Search Results (984)

Network function virtualization (NFV) enables cost reduction and optimized service deployment. By means of virtualization, network functions which used to be executed on specialized hardware are being replaced with software called Virtual Network Functions (VNFs) that can run on commodity hardware. These VNFs are applied to data flows passing through network nodes with VNFs hosted on them. To fully realize the benefits of NFV, each flow must be fully processed on VNF nodes. Given the budget constraints, only a finite number of nodes can be selected to host VNFs, and these nodes also have limited capacity to process the flows passing through them. In this paper, we consider the problem of VNF node placement and capacity allocation in a network graph $G=(V,E)$, i.e., selecting the best subset of VNF nodes and optimally distributing their bandwidth to maximize the total volume of fully processed traffic flows F. We propose a simpler algorithm for solving this problem than the previously proposed version, representing it as an integer linear programming problem with an approximation ratio of ${\displaystyle \frac{1}{2}}(1-1/e)$, and time complexity $O\left(\right|V{|}^{2.5}·\left|F{|}^{2.5}·L\right)$, where L is the number of bits of input data. Full article

The selective conversion of high-molecular-weight paraffins (C₂₀–C₄₀) into linear alpha-olefins is often hindered by severe diffusion limitations and secondary over-cracking. This study addresses these challenges by transforming low-value natural minerals into sophisticated catalytic systems. We present a “top-down” engineering strategy for designing hierarchical catalysts based on natural Kazakhstani clinoptilolite. The multi-stage modification involves synergistic demineralization and precision chelation (EDTA, sulfosalicylic acid) to generate a tailored mesoporous architecture. This framework serves as a host for the sub-nanometric immobilization of Keggin-type heteropolyacids (PW₁₂, PMo₁₂), ensuring optimal active-phase dispersion. The innovative dual-step modification successfully bypassed the “micropore barrier”, creating a high-surface-area hierarchical network that facilitates the transport of bulky paraffinic molecules. Precise localization of heteropolyacid clusters within the created mesopores resulted in the formation of superstrong Lewis acid sites, as confirmed via temperature-programmed ammonia desorption. These sites triggered a highly efficient monomolecular beta-scission mechanism, suppressing undesirable hydrogen transfer reactions. The resulting catalysts achieved a breakthrough in technical paraffin cracking, delivering a 70% liquid product yield with an unprecedented >50% selectivity toward the C₇–C₁₄ α-olefin fraction. This work demonstrates a sustainable pathway for upgrading natural zeolites into high-performance, green catalysts that rival expensive analogs in precision and efficiency. Full article

Hierarchical cloud–edge networks rely on distributed control planes to manage large-scale heterogeneous infrastructures, where controller placement and node assignment strongly affect latency, load balancing, and resilience. Existing methods typically decouple these decisions and provide limited guarantees under controller failures or topology constraints. We introduce a topology-aware joint optimization framework for controller placement and node assignment in hierarchical cloud–edge networks. The problem is formulated as a multi-objective integer linear program capturing latency, load balancing, and control continuity. To ensure scalability, we design a two-phase heuristic: structurally important controller candidates are selected using graph-based metrics, including node degree and k-core decomposition, followed by a redundancy-aware proximity assignment strategy that preserves connectivity under single-controller failures. Experiments on synthetic hierarchical and random topologies with up to 500 nodes show that the proposed approach achieves optimality gaps below 10% with execution times under 10 ms. It improves load distribution and reduces control latency compared to baseline methods while maintaining resilience under controller failures. Results show that exploiting topological structure in joint placement and assignment enables efficient and resilient control plane design for hierarchical cloud–edge networks, supporting near-real-time reconfiguration. Full article

