SACM - Australia
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9648
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Item Restricted Hail University employee perceptions about the relationship between Total Quality Management and their performance(Saudi Digital Library, 2026) Almusaieed, Mashael; Nguyen, Thi Phuong Lan; Rachel, Burke; John, Mitchell O’TooleTotal Quality Management (TQM) is being widely implemented in Saudi universities, but evidence regarding employee perceptions of its impact on their performance remains limited, particularly in institutions that have undergone organizational transformations following the implementation of quality standards. This study aims to bridge this gap by examining perceptions of the relationship between TQM practices and employee performance at the University of Hail. The study rests on perceptions of academic and administrative staff with experience at the university both before and after TQM was implemented, explored through a mixed-methods approach that included a quantitative phase using a questionnaire that 145 staff completed, and a qualitative phase comprising analysis of 17 semi-structured interviews. Both academics and administrators reported that the implementation of TQM resulted in a gradual transformation that contributed to improving procedures, clarifying tasks, fostering a performance culture, and increasing efficiency through digital transformation. Perceptions of academics and administrators diverged regarding the impact of the management model on performance. However, both groups agreed on the central role of their immediate superior and that the five TQM practices of leadership, empowerment, training, teamwork, and communication positively affected performance. Teamwork was seen as the most influential practice, followed by training and leadership, but empowerment, and communication were viewed as less effective. The study identified both institutional and individual obstacles to improvement and participants noted that these barriers limit the sustainability of implementation and may reduce the overall success of TQM practices and requirements. The results of the study suggest that effective implementation of Total Quality Management requires integration of its practices into the workplace, strengthening employee experience directly, aligning administrative policies with actual leadership behaviour, and supporting digital transformation to sustain improvement and enhance individual and organizational performance. The findings also highlight the importance of placing the right person in the right position by identifying those personal qualities that support cooperation, development, and openness to change rather than resistance to it. Investing in people is just as important as investing in systems, and it plays a key role in achieving continuous improvement.4 0Item Restricted Enhancing a Telecommunications GIS App for Capacity Planning and Network Performance Visualisation(Saudi Digital Library, 2026) Alqarni, Faisal H; Gregory, MarkTelecommunications networks require effective tools for visualising and managing spatial network information. Geographic Information Systems (GIS) can support this by linking network elements with location data and technical attributes. However, an existing telecommunications GIS application required further enhancement to improve how telecom-specific parameters are displayed, interpreted, exported, and aligned with database-related workflows. This research project enhanced an existing telecommunications GIS application for capacity planning and network performance visualisation. The work continued from Part 1 of this research project, where Tower ID, Channel ID, Cell ID, and RSCP were introduced. In the final stage, these parameters were refined through improvements to the selected node panel, coordinate display, signal quality interpretation, CSV export/import workflow, and database alignment. Additional fields, including Operator ID and Band, were also added to improve network record classification. A design-oriented development methodology was used. The existing system was reviewed, enhancement requirements were identified, and targeted improvements were implemented in the interface, data model, signal quality logic, CSV workflow, and database-related structure. Functional testing and usability observations were then used to evaluate the enhanced application. The results show that the enhanced system provides a clearer side panel for selected node information, including geographic coordinates, telecommunications identifiers, operator information, band information, and RSCP-based signal quality status. The CSV export workflow preserved the enhanced fields, supporting better traceability between the interface and exported records. Database alignment was confirmed for the core MariaDB mps_data fields, including Tower ID, Cell ID, Channel ID, and RSCP, while Operator ID and Band remain future database extensions. Overall, the project demonstrates that practical GIS application enhancement