The advancements in cloud computing platforms (such as the Google Earth Engine (GEE)) and the optical, radar, and thermal infrared (TIR) observations hosted on these platforms significantly enhance satellite-based digital mapping of soil organic carbon (SOC). However, the literature generally lacks consensus on the effects of different optical, TIR, and radar sensors, as well as radar sensor configurations, on the digital mapping of SOC. In this study, we aimed to investigate the effects of these three satellite sensors and radar sensor configurations on the digital mapping of SOC at two soil depths (0–20 and 20–100 cm) on the continental (Asian) scale using long-term satellite observation data from the GEE. Models to predict SOC at two depths were developed by constructing eleven prediction scenarios with different data configurations using long-term optical (MODIS), thermal (Landsat-8), and radar (C-band Sentinel-1 and L-band PALSAR-1/2) observations combined with the World Soil Information Service (WoSIS) soil profile database and two machine learning methods, with performance evaluated through cross-validation. The results indicated that the SOC predictions at both depths were influenced by the machine learning algorithm, sensor type, and radar sensor configuration. The model trained using single-polarization data yielded the poorest performance, whereas cross-polarization outperformed copolarization for radar data under both bands; incorporating backscatter information obtained from more polarization modes and operating frequencies into the model improved the predictions. Specifically, dual‐band integration increased R² from 0.41 to 0.46 (0–20 cm) and 0.36–0.40 (20–100 cm) compared to L -band data alone. When training models independently using optical, TIR, and radar data, the radar-based model performed the worst, whereas the models based on TIR and optical data exhibited the best performance in terms of SOC prediction at depths of 0–20 cm (R² = 0.56) and 20–100 cm (R² = 0.46), respectively. Compared with the models developed with only one or two imaging techniques, the models developed with all three imaging techniques performed best across all the scenarios, obviously improving the predictions, and explained approximately 62 % of the SOC variability at the 0–20 cm depth and 53 % at the 20–100 cm depth. Importance analysis indicated that long-term optical, TIR, and radar observations were important in our models at different depths. The maps predicted by the three imaging techniques, which varied between depths and exhibited high spatial heterogeneity, were broadly similar in large-scale spatial gradients but differed mainly in fine-scale spatial detail and predicted magnitude. • Long-term optical, radar, and thermal imagery were important for models at different depths. • The modeling output of SOC was affected by the prediction method, sensor type, and sensor parameters. • Incorporating backscatter information obtained at more polarizations and frequencies improved the predictions. • The model developed using the three imaging techniques performed best. • The GEE platform can effectively integrate optical-radar-thermal data to support continental-scale SOC mapping.

Abstract The system design of hypersonic vehicles is a typical multidisciplinary design optimization (MDO) problem. To address the challenge of hypersonic vehicles multidisciplinary design and optimization, a Multidisciplinary Design Optimization System for Hypersonic Vehicles (MDOS-HV) is developed. MDOS-HV adopts a five-layer client/server (C/S) architecture and integrates scalable modules for disciplinary simulation, algorithm configuration, optimization execution, and interactive results visualization. Moreover, a metamodel-based multi-objective optimizer is integrated into MDOS-HV to reduce the optimization cost. Then a practical hypersonic vehicle MDO example is investigated. For the studied problem, the range and heat flux are optimized simultaneously subject to engineering constraints to improve the flight performance of the hypersonic vehicle, where the geometry, aerodynamic, thermal, and trajectory disciplines are considered. With only 200 evaluations of multidisciplinary analysis process, MDOS-HV successfully obtains ten non-dominated solutions. Compared with the initial design, the optimized range is increased by 15.1% with lower stagnation-point heat flux. The results demonstrate the effectiveness and practicality of the developed MDOS-HV, which an open template and reusable workflow for real-world hypersonic vehicles system design.

