Namal Institute

Financial reporting systems increasingly use large language models (LLMs) to extract and summarize corporate disclosures. However, most assume a single-market setting and do not address structural differences across jurisdictions. Variations in accounting taxonomies, tagging infrastructures (e.g., XBRL vs. PDF), and aggregation conventions make cross-jurisdiction reporting a semantic alignment and verification challenge. We present FinReporting, an agentic workflow for localized cross-jurisdiction financial reporting. The system builds a unified canonical ontology over Income Statement, Balance Sheet, and Cash Flow, and decomposes reporting into auditable stages including filing acquisition, extraction, canonical mapping, and anomaly logging. Rather than using LLMs as free-form generators, FinReporting deploys them as constrained verifiers under explicit decision rules and evidence grounding. Evaluated on annual filings from the US, Japan, and China, the system improves consistency and reliability under heterogeneous reporting regimes. We release an interactive demo supporting cross-market inspection and structured export of localized financial statements. Our demo is available at https://huggingface.co/spaces/BoomQ/FinReporting-Demo . The video describing our system is available at https://www.youtube.com/watch?v=f65jdEL31Kk

Novel nanomaterial applications claim distinct uses in thermal engineering, cooling processes, heat transfer devices, and automobile industries, among others. Motivated research uses modified heat and mass flux theories to present thermal observations for the unsteady flow of magnetized Maxwell nanofluid, confined by porous bidirectionally stretched surfaces. The heat transfer model's extension is based on Joule heating and heat source effects. The Cattaneo-Christov theories govern the expansion of mass and heat transfer. We analyze thermal problems under zero-mass diffusion constraints. The use of proper variables simplifies mathematical modeling into a dimensionless form. The Homotopy Analysis Method (HAM) solves the dimensionless system. The paper highlights the convergence criteria for the HAM procedure. Graphics underline the problem's physical perspective. We observe that the Deborah number enhances heat and mass transfer. The temperature profile decreases when the parameter becomes unstable.

The nanofluid flow through a Riga device has vibrant applications in heat transfer, biomedical engineering specifically to direct the nanoparticles in a particular area. Also, enhanced properties of nanofluids are advantageous for therapeutic applications like hyperthermia treatment. Thus, the current study aims to model a novel tetra nanofluid for enhanced heat transfer applications. The model accommodates the influence of heating source, convective condition, dissipation and magnetization. The tetra nanofluid comprises the NPs of Al2O3, TiO2, CuO and Ag owing to their excellent thermal characteristics. The developed model analyzed numerically and then investigated the influence of the parameters. It is examined that the unsteady (A=0.1,0.5,0.9,1.3), radiations (Rd=0.1,0.3,0.5,0.7) and heating source parameters excellently promote the heat transfer under in the presence of dissipation effects in nanofluids. However, the strong Lorentz forces produced due to magnetic field (M=1.0,2.0,3.0,4.0) observed good to maintain the system's cooling. Further, the skin friction coefficient enhances due to increasing strength of magnetic field and unsteady number. Further, the tetra nanofluid possesses dominant heat transfer rate than conventional ternary, hybrid and simple fluids due to promising thermal conductivity. Moreover, the Eckert number (Ec=0.1,0.2,0.3,0.4) observed as a key tool to augment the heat across all under consideration nanofluids, while tetra type shows higher increasing trends. On the basis of current findings, the tetra nanofluids are suggested more efficient heat transfer fluids than the previous classes.

Employee turnover presents a significant challenge to modern organizations, often resulting in operational disruptions, substantial hiring costs, and a loss of institutional knowledge. While traditional human resource practices have historically been reactive, the emergence of machine learning has introduced a proactive capability to anticipate and mitigate attrition before it occurs. This research utilizes the IBM HR Analytics dataset, which contains 1470 employee records and 35 distinct features, to develop a hybrid machine learning model designed to enhance the accuracy of turnover predictions. To ensure the model’s effectiveness, the researchers employed a comprehensive preprocessing phase that included eliminating non-informative features, applying label encoding to categorical data, and using StandardScaler to normalize quantitative values. A critical component of the study addressed the common issue of class imbalance within HR data. To resolve this, a hybrid sampling strategy was implemented, combining Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to create a more balanced learning environment for the algorithms. The core of the predictive engine is a soft voting ensemble that integrates three powerful algorithms: Random Forest, XGBoost, and logistic regression. Evaluated on an 80/20 train–test split, the tuned XGBoost model achieved an impressive 84% accuracy and an Area Under the Curve (AUC) of 0.80. Meanwhile, the logistic regression component contributed the highest F1-score, reinforcing the overall strength and balance of the ensemble approach. These metrics confirm that the hybrid model is both robust and reliable for identifying at-risk employees. Beyond simple prediction, the study prioritized interpretability by using SHapley Additive exPlanations (SHAP) to identify the primary drivers of attrition. The analysis revealed that the most significant variables influencing an employee’s decision to leave include the interaction between job level and experience, frequent overtime, monthly income, current job level, and total years spent at the company. By providing these data-driven insights, the model empowers HR teams to transition from reactive troubleshooting to proactive retention planning, ultimately securing the organization’s talent and stability.

Efficient management of aerodynamic heating on the curved peak-temperature surfaces necessitates precise modeling of nonlinear radiated and drag-induced stagnation point features, motivating the need generalized similarity frameworks for nanofluid flows. This investigation scrutinizes curved surface nanofluid flow by incorporating the Darcy-Forchheimer effects. The inspiration of heat and mass transfer mechanisms is subject to applications of nonlinear radiated effects. Buongiorno nanofluid model has been incorporated to inspects thermophoresis and diffusion of Brownian movement, capturing the intricate transport characteristics of nanofluids. Truncation analysis is employed to simplify the mathematical model, ensuring analytical feasibility while preserving the essential physics of the problem. The consequential system of equations is numerically complied by using shooting scheme with high-accuracy approximations. The influence of significant parameters, including porosity constant, Forchheimer parameter, radiation constant, and nanoparticle concentration, is systematically scrutinized. The observations attribute that higher velocity ratio coefficient leads to enhancement of velocity while declining effects are observed for curvature constant. Consideration of nonlinear thermal radiation effects significantly augments the heat transfer. The proposed simulations provide significance to drug delivery systems, automotive sectors, petroleum engineering, advanced thermal systems, energy conversion and industrial cooling technologies.