Comput. Sci. Math. Forum, 2026, IOCMA 2026

Forecasting petroleum prices in state-administered markets presents unique challenges distinct from those encountered in liberalized commodity exchanges. Algeria’s hydrocarbon sector, which accounts for 95% of export earnings, operates under pricing administered by the national oil company (Sonatrach), thereby creating irregular temporal dynamics, regime-dependent policy inertia, and geopolitical risk endogeneity that violate classical forecasting assumptions. Existing neural architectures fail to capture the institutional constraints and network effects of OPEC+ coordination. To address these limitations, we developed a novel three-tiered deep learning framework—the Algeria Petroleum Temporal Graph Network (APTGN)—which integrates: (1) a Phased Bidirectional Gated Recurrent Unit (GRU) encoder that handles irregular policy sampling intervals through learnable temporal gates; (2) a conflict-gated graph convolution layer that models Algeria as a node in a dynamic OPEC+ network, with edge weights modulated by geopolitical instability and compliance correlation; and (3) a regime-aware mixture density network (MDN) for uncertainty quantification during high-volatility episodes. The model was trained on proprietary daily official selling prices (OSPs, 2010–2023), augmented with Armed Conflict Location and Event Data (ACLED) conflict intensity and TASSILI shipping logistics data, using curriculum learning and a multi-objective optimization objective that combines negative log-likelihood with a quantile calibration penalty. The proposed architecture achieved a Mean Absolute Percentage Error (MAPE) of 2.48% and a coefficient of determination ( R 2 ) of 0.92 on out-of-sample testing (2022–2023), representing a 22.7% improvement over Temporal Fusion Transformer (TFT) baselines. During high-intensity conflict periods, forecast accuracy improved by 43% compared to conventional models. Ablation studies confirmed that each architectural component significantly contributed to model robustness, with the conflict gate preventing error cascade during domestic instability episodes. This work establishes a new benchmark for forecasting in administered energy markets by explicitly encoding institutional rigidity and geopolitical constraints. The framework provides actionable intelligence for fiscal planning, demonstrating a potential annual revenue forecasting error reduction of $91 million, and is transferable to other state-administered commodity markets facing similar structural challenges. Full article

Pressure projection often limits the scalability of incompressible Navier–Stokes solvers because exact incompressibility requires a globally coupled Poisson solve. We develop Locality-Certified Screened Projection (LCSP), which replaces the classical projection with a screened pressure correction and combines exponentially localized tile solves with face-correction overlap–restrict (FCOR) assembly on a staggered grid. The screening parameter sets the localization length and makes the residual divergence explicit. For a spectrally commensurate class of two-dimensional periodic flows, each patch spans one spatial subperiod, so the assembled local correction reproduces the global screened solution without iterative interface communication. Double-precision tests on 512 2 and 1024 2 grids give assembly errors below 3.30× 10 − 15 , velocity errors below 9.83× 10 − 3 relative to the classical projection, and compatible-divergence ratios below 4.83× 10 − 2 . Time-dependent tests remain stable to T = 2 and agree with the global screened reference to roundoff. Communication traces record no inter-rank exchange, collective operation, or global reduction during the local correction, and a fixed-work batch attains a speedup of 3.99 on four graphics processing units (GPUs). These results establish a mathematically controlled communication-free screened pressure correction for this periodic class. Full article

Higher-order boundary value problems model several physical phenomena in fluid dynamics, astrophysics, hydrodynamics, beam theory, astronomy, hydromagnetic stability, and engineering. Eighth-order boundary value problems arise in the physics of various hydrodynamic stability problems, advanced structural mechanics, and several other real-world systems. This paper applies an efficient collocation method based on the shifted Horadam polynomials to obtain approximate solutions of an eighth-order boundary value problem in ordinary differential equations. The Horadam collocation method expresses the solution of the proposed problem as a shifted Horadam polynomial series. Using the zeros of the shifted Horadam polynomials as the collocation points, the proposed boundary value problem is reduced to a set of algebraic equations in the expansion coefficients of the series solution. The obtained algebraic equations are then solved for the unknown expansion coefficients using Newton’s iterative method. Two illustrative examples of the proposed boundary value problem are considered for the purpose of accuracy, efficiency, and reliability of the proposed method. Numerical solutions obtained are compared with the exact solutions and other existing solutions. Comparisons of results are demonstrated in tables and graphs. It is observed that the proposed method yields higher accuracy compared to the existing methods. This research work shows that the Horadam polynomial-based collocation method is an efficient method for obtaining accurate and reliable approximate solutions of higher-order boundary value problems in ordinary differential equations. Full article

The logistics industry plays a vital role in supporting economic growth by facilitating the efficient movement of goods and services and is a key component of Malaysia’s supply chain and trade activities. However, logistics companies are highly exposed to operational risks, economic fluctuations, and financial uncertainties, particularly during challenging periods such as the COVID-19 pandemic and its aftermath from 2020 to 2024. Without systematic financial evaluation, stakeholders such as investors, management, and policymakers may face difficulties in identifying financially healthy and distressed companies. The purpose of this study is to assess the financial performance of logistics companies listed on Bursa Malaysia from 2020 to 2024 using the Zmijewski model. A total of 28 logistics companies were evaluated in this study. The findings indicate that 24 companies remained financially healthy throughout the five-year period, representing 85.71% of the sampled firms. The results suggest that the majority of logistics companies maintained sound financial conditions despite economic challenges during the study period. On the contrary, the results of the study also revealed that a small number of companies are experiencing financial distress. The findings of this study are significant as they assist investors in making informed investment decisions, enable company management to identify potential signs of financial distress, and support policymakers in understanding the financial resilience of the logistics sector. Full article

Non-homogeneous Poisson process (NHPP)-based software reliability models (SRMs) are widely used to describe software fault-detection processes. This paper develops a likelihood-free maximum product of spacings (MPS) estimation method for finite NHPP-based SRMs. Here, “likelihood-free” refers specifically to the conditional-spacing objective: it estimates the distribution parameters without directly evaluating the NHPP likelihood or intensity function, after which the scale parameter is recovered in closed form. Its predictive performance is examined through a real-data analysis of GitHub fault-count data with representative finite NHPP-based SRMs. The results show that MPS is competitive among the considered estimation–model-selection procedures and is particularly promising in early testing, where it gives the smallest PMAE in five of eight cases. Full article