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Computational thinking and spatial thinking ability play a critical role in enabling students to respond effectively to disasters. However, limited research has examined the impact of digital literacy, disaster education, and cognitive skills in secondary school students. This study aims to assess the impact of digital literacy and disaster mitigation understanding on the development of computational and spatial thinking ability in upper secondary school students. A quantitative research approach was employed using Structural Equation Modeling to analyze the impact of the variables. The research participants in this study consisted of 258 students enrolled in two upper secondary schools, namely Public Senior High School 21 Makassar and Public Senior High School 4 Barru. Data were collected through questionnaires and performance-based tests. The results revealed that digital literacy positively impacted computational thinking and spatial thinking. Similarly, disaster mitigation understanding positively impacted computational thinking and spatial thinking. Moreover, computational thinking demonstrated a moderate positive impact on spatial thinking, indicating a strong interaction between these cognitive domains. These findings suggest that students with higher digital literacy and disaster knowledge exhibit stronger problem-solving and spatial reasoning skills, which are crucial for disaster preparedness and risk mitigation.

The Internet of Things (IoT) consist of a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Intrusion detection systems using deep learning are a common method used for providing security in IoT. However, traditional deep learning IDS systems do not accurately classify the attack and also require high computation time. Thus, to solve this issue, herein, we propose an advance Intrusion detection framework using Self-Attention Progressive Generative Adversarial Network (SAPGAN) framework for detecting security threats in IoT networks. In our proposed framework, at first, the IoT data are gathered. Then, the data are fed to pre-processing. In pre-processing, it restored the missing value using Local least squares. Then the preprocessing output is fed to feature selection. At feature selection, the optimum features are compiled using a modified War Strategy Optimization Algorithm (WSOA). Based upon the optimum features, the intruders were categorized into two categories named Anomaly and Normal using the proposed framework. Numerous attacks are assembled, including camera-based flood, DDoS, RTSP brute force, etc. We have compared our proposed framework using state of the art model and efficiency of 23.19%, 27.55%, and 18.35% higher accuracy and 14.46%, 26.76%, and 13.65% lower computational time compared to traditional models.

The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity. IAO emulates the hunting behaviors of Aquila, elucidating each step of the hunting process. The IAO algorithm contains innovative elements to boost its optimization capabilities. It combines a combination of low flight with a leisurely descent for exploitation, high-altitude vertical dives, contour flying with brief gliding attacks for exploration, and controlled swooping maneuvers for effective prey capture. To assess the effectiveness of IAO, Herein, numerous experiments were carried out. Firstly, IAO was compared using 23 classical optimization functions. The achieved results demonstrate that the proposed model outperforms various champion algorithms. Secondly, the proposed algorithm is applied to five real-world engineering problems. The achieved results prove effectiveness in diverse application domains. The key findings of the research work highlight IAO’s resilience and adaptability in solving challenging optimization issues and its importance as a strong optimization tool for real-world engineering applications. Convergence curves compare the speed of proposed algorithms with selected algorithms for 1000 iterations. Time complexity analysis shows that the best time is 0.00015225 which is better as compared to other algorithms also Wilcoxon ranksum test is carried out to calculate the p-value is less than 0.05 rejecting the null hypothesis. The research emphasizes the potential of IAO as a tool for tackling real-world optimization challenges by explaining its efficacy and competitiveness compared to other optimization procedures via comprehensive testing and analysis.

Insulin is a life-saving drug for patients with type 1 diabetes; however, even today, no pharmacotherapy can prevent the loss or dysfunction of pancreatic insulin-producing β cells to stop or reverse disease progression. Thus, pancreatic β cells have been a main focus for cell-replacement and regenerative therapies as a curative treatment for diabetes. In this Review, we highlight recent advances toward the development of diabetes therapies that target β cells to enhance proliferation, redifferentiation and protection from cell death and/or enable selective killing of senescent β cells. We describe currently available therapies and their mode of action, as well as insufficiencies of glucagon-like peptide 1 (GLP-1) and insulin therapies. We discuss and summarize data collected over the last decades that support the notion that pharmacological targeting of β cell insulin signalling might protect and/or regenerate β cells as an improved treatment of patients with diabetes. This Review summarizes emerging concepts for diabetes therapy aimed at specifically altering β cell biology and function, such as β cell insulin signalling, proliferation, differentiation, apoptosis, as well as the selective killing of senescent β cells.

