Breast cancer is the most frequently diagnosed cancer globally, annually affecting around 2 million women. Situation is getting worse with rising incidence linked to improved detection, risk factors, and enhanced registration systems. Conventional treatments like surgery, chemotherapy, radiotherapy, and hormonal therapy with several limitations are replaced by approaches like immunotherapy, HER2-targeted therapies, and nanotechnology offering improved outcomes, in metastatic cases. Risk factors range from lifestyle (alcohol, obesity, inactivity, smoking), to hormonal imbalance (early menarche, late menopause, nulliparity), to genetic aspects (BRCA1/2, TP53), to environmental determinants as well. Prognostic biomarkers now lead precision medicine: PR, ER, and HER2 stands strong as established pillars, while circulating tumor DNA, and immune-related markers such as PD-L1 offer profound perceptions into treatment response and disease progression. State-of-the-art treatment integrates traditional modalities like surgery, radiotherapy, and chemotherapy with targeted and immune-based therapies. Endocrine agents, PARP inhibitors, HER2-directed monoclonal antibodies, and checkpoint inhibitors exemplify the architype swing toward personalized, mechanism-based interventions. The insight underscores the need for twin tactics, leveraging molecular detections for precision oncology while guaranteeing impartial global access to modern therapies. Future progress depends on translational research, and biomarker validation that bridge the gap between innovation and accessibility.
Gradient boosting models predict retail demand effectively, yet inventory planners distrust predictions they cannot interpret. This paper addresses the trust gap through a four-level Explainable AI (XAI) framework that pairs LightGBM prediction with layered interpretability: Permutation Feature Importance (PFI) for global ranking, Partial Dependence Plots (PDP) for marginal effects, SHapley Additive exPlanations (SHAP) for instance-level attribution, and Individual Conditional Expectation (ICE) plots for heterogeneity detection. ICE analysis, the key extension beyond prior work, reveals when averaged trends mask divergent product behavior, justifying segment-specific inventory policies. Validated on the UCI Online Retail II dataset with 41 engineered features, the proposed model achieves RMSE of 33.71 and R(2 )of 0.243 on inherently intermittent stock-keeping unit (SKU)-level daily demand, significantly outperforming most baselines (paired t-test, p <0.001; CatBoost not significant, p = 0.917). Ablation experiments confirm rolling statistics as the dominant feature group ( Delta R-2=-0.064 upon removal). ICE heterogeneity analysis identifies product clusters where temporal features produce opposite effects, demonstrating that uniform reorder policies systematically misallocate inventory for specific segments.
Forecasting intermittent and lumpy demand is a significant supply chain challenge, as traditional metrics like MAPE and RMSE are unreliable for such sparse data. This study provides a comprehensive comparative evaluation of forecasting methods, benchmarking statistical, machine learning, and deep learning models across 3,671 intermittent time series. We utilized robust, cost-sensitive metrics like SPEC, alongside MAAPE and MWQL, for a more accurate assessment. Our results demonstrate that deep learning models significantly outperform traditional and machine learning approaches. DeepAR achieved the best probabilistic forecasting, making it ideal for managing uncertainty. In contrast, the Deep Renewal Hybrid model excelled in point accuracy. Statistical methods remained competitive in cost-sensitive scenarios, while machine learning models underperformed. This research establishes a practical decision framework to guide practitioners in selecting the optimal model based on specific operational priorities, whether they are focused on uncertainty, accuracy, or cost. This provides actionable, evidence-based recommendations for real-world supply chain implementation.
Depression is a significant public health concern, as it is among the causes of the burden of disease in the world. Both genetic and environmental factors define whether one is at risk of becoming depressed or not. Although genetic influences cannot be changed, it is vital to find out possible reversible environmental factors and make an attempt to restrict the manifestation of depression. As a timely intervention and effective mental care, it is important to identify cases of depression as early as possible. In this work, the authors provide a machine learning (ML) predictive model of depression severity as applied to the DASS (Depression Anxiety Stress Scales) dataset. The LSTM (Long Short-Term Memory) model and XGBoost (Extreme Gradient Boosting) were developed and tested with better results on capturing sequential patterns and crucial emotional features. A lot of pre-processing of data, feature selection, and class balancing methods, such as SMOTE (Synthetic Minority Over-sampling) and random oversampling, were used to increase the reliability of models. The LSTM model got 99.73% in accuracy, precision, recall and F1-score, whereas XGBoost got 99.48% as compared to the baseline models, namely BERT (Bidirectional Encoder Representations from Transformers), Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR) and Naïve Bayes (NB). The findings support the effectiveness of the suggested methodology in effective, consistent, and almost perfect depression classification, which contributes to its possible use in the field of practice as a mental health assessment and early intervention.
This paper investigates how consulting services affect audit quality, from the perspective of knowledge-and expertise-sharing between employees. Semistructured interviews with 16 audit partners reveal that consulting expertise is used in 60-80 percent of audit engagements, with the main rationale for such collaboration being knowledge-sharing and improved audit quality. We leverage a comprehensive office-level dataset of employment profiles covering 86 percent of all employees at large U.S. public accounting firms to systematically investigate the effect of consulting employees on audit quality. We document that a one standard deviation increase in the share of consulting employees in an office results in a 2.6 percentage point reduction in restatements (a decrease of 19 percent relative to the baseline). This effect is strongest when consulting employees have skills complimentary to auditors, e.g., technical and human resources skills, and when consultants have specific industry expertise in the same industry as the audit client.