can improve the usability and analytical value of telecommunications planning tools. The enhanced system provides a stronger foundation for future capacity planning, network inspection, reporting, and performance visualisation.8 0Item Restricted Thermal–Mobility Nexus in Riyadh: Integrating TOD and Climate Adaptation for Hot-Climate Urban Futures(Saudi Digital Library, 2026) Alhidarey, Hattan; Iris, LevinExtreme heat and car-oriented urban development have created a reinforcing mobility challenge in Riyadh, where uncomfortable pedestrian conditions discourage walking and public transport use and strengthen dependence on private vehicles. This study examines how urban design and mobility strategies can be integrated to reduce car dependency and improve resilience to extreme heat. It develops the Thermal–Mobility Nexus, a framework that positions thermal comfort as a fundamental condition for accessibility rather than an optional urban design feature. The research adopts a qualitative comparative case-study approach combining documentary analysis, spatial assessment and regional benchmarking. Three major Riyadh policy instruments were analysed alongside an audit of the 800-metre catchment surrounding King Salman Park Metro Station, supported by satellite imagery and 47 field photographs. Dubai and Doha were examined as comparative hot-climate cases. The analysis applied indicators related to shade coverage, corridor continuity, Mean Radiant Temperature and the Universal Thermal Climate Index. The findings reveal a substantial disconnect between Riyadh’s transport investment and the thermal conditions of first- and last-mile pedestrian routes. Shade provision is fragmented, pedestrian corridors remain highly exposed, and mobility and greening policies are implemented through separate institutional structures. The study therefore recommends a binding Thermal Performance Code for station catchments, a cross-agency Thermal–Mobility Coordination Unit, and a corridor activation programme prioritising shaded, mixed-use routes for transit-dependent and heat-vulnerable populations. The proposed framework demonstrates how integrated governance, climate-responsive urban design and measurable thermal standards can improve public transport accessibility and support more equitable, walkable and climate-resilient urban development in Riyadh and other hot-climate cities. Keywords: Thermal–Mobility Nexus; Transit-Oriented Development; extreme heat; thermal comfort; car dependency; Riyadh; climate adaptation; walkability.5 0Item Restricted An Optimised Hybrid Framework for Predicting Learner Dropout in Massive Open Online Courses Using Machine Learning and Behavioural Analytics(Saudi Digital Library, 2026) Alghamdi, Saad Hamdan A; Ben, SohThis research addresses the persistent challenge of high dropout rates in Massive Open Online Courses (MOOCs) by developing and evaluating an enhanced predictive model capable of identifying learners at risk of disengagement. The study introduces the Integrated Stacked Ensemble Learning Dropout Prediction (ISELDP) model, which combines machine learning and meta-heuristic optimisation to improve predictive performance and interpretability. Employing an explanatory, sequential quantitative methodology, the research was conducted in two phases: (1) developing and testing the proposed model using a secondary in-session dataset, and (2) validating the model with primary behavioural and demographic data collected from 466 MOOC participants through a structured survey. The proposed ISELDP integrates multiple base learners (random forest, AdaBoost, gradient boosting, and XGBoost) with a multilayer perceptron (MLP) meta-learner, supported by an optimised feature selection framework based on a hybrid genetic algorithm–correlation feature selection (GA–CFS) technique. The evaluation across balanced and unbalanced datasets demonstrates that the enhanced ISELDP significantly outperforms benchmark models, achieving an average accuracy of 91% and an F1-score of 88%. The hybrid optimisation process effectively reduces feature redundancy, mitigates overfitting, and enhances the model’s generalisability across heterogeneous data. Complementary statistical analyses of the survey data identified motivation and engagement as the most influential predictors of MOOC retention, mediated by self-management and social influence factors, and indirectly affected by course design. These findings emphasise the centrality of motivational and behavioural dimensions in sustaining learner persistence, offering a broader understanding of dropout triggers beyond in-session activity metrics. The research contributes theoretically by defining the specifications of effective dropout prediction models that integrate deep learning and meta-heuristic optimisation. Methodologically, it advances predictive learning analytics through a validated hybrid framework that balances accuracy, interpretability, and adaptability in dynamic learning environments. Practically, it provides a data-driven foundation for designing