A comprehensive understanding of human intelligence is still an ongoing process, i.e., human and information security are not yet perfectly matched. By understanding cognitive processes, designers can design humanized cognitive information systems (CIS). The need for this research is justified because today’s business decision makers are faced with questions they cannot answer in a given amount of time without the use of cognitive information systems. The researchers aim to better strengthen cognitive information systems with more pronounced cognitive thresholds by demonstrating the resilience of cognitive resonant frequencies to reveal possible responses to improve the efficiency of human-computer interaction (HCI). A practice-oriented research approach included research analysis and a review of existing articles to pursue a comparative research model; thereafter, a model development paradigm was used to observe and monitor the progression of CIS during HCI. The scope of our research provides a broader perspective on how different disciplines affect HCI and how human cognitive models can be enhanced to enrich complements. We have identified a significant gap in the current literature on mental processing resulting from a wide range of theory and practice.

The purpose of this article is to investigate the drivers and barriers to broadband adoption/use by food and drink micro-business owner-managers based in remote-rural Scotland. This study adopts a mixed methods approach consisting of 141 questionnaires involving rural restaurants, which is then followed by 42 semi-structured interviews with restaurant owners based in remote-rural Scotland. A Thematic analysis method offered the researchers simplicity as well as rigour to explore and arrange the retrieved data, which, in the opinion of the researchers, was necessary as it also offered flexibility to support the nature of this exploratory research study. The broadband adoption drivers of bookings, customer use, monetary transactions, and security [e.g. CCTV (Closed Circuit Television)], and barriers of lack of ISP selection and location of restaurants were highlighted as findings exclusive to this research study. The research also makes a key contribution by creating and developing the Broadband Adoption Framework (BAF) along with identifying broadband adoption/use drivers and barriers from the context of restaurant businesses based in remote-rural Scotland. The generalisability and representativeness of the sample in terms of different regions in remote-rural Scotland were a challenge due to a disproportionate sample under-representing some areas. Concerning remote-rural Scotland, recommendations are made for (1) food and drink business owner-managers and maximisation of the value added through broadband adoption/use; and (2) Scottish Government policy for ensuring equality of broadband access to ensure that rural-based businesses can effectively incorporate broadband into their operations. The findings from this research may better address the social exclusion and digital divide between urban and rural communities and businesses in Scotland. This study contributes to knowledge by identifying broadband adoption/use drivers and barriers exclusive to food and drink micro-businesses in remote-rural Scotland. However, some rural areas in Scotland were under-represented, and there was no primary insight gained from Scottish/UK government policymakers for broadband infrastructure in rural Scotland. It should be noted that the authors believe that in the case of future related studies, the validity, dependability and thoroughness of the findings can be enhanced by ensuring a more representative sample in terms of rurality and location. Additionally, the authors believe that results for drivers and barriers to broadband adoption/use may vary based on geography, sector, size and level of rurality for included businesses.

Infertility is a medical condition that affects both males and females and can cause the individuals biopsychosocial, spiritual, and medical detriments. Quality of life among such couples or singles is a matter of concern. The question that we need to address is whether infertility affects the quality of life. Does the stigma associated with Infertility deter Infertile females from leading normal lives? This research explores how infertility stigma affects the quality of life of infertile females and whether perceived social support reduces the stress related to stigma thereby contributing to a better quality of life among females battling Infertility in India. Participants from Jammu and Kashmir who identified as currently or previously infertile discussed their feelings about fertility stigma, the quality of their fertility-related social support, and their fertility quality of life. Only 302 fully complete questionnaires were obtained from the 351 identified individuals who were given data collection tools. Structural Equation Modeling (SEM) was used to treat data. It was seen that infertility stigma and perceived social support had an impact on fertility quality of life, either directly or indirectly. Infertility quality of life was reduced by stigma ( = -.413, SE = .017, ≤ .01 level of significance, CI, 95%), and this link was partially mediated by infertile female's perceptions of social support ( = .512, ≤ .01 level of significance, CI, 95%). In other words, it can be said that the negative effects of infertility stigma were buffered by perceived social support and improved fertility quality of life. Additionally, the sense of stigma was adversely linked with the overall quality of past fertility-related support. The findings of study confirms that perceived social support significantly mitigates the negative impact of infertility-related stigma on fertility quality of life among infertile females, highlighting the crucial role of emotional and social resources in mitigating distress. These findings emphasize the importance of encouraging supportive environment and interventions to enhance quality of life in females experiencing infertility stigma.