The paper proposes a formal model for the adaptive selection of cryptographic parameters for protecting communication channels in corporate computer networks based on dynamic trust assessment and integrated risk. The relevance of the study stems from the fact that common practices of static configuration of encryption algorithms, modes of operation, and cryptographic strength parameters do not account for changes in access context and the behavior of interacting entities, which leads either to excessive computational overhead or to the emergence of vulnerability windows during threat escalation. The scientific novelty lies in interpreting the cryptographic profile as a controllable dynamic state of the security system, where trust acts as a direct control parameter of the cryptographic configuration rather than merely a factor in access decision-making. A protected channel is formalized as a state tuple combining the subject, resource, context, trust level, risk, and cryptographic profile, while adaptive parameter selection is described by a mapping that establishes a correspondence between (resource criticality, context) and a set of cryptographic characteristics (algorithm, mode, strength parameter, session lifetime). An optimization formulation for profile selection is developed that accounts for the trade-off between cryptographic strength and operational costs, along with an event-driven mechanism for updating the cryptographic state (Rekey/Upgrade/Revoke) in response to trust degradation, risk increase, or critical security events. Scenario analysis (normal operation, contextual/behavioral anomaly, critical event) demonstrates the model’s ability to coherently enhance strength and reduce cryptographic session lifetimes in high-risk situations, thereby reducing the potential attack window while maintaining acceptable performance under low-risk conditions. The obtained results provide a theoretical foundation for deploying adaptive cryptographic profiles in TLS/VPN and Zero Trust–oriented corporate environments.

Quantum Software Engineering (QSE) is emerging as a critical discipline to make quantum computing accessible to a broader developer community; however, most quantum development environments still require developers to engage with low-level details across the software stack—including problem encoding, circuit construction, algorithm configuration, hardware selection, and result interpretation—making them difficult for classical software engineers to use. To bridge this gap, we present C2 \(\lvert Q \rangle\) , a hardware-agnostic quantum software development framework that translates specific types of classical specifications into quantum-executable programs while preserving methodological rigor. The framework applies modular software engineering principles by classifying the workflow into three core modules: an encoder that classifies problems, produces Quantum-Compatible Formats (QCFs), and constructs quantum circuits, a deployment module that generates circuits and recommends hardware based on fidelity, runtime, and cost, and a decoder that interprets quantum outputs into classical solutions. This architecture supports systematic evaluation across simulators and Noisy Intermediate-Scale Quantum (NISQ) quantum devices, remaining scalable to new problem classes and algorithms. In evaluation, the encoder module achieved a 93.8% completion rate, the hardware recommendation module consistently selected the appropriate quantum devices for workloads scaling up to 56 qubits. End-to-end experiments on 434 Python programs and 100 JSON problem instances demonstrate that the full C2 \(\lvert Q \rangle\) workflow executes reliably on simulators and can be deployed successfully on representative real quantum hardware, with empirical runs limited to small- and medium-sized instances consistent with current NISQ capabilities. A proxy-based usability analysis further indicates substantial reductions in handwritten lines of code and explicit configuration steps compared to conventional quantum SDK workflows. These results indicate that C2 \(\lvert Q \rangle\) lowers the entry barrier to quantum software development by providing a reproducible, extensible toolchain that connects classical specifications to quantum execution. The open-source implementation of C2 \(\lvert Q \rangle\) is available at https://github.com/C2-Q/C2Q and as a ready-to-use Python package at https://pypi.org/project/c2q-framework/ .

Reservoir water level forecasting is a critical component of effective water resources management, supporting flood mitigation, water supply planning, and sustainable reservoir operation, particularly under increasingly variable rainfall conditions. During periods of heavy rainfall, inaccurate or delayed water level prediction may increase flood risk, while during low rainfall seasons, poor forecasting can compromise water storage and operational efficiency. Artificial Neural Networks (ANNs) have been widely adopted for reservoir water level forecasting due to their capability to model nonlinear rainfall–reservoir relationships. However, existing studies largely focus on algorithm selection or architectural enhancement, with limited attention given to how rainfall data representation and dataset construction influence neural network performance. This study addresses this gap by analysing the impact of rainfall pattern dataset construction on ANN performance for reservoir water level forecasting. The primary aim is to evaluate how different rainfall representations affect predictive accuracy when the learning algorithm and training configuration are held constant. Two rainfall pattern datasets were constructed using the same raw rainfall and reservoir water level data from the Timah Tasoh Reservoir, Malaysia. The first dataset represents a compact abstraction of rainfall behaviour using rainfall change indicators derived from day-to-day observations. The second dataset enriches the feature space by incorporating both rainfall change and rainfall intensity categories for each upstream station. In both datasets, the reservoir water level category serves as the prediction target. Prior to model training, redundancy and conflicting data instances were removed to ensure data consistency. A consistent ANN architecture was employed for both datasets and evaluated using 10-fold cross-validation. Model performance was assessed using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The experimental results demonstrate that the enriched rainfall pattern dataset achieved significantly lower RMSE and MAE values compared to the compact rainfall change dataset, indicating improved learning capability and generalisation performance. Although the enriched dataset required higher computational effort, the improvement in forecasting accuracy was substantial. The findings highlight that dataset construction plays a decisive role in neural-network-based reservoir water level forecasting.

