Gurleen Kaur

Abstract: In this paper, the cyclic codes of length n , where n is odd with certain restrictions, over a finite chain ring R , have been studied using the structure of group algebra approach.

Aims To explore the attitudes of undergraduate dental students towards dental foundation training (DFT), also known as dental vocational training in Scotland, and other early career pathways. Methods An online questionnaire was distributed via the British Dental Students' Association's communication channels to UK dental students using convenience and snowball sampling. The survey included closed- and open-ended questions. Quantitative data were analysed statistically and thematic analysis was applied to open-ended responses. Results A total of 177 responses were received, primarily from final-year students. Nearly all respondents planned to apply for DFT, with 25% aiming for NHS-only work, 52% for mixed NHS and private work, 9% private-only, 4% abroad, and 10% undecided. Expected earnings post-DFT ranged from £30,000 to £70,000 (median £50,000–£60,000) and a trend toward reducing workdays over time was noted. Conclusion UK dental students perceive DFT as a valuable step in preparing for independent practice. Insights into student attitudes, along with concerns about NHS challenges, financial pressures and career pathways, can inform workforce planning and policy, supporting sustainable development of the dental profession.

As digital marketing continues to dominate global outreach strategies, worldwide spending on digital advertising is projected to surpass $785 billion by 2026. This surge is driven by real-time data analytics, behavioral targeting, and AI-powered personalization, all of which rely heavily on the large-scale collection and processing of consumer data. However, growing regulatory pressure from data privacy laws such as GDPR and CCPA, along with increasing public scrutiny, has elevated the demand for privacy-preserving and decentralized analytics frameworks. This study introduces an adaptive federated learning FL framework tailored for ordinal classification in digital marketing environments. The proposed system integrates two ordinal classifiers CORAL and CLM with a novel adaptive aggregation strategy that assigns dynamic weights to clients based on their contribution relevance, measured via feature importance. The experimental setup simulates a realistic collaborative marketing scenario involving five federated clients, each handling either real-world as Google Merchandise Store, UK Online Retail or synthetic as influencer and email campaign datasets. Through extensive experimentation, the framework demonstrates strong generalization across synthetic and real datasets, achieving classification accuracies up to 93.9%. Scalability tests across 5, 10, 15, and 20 clients validate the robustness of the aggregation method, with performance degradation remaining within 5%. The framework is benchmarked against baseline federated strategies such as FedAVG, FedSGD, and FedProx, and is evaluated under practical constraints using deployment-aligned analysis with frameworks like NVIDIA Clara, OpenFL, and Flower. This manuscript presents multiple comprehensive experimental analyses including convergence trends, client contribution evolution, resource utilization, and fairness-aware aggregation, making it a comprehensive study on privacy-preserving, ordinal, and adaptive federated analytics for modern digital marketing systems.

Federated Learning (FL) enables privacy-preserving collaborative model training across distributed agricultural IoT networks, yet existing aggregation strategies such as Federated Averaging (FedAvg), Trimmed Mean, and Krum either assume data homogeneity or discard informative outlier updates, limiting performance in heterogeneous soil environments. This study introduces the Hybrid Federated Averaging–Weighted Winsorized Aggregation (WWA) framework for multiclass soil quality prediction across five distributed farms. The method employs FedAvg for warm-up stabilisation, then applies metadata-driven trust weighting and winsorization from round six onward, attenuating rather than discarding divergent client updates to preserve rare but meaningful soil patterns. Formal definitions of the trust weight computation and sensitivity analyses of weighting hyperparameters confirm the stability and fairness of the aggregation mechanism. Experiments on both a synthetic federated soil dataset and the real-world Crop Recommendation Dataset demonstrate consistent superiority of the proposed approach: on the primary dataset, Hybrid FedAvg–WWA achieves 96.9% accuracy and 96.2% F1-score, outperforming FedAvg (94.1%), FedProx (95.0%), and TrimmedFL (95.4%), while reducing communication rounds by 25% and improving the weakest client’s accuracy by up to 3.5 percentage points.

This report details the diagnosis and management of a 60-year-old postmenopausal woman presenting with a 3-year history of progressive exertional dyspnea. High-resolution computed tomography (HRCT) revealed diffuse, thin-walled cystic lung lesions. Serum vascular endothelial growth factor-D (VEGF-D) was below the diagnostic threshold (800 pg/mL), and no other non-invasive confirmatory features, such as tuberous sclerosis complex, renal angiomyolipoma, chylous effusion, or lymphangioleiomyoma, were present; therefore, a video-assisted thoracoscopic surgery (VATS)-guided surgical lung biopsy was pursued. Histopathology confirmed the diagnosis of lymphangioleiomyomatosis (LAM) through the identification of proliferating spindle-shaped cells within cyst walls that stained positive for HMB-45, smooth muscle actin (SMA), and estrogen receptors. The patient was treated with sirolimus (titrated to a target trough of 5 - 15 ng/mL), resulting in stabilized lung function and the ability to discontinue supplemental oxygen after 12 months. The case underscores the importance of considering LAM in the differential diagnosis of diffuse cystic lung disease in women of all ages, including those who are postmenopausal.