proactive retention strategies and personalised interventions in MOOCs. Despite limitations related to data scope, computational complexity, and temporal coverage, the findings establish a replicable and extensible model for future cross-platform and longitudinal studies in predictive educational analytics. Compared to state-of-the-art approaches, this study proposes a hybrid GA–CFS feature selection method embedded in a stacked ensemble framework. The proposed approach yields a more compact feature set while enhancing prediction accuracy and robustness compared to conventional single-method techniques.9 0Item Restricted Effectiveness of Community-Based Early Warning Systems in Reducing Dengue Incidence in Endemic Regions: A Scoping Review(Saudi Digital Library, 2026) ALHAJOOJ, Mohammed; Philip, BakerAbstract Dengue fever has become increasingly prevalent and poses a threat to public health worldwide. Despite advances in surveillance and vector control, dengue responses remain largely reactive, relying on passive case reporting systems that limit timely outbreak detection and response(1-3). In addition to improvements in surveillance and vector control, early warning systems have been developed to improve outbreak predictions by integrating data from epidemiology, entomology, and the environment. Recently, there has also been interest in developing community-based early warning systems (CBEWS), which incorporate community knowledge and involvement into surveillance efforts (4). However, the current state of evidence on whether CBEWS can reduce dengue incidence varies widely across studies due to differences in study design and methodology. Methods This review employed a scoping review process. The literature search followed existing methodologies for conducting reviews (5, 6). A comprehensive search of four electronic databases (PubMed, Scopus, Embase, and Medline) was completed to locate all relevant peer-reviewed articles that were published between 2016 and 2026. Eligible studies included observational designs (cross-sectional, cohort, and ecological studies), modelling studies, and evidence syntheses (systematic and scoping reviews) that examined dengue surveillance or early warning systems. Also, experimental and non-randomised intervention studies were considered, where they evaluated intervention components linked to surveillance or response systems. Data extraction focused on information about the characteristics of the early warning system, how the system was implemented, what methods were used to engage communities and the effects of each variable on dengue incidence and response to dengue-related outbreaks. Results Twenty-eight studies met eligibility criteria and demonstrated three key factors that influence the success of CBEWS. First, while many dengue early warning systems demonstrate high predictive value for future dengue activity, few have shown an association with decreased dengue incidence when no prompt response mechanisms exist. Second, when communities are engaged in the surveillance effort through active participation in reporting, in surveillance sensitivity, timeliness of reporting and community awareness of dengue risk were enhanced; however, few studies have found that increased community participation results in measured declines in dengue transmission. Finally, studies where early warning systems were part of larger integrated programmes that included targeted interventions (such as vector control or biological strategies) presented higher rates of dengue incidence reduction than those without integrated components. All three variables impacted by the success of CBEWS show great variability depending on the degree to which system integration occurs with high-quality data and robust governance structures. Conclusion CBEWS provide support to enhance dengue surveillance and improve readiness for dengue outbreak, they represent inadequate stand-alone interventions. Therefore, to be successful CBEWS need to be used in conjunction with swift public health response and functional surveillance-response systems that include continued active community involvement. As identified within this review, there is a clear need to transition from predictive models towards integrated-actionable models for addressing dengue in endemic areas. Keywords: Dengue, Early Warning Systems, Community-Based Surveillance, Vector Control, Public Health, Disease Prevention, Synthesis of Literature10 0Item Restricted Defining cAMP regulated cell death in Glioblastoma(Saudi Digital Library, 2026) AlDaweesh, Amnah Mohammed H; Mantamadiotis, TheoGlioblastoma (GBM) is the most aggressive type of malignant brain tumour in adults. Despite treatment, GBM patients have a poor median survival rate of approximately 15 months. This poor survival is related to apoptosis resistance in GBM tumour cells. The cyclic adenosine monophosphate (cAMP) signalling pathway is a well-established second messenger system that regulates both cell survival and death, but its role in GBM is still not fully understood. As