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Software-Defined Networking (SDN) separates data and control planes for flexible management, but large-scale networks need multi-controller collaboration to avoid single-point failures, leading to the NP-hard Controller Placement Problem (CPP). This paper proposes a Topology-based Discrete Water Wave optimization algorithm (TDWWO). It discretizes traditional WWO for 2D geographic coordinate topology graphs, integrating node location and connection info. By using WWO’s propagation, breaking and refraction operators, it balances exploration and exploitation for optimal controller deployment. Notably, when discretizing WWO, node coordinate info is stored via TDtree and applied in propagation-stage searches. A hash table and controller number array accelerate calculations by eliminating redundant traversal. To verify the effectiveness of the TDWWO algorithm, experiments are conducted on five real topologies, and comparisons are made with multiple standard and state-of-the-art algorithms as well as the global optimal solution. The results show that TDWWO outperforms four heuristic algorithms, reducing the SC-avg and SC-worst latencies by up to 65.14% and 31.99%. It ensures calculation stability within 7.28% margin for large-scale topologies and maintains an error rate [Formula: see text]1.18% versus the global optimal solution.

Fast computation with low-energy treatment has an immense effect on the advancement of modern society, including industry, healthcare, education sector and telecommunication. Complementary Metal Oxide Semiconductor (CMOS) chips in classical computers suffer many types of difficulties such as short channel effects, leakage current, switching speed, energy consumption and packaging density. These complications become significant when applied in integrated circuits. The substitute for CMOS chips is Quantum-Dot Cellular Automata (QCA) nanotechnology. The QCA-based digital designs are compact, fast and involve low energy dissipation as compared to CMOS-based digital designs. This research brings a compact-size QCA-based Arithmetic Logic Unit (ALU) having 151 cells with an area of 0.143 [Formula: see text]m 2 . This single-bit ALU circuit is extended to design an efficient 2-bit ALU and then 4-bit ALU circuits. The proposed design reveals 47% improvement in energy dissipation and 39% in cell count over the recent 1-bit ALU design. The proposed designs are structured and simulated on QCA Designer 2.0.3 and estimation of power dissipation has been conducted on QCA Designer-E software.

This study presents a data-driven framework that integrates machine-learning-based diagnosis with gradient-based optimization for lightning protection of transmission corridors. Six standardized features — ground resistance, insulator-string length, tower height, protection angle, ground inclination, and ground-flash density — are normalized to construct tower state vectors for model training and inference. The diagnostic model outputs risk scores and cause attributions, identifying ground-flash density as the dominant hazard (27.9%), followed by excessive ground inclination, increased tower height, and shortened insulator strings that elevate wrap-around strike risk. Using these diagnostic results, a gradient-based optimizer minimizes state deviations within engineering constraints to generate actionable protection measures, including arrester configuration and parameter adjustments. In a 220-kV case study covering 39 towers, 30 satisfied the standard and 9 were recommended for arrester installation; for the high-risk Tower #034, the trip-out rate decreased to 1.632 times/(100 km⋅a). The proposed approach improves objectivity and efficiency relative to manual practice. Future work will incorporate cost weighting to enhance practical deployment.