Dear Editor, We read with great interest the recent publication by Dai et al entitled “A Multi-Omics Pipeline Integrating Machine Learning and Spatial-Cellular Analysis Identifies SASH1 as a Prognostic Biomarker and Therapeutic Target in Head and Neck Squamous Cell Carcinoma”[1]. The study presents a sophisticated integration of multi-omics data, machine learning algorithms, and spatial transcriptomic profiling to identify potential prognostic biomarkers in head and neck squamous cell carcinoma (HNSCC). Its comprehensive analytical framework – spanning computational discovery to cellular and protein-level validation – demonstrates a strong contribution to the advancement of precision oncology. While the study provides valuable insights, several methodological and interpretative aspects warrant further discussion and clarification. First, the study utilized four machine learning algorithms – LASSO, SVM-RFE, XGBoost, and Boruta – to identify key predictive genes. While such an ensemble strategy may enhance model robustness, this advantage could also introduce algorithm-selection bias. Given the wide range of available approaches, including Elastic-Net regression[2], Random Forests, Gradient Boosting Machines[3], and deep neural networks[4], the rationale for selecting only these four algorithms remains insufficiently justified. Each algorithm is based on distinct assumptions regarding linearity, feature interdependence, and noise tolerance. Without a systematic rationale or stability analysis across alternative models, there is a potential risk that SASH1 was highlighted as a consequence of this specific algorithmic configuration rather than reflecting its intrinsic biological significance. Second, the study provides limited comparative evaluation of model performance and interpretability. Specifically, presenting metrics such as classification accuracy, area under the curve values, or the consistency of feature importance across models would have enhanced methodological transparency and reproducibility. Furthermore, model interpretability – particularly the biological reasoning underlying algorithm-derived feature rankings – was not sufficiently addressed. Incorporating explainable AI techniques, such as SHAP analysis[5], could have elucidated how SASH1 contributes to the model’s decision-making process and whether its predictive relevance extends beyond mere statistical association. Third, although the inclusion of single-cell and spatial transcriptomic analyses is technically valuable, several important limitations should be acknowledged. The spatial data were generated using the 10× Genomics Visium platform, which features a spot diameter of approximately 55 μm[6], thereby capturing transcripts from multiple adjacent cells. This constraint inherently limits single-cell resolution and may introduce signal contamination among tumor, stromal, and immune compartments. As a result, the spatial maps exhibit extensive overlap and color blending of annotated cell types – particularly within the tumor core – making the deconvolution results difficult to interpret. Moreover, the excessive overlay of text labels further compromises figure readability. Reorganizing these plots – for instance, by grouping related cell types, simplifying legends, and overlaying gene-expression heatmaps delineated by clear tissue boundaries – could substantially improve interpretability. Fourth, at the single-cell level, SASH1 expression was predominantly detected in nonmalignant fibroblast and endothelial populations rather than in malignant epithelial clusters. This pattern suggests that if SASH1 is not tumor-cell-specific, its prognostic significance may derive from stromal regulation rather than intrinsic tumor suppression. Clarifying whether SASH1 functions primarily within cancer cells or in the surrounding microenvironment is critical to establishing its translational relevance. Finally, in terms of experimental validation, the verification was limited to Western blot analysis of SASH1 expression. Although the observed protein-level downregulation supports the computational findings, no functional assays were conducted to demonstrate a causal link between SASH1 depletion and malignant behaviors, including proliferation, migration, and epithelial–mesenchymal transition. In summary, this study represents a meaningful step toward biomarker discovery in HNSCC. Moving forward, clarifying the algorithmic rationale, enhancing model interpretability, improving the visualization of spatial data, as well as experimentally validating the functional mechanisms, could further enhance the methodological rigor and translational relevance of the findings.