such, this thesis investigated if the cAMP pathway affects the expression of genes regulating apoptosis and cell viability in GBM cells. It also examined whether expression differences in these genes correlate with patient survival and treatment response, alongside the effect of forskolin-induced cAMP activation on tumour behaviour. To achieve this, cAMP gene expression signature analysis was performed using different public datasets, followed by literature validation of 30 selected genes. TCGA-GBM data were used for gene expression and survival analyses, while selected cAMP genes were used to analyse pathway enrichment across glioma grades and sex differences. An in vivo GBM mouse model was treated with forskolin, TMZ or vehicle control, followed by H&E staining, IHC for pCREB, BIM and cleaved caspase-3, and digital image analysis. Furthermore, the 30-gene cAMP signature showed different expression levels across GBM samples. Survival analyses did not show a significant survival effect for the selected cAMP genes. cAMP enrichment was lower in GBM than in LGG, and male and female GBM samples had similar enrichment scores. In the histological analysis, the forskolin group showed a less dense tumour region on H&E staining. IHC staining showed BIM, cleaved caspase 3 and pCREB signals in tumour regions, with stronger staining in the forskolin group. These results show that cAMP activation affects apoptosis in GBM tissue and cell viability11 0Item Restricted Intelligent Fault Detection for Belt Conveyor Idlers Using Machine Learning(Saudi Digital Library, 2026) Alharbi, Fahad; Luo, Suhuai; Zhang, Hongyu; Chen, Zhiyong; Wheeler, CraigConveyor belt systems are essential components of modern mining, logistics, and manufacturing operations. However, faults in idlers, which are the rollers that support and guide the belt, can reduce system reliability, cause unplanned downtime, increase maintenance costs, and create safety risks. Conventional inspection methods, such as visual assessment and manual listening, are widely used to identify abnormal idler behaviour, but they are labour intensive, subjective, and difficult to apply across large conveyor systems. Acoustic monitoring offers a promising contactless alternative because developing faults often produce changes in sound before complete failure occurs. However, these fault related acoustic signatures can be subtle and may be masked by environmental and operational noise, making them difficult to interpret using traditional signal processing methods alone. Machine learning (ML) has therefore been increasingly applied to acoustic signals to automate feature extraction, fault detection, and classification. Nevertheless, existing ML approaches still face several important challenges, including the limited availability of labelled fault data, inadequate generalisation across recording conditions and sensing platforms, and insufficient modelling of the spatial and temporal characteristics of acoustic signals. This thesis addresses these challenges by developing intelligent fault detection (IFD) models that automatically analyse acoustic signals from belt conveyor idlers. The research follows a progressive methodology comprising supervised transfer learning, semi-supervised anomaly detection, task-specific spatial–temporal deep learning, and cross-domain Convolutional Neural Network (CNN)–Transformer adaptation. To support these investigations, acoustic datasets were collected using handheld microphones and drone-mounted recorders under multiple idler operating and fault conditions. The first study investigated supervised fault classification using the original handheld acoustic dataset, which contained 255 four-second samples representing Normal, Stage 1, Stage 2, and Stage 3 operating conditions. Embeddings were extracted using YAMNet, a pre-trained audio neural network, and processed using Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) networks to model contextual relationships in both forward and backward temporal directions. Attention mechanisms and an Extreme Gradient Boosting (XGBoost) classifier were also evaluated. The best-performing YAMNet–BiLSTM configuration achieved an accuracy of 90.59% and an F1-score of 90.57% for four-class fault-stage classification. Although this study established a strong supervised baseline, it required labelled examples from every operating condition and fault stage. This dependence limits practical application because faulty-idler recordings are relatively rare, costly to collect, and difficult to label in industrial environments. To address this limitation, the second study developed CASSAD (Chroma-Augmented Semi-Supervised Anomaly Detection), which was trained exclusively on normal operating samples during training. Following the first study, additional handheld recordings were collected, increasing the dataset from 255 to 468 acoustic samples. This larger dataset enabled CASSAD to be evaluated using a broader set of normal and abnormal operating recordings. CASSAD combines Chroma-STFT, Chroma-CQT, and