potential exacerbation of mental health conditions such as depression and anxiety disorders. The study employed a cross-sectional design using a survey administered to 500 university students to assess anxiety levels, emotional attachment to smartphones, and daily usage time. Advanced analytical methods, including logistic regression and neural networks, were applied, both achieving a high predictive accuracy of 98% in identifying nomophobia among students.
This study investigates the impact of adopting Accounting Information Systems (AIS) on the effectiveness of financial processes in Jordan, with a particular focus on the mediating role of cloud computing use. Data was collected from 425 accountants across various sectors in Jordan. Structural equation modelling using SmartPLS 4 was used to evaluate both the measurement and structural models. The findings reveal that both PU and PEOU significantly influence cloud computing use, which in turn has a substantial positive effect on the effectiveness of financial processes. PU and PEOU also directly affect financial process effectiveness, although to a lesser extent. Furthermore, cloud computing use was found to partially mediate the relationships between PU and PEOU with financial process effectiveness. This study contributes to the literature by empirically validating a technology-driven model that integrates cloud computing use to explain improvements in financial performance in a developing country context.
Using a sample of unlisted companies from the 2007–2020 stock market, this study validates the association between tax reduction, rate reduction, digitization, model modification, and total element productivity. The study found that (1) Tax cut and rate reduction policy greatly increases the company's total element productivity. (2) Conversion of digitization encourages high-quality development through the resource optimization effect and technological energy supply. (3) Digitization conversion had the strengthening effect on the tax cut and rate reduction policy, and the total element productivity increased by 7.75% by the synergistic effect of both. Heterogeneity analysis shows that synergistic effects of non national and high-tech industries and low competitive markets are more pronounced. According to the verification of the mechanism, the tax reduction and the rate reduction are promoting quality development through the relaxation of the loan constraint, the promotion of the innovative investment, and the expansion of the scale economy.
This paper explores the process of automation of the classification of open-ended questions regarding the economic activities of enterprises, in official statistics. Neural networks (NN) and transformer-based models such as BERT are compared. This study's aim is the improvement of the accuracy and efficiency of the economic activities classification in alignment with the NACE classification system. The textual data from official statistics are processed beforehand. NN and BERT models are utilized to classify at the 2-digit and 4-digit levels. To assess and compare the effectiveness of these models, performance metrics, methods such as accuracy and F1-scores, are used. The results show the potential of transformer-based models to improve the process of automation codification of economic activity, by the reduction of manual work and increasing consistency in classifications. This research makes an essential contribution by exploiting the potential of the application of transformer models to domain-specific data such as the ones in official statistics, advancing the field of automatic text classification.
This research aims to compare the efficiency in detecting changes of process mean between the Exponentially Weighted Moving Average (EWMA), Modified EWMA (MEWMA), Extended EWMA (EEWMA), and New EEWMA (NEEWMA) under observation values from processes with normal and Laplace distributions. The Monte Carlo simulation is used to carry on the numerical results by setting the in control average run length (ARL0) = 370, 500, and 1,000; the simulation is repeated 10,000 times, and sample size (n) = 10 by considering the minimum of out of control average run length (ARL1) and expected ARL (EARL) as the decision criterion. The numerical results found that the NEEWMA chart outperforms in detecting the minor to small changes. In addition, the EARL is an alternative effective to the ARL1 to benchmark the chart, and the NEEWMA chart is superior to other charts for all case studies. This study presents application to real data.
Machine learning has been used for decades to analyze vast datasets, classify and cluster data, and make predictions using algorithms. One of its top use areas is cybersecurity, where it can help detect and prevent destructive threats such as malware. The use of machine learning in cybersecurity has proven to be a powerful tool in detecting and predicting malware attacks. In recent years, the number of Internet users has greatly increased and with it the number of malware attacks. This has made predicting malware a challenge. Consequently, to date, there is still a need to examine the numerous existing MLs’ performance. This study is presented to identify the best classification model for predicting malware using two datasets and 18 different classifiers belonging to six learning strategies. The results showed that the RandomForest classifier had the highest accuracy, precision, recall, F1-measure, and ROC Area metrics, Moreover, Trees and Bayes learning strategies showed the best predictive performance on the two datasets compared with the other five learning strategies.
This article is devoted to the development of new mathematical models based on independent parts of speech in the process of translating from Uzbek into English. The study analyzes the construction of words belonging to Uzbek independent parts of speech through affixes and examines new mathematical models built on an expandable input language, implemented through software tools. The research provides a detailed analysis of the agglutinative morphology of the Uzbek language, word formation processes, and affixation system, and compares them with the analytic features of English. The proposed model integrates morphological, syntactic, and semantic stages and introduces new mathematical approaches for processing both simple and complex words. The Machine translation system developed on the basis of these models effectively addresses grammatical differences between the two languages and ensures high-quality and semantically accurate results. In addition, the paper presents the software code and visual interface of the system, developed using 67 mathematical models, as well as the architecture, components, and implementation stages of the automatic translation system. This approach not only enables an in-depth analysis of the complex morphological structure of the Uzbek language but also ensures precise and systematic outputs in translation into English. The results of the study demonstrate practical applicability in the field of scientific translation and hold value for young researchers, higher education institutions, and international organizations.
Supply chain finance, as an emerging financial service, plays a crucial role in addressing the financing challenges and high costs faced by small and medium-sized enterprises (SMEs). However, in recent years, traditional supply chain finance has encountered various limitations during its practical application. With the continuous advancement and growing maturity of blockchain technology, leveraging blockchain to enhance traditional supply chain finance has emerged as a significant trend. This study focuses on analyzing 890 research papers published between 2016 and 2024, sourced from reputable databases such as ScienceDirect, Scopus, Web of Science, Springer, and Emerald. By employing econometric methods, the research explores key topics and emerging trends related to blockchain technology in the domain of supply chain finance. Through a comprehensive literature review and an evaluation of industry applications, the study assesses how blockchain technology can effectively address the challenges of supply chain finance and highlights future research directions in this field. At present, research on blockchain-enabled supply chain finance primarily revolves around three critical areas: the development models and influence mechanisms of supply chain finance, the application scenarios for blockchain in supply chain finance, and the construction and optimization of blockchain-based supply chain finance platforms. This analysis provides valuable insights into the potential of blockchain technology to revolutionize the landscape of supply chain finance.