ABSTRACT To tackle the significant challenge of achieving the frequency domain and time domain resource allocation on the Geostationary Earth Orbit (GEO) satellite return link, we develop a two‐tier “Carrier Planning‐Timeslot Allocation” cooperative resource‐management framework to optimize system efficiency and different quality of service (QoS) for diverse user demands simultaneously. The Dynamic Planning Carrier Configuration (DPCC) algorithm continuously reconfigures the frequency carrier set through a multi‐layer aggregation and proportional allocation procedure, in which the share granted to each carrier tier is steered by the weighted traffic demand of every priority class and the prevailing channel fading severity. The Timeslot Allocation under Fixed Carriers Set (TAFCS) algorithm initially utilizes a modulation and coding (MODCOD) update mechanism to exploit favorable channel conditions. Then it conducts scheduling in three successive phases “priority‐driven allocation, idle‐slot aggregation and reuse, and residual‐request slicing”. Extensive Satellite Network Simulator 3 simulations show that TAFCS alone outperforms the first‐fit algorithm and the Reserve Channel with Priority (RCP)‐fit algorithm, boosting timeslot utilization and return channel satellite terminal (RCST) response rates by roughly 8%–11% while meeting QoS targets across high‐/medium‐/low‐priority traffic. When DPCC is introduced, the simulation under seven scenarios with diverse traffic and channel dynamics confirms that the DPCC‐TAFCS framework markedly surpasses single‐layer TAFCS on both system efficiency and QoS support, while demonstrating robust effectiveness under harsh link conditions.

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study.

In injection molding, advanced numerical modeling tools, such as Moldex3D, can significantly improve product development by optimizing part functionality, structural integrity, and material efficiency. However, the complex and nonlinear interdependencies between the several decision variables and objectives, considering the various operational phases, constitute a challenge to the inherent complexity of injection molding processes. This complexity often exceeds the capacity of conventional optimization methods, necessitating more sophisticated analytical approaches. Consequently, this research aims to evaluate the potential of integrating intelligent algorithms, specifically the selection of objectives using Principal Component Analysis and Mutual Information/Clustering, metamodels using Artificial Neural Networks, and optimization using Multi-Objective Evolutionary Algorithms, to manage and solve complex, real-world injection molding problems effectively. Using surrogate modeling to reduce computational costs, the study systematically investigates multiple methodological approaches, algorithmic configurations, and parameter-tuning strategies to enhance the robustness and reliability of predictive and optimization outcomes. The research results highlight the significant potential of data-mining methodologies, demonstrating their ability to capture and model complex relationships among variables accurately and to optimize conflicting objectives efficiently. In due course, the enhanced capabilities provided by these integrated data-mining techniques result in substantial improvements in mold design, process efficiency, product quality, and overall economic viability within the injection molding industry.

Particle Swarm Optimization in Medical Applications: A Systematic Review of Data Dimension Reduction and Algorithmic Configuration

On the inherent robustness of one-stage object detection against out-of-distribution data.

Abstract Mobile robots face significant challenges in obstacle detection and safe navigation in populated indoor environments. While commonly used, traditional sensors such as a depth camera and a 2D LiDAR exhibit limitations in handling dynamic objects and complex scenarios. Additionally, most current perception pipelines rely on discrete sensors and rarely integrate motion prediction or object tracking, leaving planners blind to rapidly changing obstacles. To address these issues, this study introduces an Obstacle Detection and Tracking Framework that integrates state-of-the-art object detection algorithms—including YOLOv11, MobileNet-SSD and EfficientDet-D0—with multi-object tracking using both DeepSORT and ByteTrack. This tracking information is proactively incorporated into the robot’s occupancy grid through the occupancy grid generation module, where regions predicted to be occupied by moving obstacles are dynamically assigned higher costs. Detected and tracked objects are projected onto a 2D bird’s eye view using depth data, providing spatial and temporal awareness of the environment. To enhance path planning, we add a sensor-fused trajectory predictor. For each tracked object, RGB-D and 2D LiDAR are fused via a Kalman filter to estimate position, velocity of objects for path prediction. This framework bridges the gap between perception and planning systems and provides actionable insights for selecting suitable algorithmic configurations in real-world robot deployment scenarios.

Machine learning (ML)-enabled high-throughput screening to predict potential electrocatalysts for the CO2 reduction reaction (CO2RR) offers new insights for energy conversion and environmental remediation. In this work, for the first time, we established a comprehensive electrocatalytic database containing ≈400 entries of CO2RR catalysts. Through decision tree analysis, correlation heatmaps, and feature importance ranking, we systematically decoded structure-property relationships. Among the tested algorithms, the nonlinear tree-ensemble method Random Forest Regression demonstrated superior predictive performance for CO2RR systems. Subsequent screening of 500 000 catalyst configurations generated by the the sequential model-based algorithm configuration method, using Expected Improvement as the evaluation metric, identified promising multinary alloy catalysts for C1 molecule production. Notably, BiSb-based alloys emerged as high-potential candidates for CO2RR applications. This ML-driven paradigm highlights the growing significance of artificial intelligence in materials discovery, synergistically combining screening efficiency, prediction accuracy, and proficiency in big data processing.