Chroma-CENS representations with filtering, statistical aggregation, and a one-class support vector machine (OC-SVM). On the expanded 468-sample handheld dataset, the best-performing CASSAD configuration achieved an accuracy of 90.59%, a positive-class F1-score of 92.42%, and an Area Under the Receiver Operating Characteristic Curve of 96.29%. To enable a direct comparison with the YAMNet-based models, CASSAD was also evaluated on the original 255-sample dataset after the three fault stages had been combined into a single Abnormal class. In this binary evaluation, CASSAD achieved an accuracy of 93.00% and an F1-score of 93.25%, compared with an accuracy of 92.18% and an F1-score of 93.00% for the strongest YAMNet-based configuration. These results demonstrate that competitive anomaly-detection performance can be achieved without labelled abnormal samples during training. However, CASSAD provides only binary Normal–Abnormal decisions and uses temporally aggregated features, limiting its ability to distinguish fault severity and model changes in acoustic behaviour over time. To overcome these limitations, the third study developed TD-CLNet, a Time-Distributed CNN–Long Short-Term Memory (LSTM) architecture designed to perform multi-stage fault classification while learning spatial and temporal representations from the acoustic data. The expanded 468-sample handheld dataset was re-segmented into one-second samples and converted into log-Mel-spectrogram frames. TD-CLNet applies a shared CNN feature extractor to each frame and then uses an LSTM to model the resulting temporal feature sequence. This design enables the model to distinguish among Normal, Stage 1, Stage 2, and Stage 3 conditions while preserving temporal information that was reduced through the statistical aggregation used in CASSAD. Under four-fold cross-validation, TD-CLNet achieved a mean accuracy, precision, recall, and weighted F1-score of 92.1%. It outperformed the evaluated conventional CNN–LSTM configurations and provided a small improvement over the strongest YAMNet model re-evaluated on the same expanded dataset. Nevertheless, the sequential processing used by LSTM networks limits parallel computation and provides less direct access to broader global relationships within the acoustic feature sequence. To address these limitations and investigate cross-domain generalisation, the fourth study developed hybrid CNN–Transformer models using four pre-trained CNN backbones: ResNet-18, DenseNet-121, EfficientNet-B0, and ShuffleNet-V2. Two acoustic feature representations were investigated: Mel-spectrograms, which represent the distribution of signal energy across perceptually scaled frequency bands over time, and Mel-Frequency Cepstral Coefficients (MFCCs), which provide a compact representation of the short-term spectral envelope. The CNN backbones extracted local spectral representations, while Transformer encoders modelled broader contextual relationships within the feature sequences. In the first phase, the models were trained and evaluated using 0.5-second segments derived from the handheld source-domain recordings. The ResNet-18–Transformer models achieved a cross-fold mean accuracy of 96.9%, with a 95% confidence interval of 96.4%–97.6%, while a ResNet-18–Transformer ensemble increased the handheld-domain accuracy to 98.0%. In the second phase, the models trained on the handheld source-domain dataset were adapted to the drone-acquired target-domain dataset. The drone recordings represented a more challenging sensing environment because they were affected by rotor noise, changing recording distances, varying microphone positions, and environmental interference. Multiple fine-tuning strategies were evaluated, included full fine-tuning (Full-FT), freezing the CNN backbone (CNN-Frozen), freezing the Transformer encoder (TR-Frozen), and 𝐿2-SP regularisation. Mean–Covariance Alignment (MCA) was compared with a cross-entropy (CE) baseline and several established domain-adaptation methods. MFCCs produced the strongest CE baseline in several drone-domain experiments, achieving an accuracy of 90.5% under 𝐿2-SP. However, MFCC performance generally decreased when MCA was applied. In contrast, MCA improved the Mel-spectrogram Full-FT configuration, increasing accuracy from 84.7% to 86.0% and the F1-score from 83.4% to 85.6%. These findings demonstrate that domain-adaptation performance depends on the interaction among the acoustic representation, fine-tuning strategy, model architecture, and alignment objective; no single adaptation method was uniformly optimal across all evaluated configurations. In summary, this thesis establishes a connected research pathway for acoustic fault detection in belt conveyor idlers. It provides handheld and drone-acquired acoustic datasets, establishes supervised transfer-learning baselines, introduces CASSAD for anomaly detection without labelled abnormal training samples, develops TD-CLNet for task-specific spatial–temporal fault-stage classification, and proposes a two-phase CNN–Transformer framework