This study aims to determine the construct validity of the instrument used in the application of Blended Learning . Respondents were randomly selected 60 students from the Department of Geography, Faculty of Mathematics and Natural Sciences, Makassar State University, 60 students from the Department of Biology Education, Faculty of Teacher Training and Education, Patompo University, and 60 students from the Department of Primary Teacher Education, Faculty of Teacher Training and Education, Megarezky University. Construct validity was tested by Confirmatory Factor Analysis (CFA) in Structural Equation Modeling (SEM) through the AMOS 22.0 application. The analysis findings reveal that the indicators employed in developing the Instrument for Blended Learning Model Application encompass the constructs of Orientation, Organization, Investigation, Presentation, Analysis, and Evaluation. These constructs meet the criteria of Construct Reliability, Variance Extracted, and Discriminant Validity. Consequently, the instrument proves suitable for implementation in research examining the application of the Blended Learning Model.

English to Urdu machine translation is still in its beginning and lacks simple translation methods to provide motivating and adequate English to Urdu translation. In order to make knowledge available to the masses, there should be mechanisms and tools in place to make things understandable by translating from source language to target language in an automated fashion. Machine translation has achieved this goal with encouraging results. When decoding the source text into the target language, the translator checks all the characteristics of the text. To achieve machine translation, rule-based, computational, hybrid and neural machine translation approaches have been proposed to automate the work. In this research work, a neural machine translation approach is employed to translate English text into Urdu. Long Short Term Short Model (LSTM) Encoder Decoder is used to translate English to Urdu. The various steps required to perform translation tasks include preprocessing, tokenization, grammar and sentence structure analysis, word embeddings, training data preparation, encoder-decoder models, and output text generation. The results show that the model used in the research work shows better performance in translation. The results were evaluated using bilingual research metrics and showed that the test and training data yielded the highest score sequences with an effective length of ten (10).

Background: Coronary artery disease (CAD) remains one of the leading causes of death globally. Traditional manual scoring methods using non-contrast computed tomography (NCCT) are time-consuming, subjective, and require expertise. To overcome these limitations, this research introduces an AI-driven model to predict and classify more efficiently and accurately. Convolutional Neural Networks (CNNs) are a crucial deep learning tool for detecting cardiovascular diseases (CVDs) from ECG images due to their ability to automatically extract complex patterns and hierarchical features. DenseNet201 is a deep learning model effectively used for cardiovascular disease (CVD) detection from ECG imagery, demonstrating high accuracy in classifying cardiac conditions, particularly for multi-class scenarios. InceptionV3 is a deep learning model widely used for cardiovascular disease (CVD) detection from electrocardiogram (ECG) imagery by leveraging its fine-tuned architecture to classify cardiac conditions. Objectives: To develop a deep learning-based model for automatic classification and prediction of coronary artery calcium scores. To enhance accuracy using an improved BiGRU model incorporating, to reduce the error and bias in current automatic scoring systems and improve clinical decision-making. Design: The study designs a novel architecture named HeProbAtt BiGRU Net. The model performs both classification (healthy vs non-healthy) and regression on NCCT image data. Methods: Data collection, 14 127 NCCT slices—dataset from Tabriz University of Medical Sciences, Preprocessing, Model Development, Performance Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC-AUC, MAE, RMSE. Results: The proposed model outperformed all compared models with: Classification: Accuracy = 99%, F1-score = 99%, ROC-AUC = .99, Regression: MAE = .065, RMSE = .145. The inclusion of attention and probabilistic weights enhanced learning efficiency and decision precision. Visualization tools (eg, loss curves, confusion matrix, ROC) showed stable and high-performing learning behavior. Conclusion: The HeProbAtt BiGRU Net provides a highly accurate, automated, and efficient method for coronary artery calcium scoring. Its hybrid framework allows real-time classification and regression, aiding clinicians in early CAD diagnosis. Future work could include validation on larger, multi-center datasets, and incorporation of clinical explain-ability features.