Research on deep learning for medical image segmentation has shifted from single-modality networks to multimodal data fusion. Updating the parameters of such deep learning models is crucial for accurate segmentation predictions. Although existing optimizers can perform global parameter updates, the fine-grained initialization of learning rates across different network hierarchies and its influence on segmentation performance has not been sufficiently explored. To address this, we conducted a series of experiments showing that the initialization of a differentiated learning rate across network layers directly affected the performance of medical image segmentation models. To determine the optimal initial learning rate for each network level, we summarized a general statistical relationship between early-stage training results and the model's final optimal performance. In this paper, we proposed a fine-grained learning rate configuration algorithm. To verify the effectiveness of the proposed algorithm, we evaluated 10 segmentation models on three benchmark datasets: the colon polyp segmentation dataset CVC-ClinicDB, the gastrointestinal polyp dataset Kvasir-SEG, and the breast tumor segmentation dataset BUSI. The models that achieved the most significant improvement in mIoU on these three datasets were H-vmunet, MSRUNet, and H-vmunet, with increases of 3.87%, 4.67%, and 6.22%, respectively. Additionally, we validated the generalization and transferability of the proposed algorithm using a thyroid nodule segmentation dataset and a skin lesion segmentation dataset. Finally, a series of analyses, including segmentation result analysis, feature map visualization, training process analysis, computational overhead analysis, and clinical relevance analysis, confirmed the effectiveness of the proposed method. The core code is publicly available athttps://github.com/Lambda-Wave/PaperCoreCode.

This study establishes a comprehensive structural isomorphism between Conway’s Game of Life and the entrepreneurial process, analysing the latter as a complex adaptive system governed by non-linear dynamics rather than linear predictability. Through a rigorous qualitative approach based on a systematic literature review and abductive inference, the research identifies and correlates four fundamental dimensions: uncertainty, adaptability, growth, and sustainability. Transcending traditional metaphorical comparisons, this paper introduces a novel mathematical model that modifies Conway’s deterministic logic by incorporating an «Agency» variable (A). This critical addition quantifies how an entrepreneur’s internal capabilities can counterbalance environmental pressures (neighbourhood density) to determine survival thresholds, effectively transforming the simulation into a «Game of Life with Agency» where participants actively influence their viability potential (Ψ). The analysis explicitly correlates specific algorithmic configurations with real-world business phenomena: high-entropy initial states («The Soup») mirror early-stage market uncertainty where outcomes are probabilistic; «gliders» represent the necessity of strategic pivoting and continuous displacement for survival; and «oscillators» symbolise dynamic sustainability through rhythmic equilibrium rather than static permanence. Furthermore, the study validates the «Gosper Glider Gun» pattern as a model for scalable, generative growth. By bridging abstract systems theory with managerial practice, the research positions these simulations as «mental laboratories» for decision-making. The findings theoretically validate iterative methodologies like the Lean Startup and conclude that successful entrepreneurship operates on the «Edge of Chaos», providing a rigorous framework for navigating high stochastic uncertainty.

Assessing reclamation potential of abandoned drylands using knowledge-guided machine learning (KGML) and remote sensing.

This study introduces a magnetometer-free, custom-built inertial measurement unit (IMU) combined with an opensource sensor-fusion algorithm and provides a systematic experimental comparison against commercial IMUs that integrate proprietary hardware and closed-source filtering software. The main innovation lies in demonstrating that an open and fully reproducible hardware and software architecture can achieve orientation estimation accuracy comparable to that of state-of- the-art commercial solutions under both static and dynamic conditions. Experimental results show that the proposed system achieves a mean orientation error of 2.10% (0.11°), while consistently outperforming proprietary filtering approaches in static scenarios and exhibiting competitive variability during dynamic motion. Furthermore, the study presents a comprehensive benchmarking framework that analyzes the influence of sensor noise characteristics and filtering strategy on estimation performance across multiple hardware and algorithm configurations. These findings establish that custom-built IMUs paired with open-source sensor-fusion algorithms constitute a reliable, cost-effective, and transparent alternative to commercial hardware and software solutions for biomedical and engineering applications.