Introduction: Sorafenib remains the only approved treatment for advanced hepatocellular carcinoma (HCC), yet its clinical use is hindered by toxicity and the emergence of drug resistance. Sorafenib's anticancer effects are largely attributed to its inhibition of multiple kinases, including c-Raf, a key player in the Ras-Raf-MEK-ERK signaling cascade that promotes cell growth and survival. Given the critical role of c-Raf in tumor progression, targeting this kinase offers a promising strategy for improving therapeutic outcomes. Developing new analogs with stronger c-Raf inhibition, better pharmacokinetics, and reduced side effects could help address the current limitations of sorafenib. Objectives: This study aimed to design novel sorafenib analogs with enhanced binding affinity and favorable pharmacokinetic profiles, specifically targeting the c-Raf kinase to increase therapeutic efficacy against HCC. By using a fragment replacement approach combined with computational methods, the goal was to identify candidates capable of forming stronger, more stable interactions with c-Raf, potentially overcoming resistance linked to sorafenib treatment. Methods: A total of 84 sorafenib analogs (A1-A84) were generated by modifying key functional groups, including the 2-picolinamide and substituted phenyl moieties known to influence kinase binding and anticancer activity. These analogs were evaluated through chemoinformatics and pharmacokinetic screening to assess their drug-likeness and safety. Molecular docking was performed to estimate their binding affinity toward c-Raf. Six top-performing analogs (A2, A6, A9, A20, A22, A63) were selected for further analysis. To evaluate their dynamic behavior, 100[Formula: see text]ns all-atom molecular dynamics simulations were conducted, followed by Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations to determine binding free energies. Principal component analysis (PCA) was carried out to explore key motion patterns within the protein-ligand complexes. Results: Molecular docking showed that the selected analogs exhibited stronger binding affinities (-11.6 to -10.9[Formula: see text]kcal/mol) compared to sorafenib (-9.3[Formula: see text]kcal/mol) and regorafenib (-9.5[Formula: see text]kcal/mol). Molecular dynamics simulations substantiated the docking results. MM-PBSA results revealed that at 100[Formula: see text]ns, the binding free energy for the c-Raf-sorafenib complex was 86.751[Formula: see text]kJ/mol, while the c-Raf complexes with A2, A6, A9, A20, A22, and A63 demonstrated significantly lower free energies of -129.114, -135.637, -136.242, -127.178, -94.25, and -123.176[Formula: see text]kJ/mol, respectively, indicating stronger and more stable binding. PCA further confirmed the stability and favorable dynamic profiles of these analogs trajectory with c-Raf. Discussion: The improved binding affinities and lower free energies of the top analogs indicate that specific structural changes to sorafenib can enhance its effectiveness against c-Raf. Molecular dynamics and MM-PBSA results suggest the stability and strength of these interactions, particularly for A2, A6, and A9. Conclusion: This study identified six promising sorafenib analogs with improved binding affinity, favorable pharmacokinetic characteristics, and stable interactions with c-Raf. By focusing on c-Raf inhibition, the combined use of computational modeling, molecular simulations and mmPBSA analysis provided valuable insights for drug design. Among the candidates, A2, A6, and A9 emerged as promising drug candidates for further development, supporting the potential of targeting c-Raf to enhance therapeutic strategies against HCC.

The study introduces cross-modal memory-enhanced fusion (CrossMF) as a unified transformer-based model which performs emotion recognition from speech and text data. CrossMF combines dynamic attention between modalities with a memory enhancement system for effective fusion between textual and acoustic information. The training of CrossMF involves a two-step tactic where the acoustic encoder receives clean audio inputs from the Toronto emotional speech set (TESS) for optimization, yet the text encoder and fusion module acquire training from audio-text pairs from the multimodal EmotionLines (MELD) dataset. The system allows emotion predictions from three combination types, including audio-only, text-only, and audio with text inputs, due to adjustable modality access. The evaluation process takes place across the three different input scenarios to show that the system performs well with high generalization capability in both laboratory-recorded and natural conversational conditions. Fusion between multiple sources only occurs when both inputs are available so that integrity can be maintained until integration occurs. The model reaches a maximum validation accuracy of 97.68% while demonstrating sustained high-test performance, which proves its effectiveness when operated in various conditions without depending on manually created features. The architecture also supports future extensions, allowing developers to easily incorporate ablation studies and adaptive training strategies for real-world emotion-aware systems.

Low frequency (10 Hz – 100 kHz) noise characteristics of GaAsBi quantum wells (QWs) based light-emitting diodes were investigated over the temperature range 159 K - 320 K. 1/f, 1/f α , and Lorentzian type components make up the low frequency noise spectra of the investigated structures. Noise spectroscopy revealed that the investigated GaAsBi QW structures, possibly due to lower growth temperature, contain defects that redistribute current out of the diode's active region.