for handheld-to-drone domain adaptation. Collectively, these contributions advance acoustic idler monitoring towards more accurate, data-efficient, and adaptable fault detection while identifying the need for further validation across additional industrial sites, conveyor configurations, sensing platforms, and operating conditions before large-scale real-world deployment.18 0Item Restricted Multiscaling Asymptotic Behaviour of Random Fields Driven by SPDEs(Saudi Digital Library, 2026) Alghamdi, Maha Mosaad A; Andriy, Olenko; Nikolai, LeonenkoThis thesis studies the asymptotic behaviour of random fields which are solutions of stochastic partial differential equations with random initial conditions exhibiting long-range and cyclic long-range dependence. The main focus is on investigating multiscaling limits and their spectral and covariance structures. The cases of solutions to fractional, high-order, and Riesz–Bessel type equations are considered. The results are obtained for the cases of arbitrary multiple spectral singularities, which have not been considered before. First, the thesis investigates multiscaling limits for stochastic fractional equations with random initial conditions possessing cyclic long-memory. The corresponding rescaled solutions are shown to converge to Gaussian limit fields. Their explicit spectral and covariance representations are provided. Next, the thesis studies high-order heat equations with random initial conditions whose spectra have singularities at zero and at non-zero frequencies. Using spectral and scaling techniques, it is proved that the normalised solutions converge to Gaussian random fields determined by the presence or absence of a zerofrequency singularity. Kernel averaging is introduced for odd-order equations to obtain non-degenerate limits. Finally, the thesis analyses fractional Riesz–Bessel equations with initial conditions exhibiting both classical and cyclic long-range dependence. It is proved that the rescaled solutions converge to spatio-temporal Gaussian random fields that are stationary in space and non-stationary in time. In addition, multiscaling limit theorems are derived for the case of regularly varying asymptotics.4 0Item Restricted The Influence of Saudi Women’s Empowerment on Female Characters in Children’s Literature: A Feminist Critical Discourse Analysis(Saudi Digital Library, 2026) Alomari, Shatha; Bulfin, Scottتكمن أهمية هذه الدراسة في محاولتها فهم التقاطع بين أدب الأطفال والواقع الاجتماعي، إذ تستكشف كيف أثّرت سياسات تمكين المرأة في السعودية على الشخصيات النساء وتمثيلها في قصص الأطفال من خلال مقارنة أربعة قصص: اثنتان نُشرتا قبل عام 2016 واثنتان بعده. تناولت الدراسة ثلاثة أسئلة رئيسية: الأدوار التي أُسنِدت للشخصيات النسائية، ومستوى الوكالة التي مارستها داخل السرد، ومدى اتساق القصص المنشورة بعد مرحلة التمكين مع الخطاب الوطني حول تمكين المرأة. اعتمد البحث منهجًا نوعيا مقارنًا يستند إلى التحليل النسوي النقدي للخطاب، مستخدما نموذج فيركلوف الثلاثي كأداة التحليل الأساسية. أظهرت النتائج استمرار الأدوار الجندرية التقليدية في القصص الأربع، في حين أبدت الشخصيات النسائية درجات متفاوتة من الوكالة تأثرت بالسياق الاجتماعي لكل فترة زمنية. كما كشف التحليل أن القصص المنشورة بعد التمكين عكست الخطاب الإعلامي المصاحب لرؤية 2030، وفي الوقت نفسه أعادت إنتاج بعض الأيديولوجيات الجندرية وقاومت بعضها الآخر. وبناءً على هذه النتائج، يمكن القول إن القصص اللاحقة لمرحلة التمكين تأثرت بسياسات تمكين المرأة. ومع ذلك، تبقى النتائج محدودة بسبب صغر حجم العينة، وهو أحد قيود الدراسة. لكن تفتح هذه الدراسة المجال أمام مزيد من الأبحاث حول تأثر أدب الأطفال بالتحولات الاجتماعية.21 0Item Restricted Identifying Cooler and Flood-Safer Urban Zones on Riyadh's Fringe Using a Digital Elevation Model (DEM) and the National Geoid Model KSA-Geoid21(Saudi Digital Library, 2026) Aljebreen, Suliman; Dewan, AshrafThis thesis develops a GIS-based suitability framework to identify relatively cooler and flood-safer zones for urban fringe planning in Riyadh, Saudi Arabia. The study integrates two physical indicators: (i) seasonal Land Surface Temperature (LST) derived from Landsat data, and (ii) DEM-derived hydrological indicators including D-Infinity flow routing, wadi channel delineation, flood-exposure modelling, and terrain slope. These indicators were standardised and combined using Weighted Linear Combination (WLC) under three weighting scenarios. A ±15% sensitivity analysis using Spearman’s rank correlation was used to assess the stability of the resulting suitability patterns. The results show that most of Riyadh’s fringe remains constrained by high thermal stress and/or flood-related exposure, while comparatively more suitable zones are concentrated mainly in the western and south-western fringe. The identified spatial pattern remained reasonably stable under different weighting scenarios, suggesting that the framework can support comparative planning decisions. All analyses were undertaken in UTM Zone 38N (EPSG:32638). The outputs include suitability maps, extracted priority zones, and summary statistics relevant to physically informed urban fringe planning in Riyadh.4 0