This research investigates the secrecy performance and energy efficiency of a dual-unmanned aerial vehicle (UAV) computation offloading system that leverages uplink non-orthogonal multiple access (NOMA) designed for Internet of Things (IoT) networks operating under Nakagami-m fading propagation. Specifically, a multiantenna UAV-enabled mobile-edge computing (MEC) server offers proximate computation services to clusters of resource-constrained edge devices (EDs), while utilizing selection combining (SC) and maximal ratio combining (MRC) schemes to enhance the offloading reliability. Simultaneously, a secondary UAV is deployed as a dedicated aerial friendly jammer (FJ) to fortify data confidentiality against eavesdropping threats. Accordingly, the systems secrecy computation offloading performance is evaluated through a novel closed-form formulation termed secrecy successful computation probability (SSCP). This metric uniquely incorporates the detrimental effects of imperfect channel state information (iCSI) and imperfect successive interference cancellation (iSIC), both of which are critical real-world factors that reduce the effective signal-to-interference-plus-noise ratio (SINR) of legitimate links at UAV-MEC. This reduction in SINR directly leads to a significant decrease in the SSCP and overall reliability of the secure offloading process, due to the impaired ability to successfully decode information at the legitimate UAV. Building upon the SSCP derivation, the secrecy energy efficiency (SEE) metric is adopted to analyze the fundamental trade-off between security and energy. Furthermore, the problem of jointly enhancing computation offloading security and energy efficiency is formulated as a SEE maximization problem under the constraints of dual-UAV altitudes and positions, the task-offloading ratio (TOR), the power-allocation ratio (PAR), and the number of UAV-MEC antennas. Hence, an optimization approach grounded on the Ananya algorithm is employed, as its superiority is validated against the benchmark particle swarm optimization (PSO) algorithm and baseline arbitrary configurations, achieving a significantly higher SEE solution while converging over 35% faster. Ultimately, the accuracy and feasibility of the proposed system model are consistently verified through extensive numerical simulations of sixth-generation (6G)-envisioned conditions, accounting for a variety of key system parameters.

Optical communications provide high capacity and scalability, while quantum communications, particularly through quantum key distribution (QKD), enable information-theoretic security. However, it still remains challenging to deliver both high throughput communication and long-term security within a hybrid quantum-classical optical network. Specifically, classical optical resources can be reallocated to support QKD, aiming to achieve synergistic resource utilization that surpasses the sum of individual functionalities. In this work, we formulate a networking model that captures the resource allocation constraints, aiming to figure out the necessary conversion of classical optical resources to QKD capacity while maintain the connectivity of classical optical communication. Based on that, we propose a synergistic resource allocation strategy, implemented through heuristic routing, wavelength assignment, and power configuration (SRWP) algorithms. Simulation results demonstrate that the proposed SRWP algorithm, with the objective of network-wide average secret key rate (SKR) maximization (SRWP-ARM), enhances the average SKR from the order of kbit/s to Mbit/s at GHz-level pulse repetition rates. In a 9-node mesh network topology with 50 km links, the algorithm achieves this gain with zero service blocking under a traffic load of 100 erlangs. At a higher load of 300 erlangs, it maintains a service blocking probability as low as 1.24% while still achieving an average SKR of 18.05 kbit/s. This represents the best trade-off between classical service connectivity and secret key generation across all evaluated algorithms.

The methodology proposes an approach to the adaptive renovation of centrifugal multicyclone separators during declining production, without capital expenditure or hot work. The problem is demonstrated to be relevant, driven by the mismatch between gas-dynamic regimes and apparatus geometry, the growth of liquid carryover, and the risk of catastrophic failure of compressor equipment. The objective of the study is to develop and verify a computational–experimental procedure for selecting the active separation area by selectively plugging part of the centrifugal elements to maintain the operating point within the zone of maximum efficiency. The scientific novelty lies in introducing the efficiency triangle concept (velocity–pressure–design), using modified similarity correlations to determine critical velocities, and combining isokinetic probing with algorithmic tray configuration, implemented as a Python module. It is shown that application of the methodology makes it possible to restore the regulatory level of carry-over (<5 mg/m³), extend the service life of separation equipment, and reduce total costs by eliminating the need for separator replacement, with the intervention cost being less than 1 % of the price of a new separator. The methodology is intended for engineering and technical personnel at gas production and gas processing enterprises, as well as for design and service organizations involved in modernizing integrated gas treatment units.