This paper addresses the Longest Filled Common Subsequence (LFCS) problem, a challenging NP-hard problem with applications in bioinformatics, including gene mutation prediction and genomic data reconstruction. Existing approaches, including exact, metaheuristic, and approximation algorithms, have primarily been evaluated on small instances, which provide limited insight into their scalability. In this work, we introduce a new benchmark dataset with significantly larger instances and demonstrate that existing datasets lack the discriminative power needed to meaningfully assess algorithm performance at scale. To solve large instances efficiently, we utilize an adaptive Construct, Merge, Solve, Adapt (CMSA) framework that iteratively generates promising subproblems via component-based construction and refines them using feedback from prior iterations. Subproblems are solved using an external black-box solver. Extensive experiments on both standard and a newly introduced set of large-sized instances prove that the designed adaptive CMSA achieves state-of-the-art performance, outperforming four known approaches. Notably, among 1510 problem instances with known optimal solutions, our approach matches the proven optimum on 1486 instances—corresponding to 98.4% of these instances. These results demonstrate substantially improved scalability on large problem instances. As an engineering contribution, we propose a novel application of LFCS to song identification from degraded audio excerpts, using real-world energy-profile instances derived from popular music. Finally, we conduct an empirical explainability analysis to identify critical feature combinations influencing algorithm performance, revealing the key problem features associated with the success or failure of the approaches across different instance types.
Ultra-precision machining of silicon underpins semiconductor, optical and MEMS manufacturing, yet atomistic predictions remain highly sensitive to the interatomic potential employed. Here, seven popular interatomic potentials of silicon were benchmarked for thermal, cohesive, stacking-fault and tensile properties, with five carried through detailed dislocation and nanometric-cutting analyses and compared against TEM characterisation of ground silicon. The predicted responses vary markedly: apparent one-phase thermal-instability temperatures span 2309–3427 K, the unstable stacking-fault energies associated with the first {111} glide-set Shockley-partial displacement range from 1.918 to 2.948 J m−2, and Young's moduli vary from 65.06 to 99.93 GPa. Fitted dislocation-core energies, i.e., Ec(110) differ by almost a factor of six, from 0.1109 to 0.6398 eV Å−1. These differences translate into distinct stress-accommodation pathways during cutting. ABOP (Erhart and Albe) and classical Tersoff revealed localised deformation within narrow transformed/disordered subsurface regions, Stillinger–Weber activates transient dislocation-mediated plasticity, Screened ABOP promotes extensive disorder-mediated accommodation, and Kumagai exhibits an intermediate response. TEM observations of ground silicon revealed coexisting amorphisation, {111} stacking faults and partial-dislocation activity, supporting a mixed-mechanism picture of machining-induced damage. The results show that predictive atomistic simulations of silicon machining require mechanism-specific potential selection rather than validation against a single bulk property.
Insights generated by advanced analytics in smart grids are critical for delivering safe and reliable power and providing value-added services in a cost-effective manner. However, electricity consumption data collected from advanced metering infrastructure (AMI) often contain missing values due to measurement errors, communication failures, device outages, or other unknown causes. Thus, missing data imputation is critical for ensuring the reliability of data-driven analysis. The existing literature has applied various imputation methods for electricity consumption data, but systematic comparison across different method families, missingness rates and contiguous gap sizes remain limited. In this work, we conducted a comparative analysis of eleven widely used imputation methods, including persistence-based, fixed statistical methods, time-series based, and machine learning based approaches, using high-resolution electricity consumption data from Slovenian households. The methods are evaluated using normalized root mean square error (NRMSE) as the key performance metric. Statistical significance testing is further used to determine whether the observed performance differences between the leading methods are consistent across households and experimental settings. The results show that the performance of imputation methods is strongly influenced by both missingness rate and gap size. While linear interpolation performs well for very short gaps, methods capturing historical and contextual consumption patterns perform better for medium and long gaps. Among the eleven evaluated methods, XGBoost achieves the best overall performance, with the lowest average NRMSE. Pairwise Wilcoxon signed-rank tests further confirm that XGBoost significantly outperforms the leading competing methods in most evaluated configurations. The consumer level analysis also shows that the most accurate method on average is not always the most consistent across households. Thus, researchers and practitioners should consider gap size, temporal structure, and consumer-level variability, in addition to the percentage of missing values, when selecting imputation methods for smart meter data.
Nonequilibrium Green's functions provide a powerful framework for studying quantum many-body dynamics including the laser-induced dynamics in solids. The Non-Equilibrium Systems Simulation package (NESSi) offers an efficient platform for such simulations, ranging from perturbative approaches like nonequilibrium GW to nonequilibrium dynamical mean-field theory. However, simulations based on nonequilibrium Green's functions become computationally demanding when the dynamics span a large temporal range, such as from subfemtosecond electron dynamics to the picosecond dynamics of collective modes. Due to the memory integral in the Kadanoff-Baym equations, which serve as equations of motion for nonequilibrium Green's functions, the computational cost scales as O(Nt3)with the number of timesteps Nt, and the memory requirement scales as O(Nt2 ). In this work, we extend NESSi by incorporating techniques that aim to overcome this bottleneck: (i) By truncating the memory integrals in the KBE to a maximum of Nc timesteps, the computational complexity is reduced to O(NtNc2), and the memory requirement to O(Nc2). Provided that the results converge with respect to the cutoff Nc, memory truncation allows to extend the simulations to significantly longer times. (ii) We introduce functionalities to describe nonequilibrium steady states, i.e. time-translationally invariant nonequilibrium states. Such states are relevant for transport settings, and they provide an approximate description of slowly evolving (prethermal) nonequilibrium states.
Safety relevant questions during severe accidents in a nuclear power plant may concern the ex-vessel debris bed coolability in combination with molten corium concrete interaction. The behaviour within the debris bed is expected to be multidimensional, as the bed may be irregular in geometry, composed of heterogeneous particles, exhibit spatially varying porosity, be flooded from different directions, partially re-melt due to local or global dry out, and be affected by non-condensable gases. Fundamental investigation of such conditions requires consideration of multidimensional behaviour. It can be supported by top-flooding experiments and simulations with validated computer codes.This paper evaluates the suitability of an axisymmetric description for analysing multidimensional behaviour during debris bed quenching under top-flooding conditions. Analyses of recent FLOAT experiments (IKE, Germany) show that the multidimensionality of the quenching process is governed by the location of the penetrating water. A detailed numerical investigation performed with the MC3D code (ASNR, France) provides additional valuable insights supporting the interpretation of the experimental results. The axisymmetric modelling shows good qualitative agreement with the measurements. An improved quantitative description is possible with non-axisymmetric modelling. Sensitivity to air flow, initial temperature, porosity, and friction modelling is observed.