Selecting a compact subgraph of a document graph while maximising a learned coherence function, subject to flow conservation and temporal ordering, is important in storyline detection, event threading, and Narrative Map extraction. Existing Narrative Map methods either recover a single optimal path (a Narrative Trail) or solve a linear program with an output size which grows with graph density (Narrative Maps). We propose a hybrid classical–quantum pipeline that casts the problem as a Quadratic Unconstrained Binary Optimisation (QUBO) problem and solves it both with the Quantum Approximate Optimisation Algorithm (QAOA) and with off-the-shelf classical QUBO solvers (simulated annealing, Tabu search) on the same Hamiltonian; this approach uses a classical mean field active space reduction and Matrix Product State tensor network simulation to scale beyond 16 qubits. We evaluate node- and edge-level qubit encodings under a range of QAOA circuit variants (transverse field and XY mixers; classical warm-start deeper circuits) on a 418-document news corpus across four graph densities and ten endpoint pairs, and audit their reproducibility across optimiser seeds. The QUBO formulations—whether solved by QAOA or by classical QUBO solvers on the same Hamiltonian—produce maps averaging $4.7$–$9.0$ nodes versus $26.6$ for Narrative Maps ($p<{10}^{-7}$) and they are far more focused on their main storyline (main path fraction $0.61$–$0.99$ versus $0.20$). The Hamming-weight-preserving XY mixer goes the furthest: the node-level XY mixer variant produces the most compact ($4.7$ nodes) and most spine-focused ($0.99$ main path fraction) maps of any method tested, and a multi-seed audit identifies it as the most reproducible of the eight QAOA variants we compared. Main path coherence is on par with Narrative Maps’ and $0.031$–$0.072$ below the bottleneck-optimising baselines—Narrative Trails ($0.770$) and Iterative Maximin ($0.758$). These results position QAOA not as a uniformly stronger alternative but as a distinct trade-off region favouring compactness and spine focus over raw bottleneck coherence and corpus topic breadth. Full article

This narrative review develops fermentation-oriented viticulture as an agronomic-oenological framework linking vineyard environment, management and must ecology to fermentation performance. The literature from 2010 to April 2026 was synthesized through structured searches in PubMed and Google Scholar, complemented by targeted searches in MDPI, Frontiers, Nature, ScienceDirect, OENO One, PNAS and European Union regulatory sources, with emphasis on 2020–2026 publications and retention of older foundational sources. Current evidence indicates that must microbiota is not a linear derivative of soil or berry surfaces, but a network outcome of connected habitats spanning the viticultural biotope and grapevine-associated biocenosis (soil, rhizosphere, phyllosphere, berry, insect, atmospheric and winery). Climate warming, drought, altered phenology, soil fertility, nitrogen nutrition, crop-protection programs and bio-based inputs jointly modify berry chemistry, yeast-assimilable nitrogen (YAN), microbial inocula and pre-fermentative selection pressures. The review distinguishes fermentation-oriented viticulture from descriptive microbial terroir by defining practical endpoints: fermentation onset and completion, sluggish or stuck fermentation risk, microbial stability, spoilage taxa, volatilome development and wine typicity. It also proposes operational indicators and a decision matrix for integrating vineyard and winery management. The framework supports future multi-vintage studies combining climate, soil, agronomic metadata, YAN, microbiome profiling and microvinification outcomes. Full article

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. Full article

To enhance the overall capacity utilization of HSR networks and promote balanced capacity utilization across different lines, this study considers optimizing the capacity utilization of HSR networks by adjusting the travel routes of direct trains. Based on the arc-path modeling framework for multi-commodity network flows, the problem was formulated as a dual-objective mixed-integer linear programming model to minimize the total travel time and enhance the balance of capacity utilization across the railway network, with consideration of the unique train routes, the matching of paired train routes, section capacity, operating mileage, and maximum operating time per train trip limitations. Then, the model was transformed into a single-objective function using the weighted-sum approach. A case study based on actual data from China’s HSR network and train line plans in early 2024 was conducted to demonstrate the effectiveness of the proposed method. The results show that the proposed method can control the total travel time while significantly reducing the number of sections with over-utilized capacity and improve the balance of railway network capacity utilization. The method can thus provide decision support for the efficient utilization of HSR network capacity. Full article

After a severe outage occurs, restoring a distribution network can take from several hours to days, making the secure and stable operation of restoration areas (RAs) critical. During a post-disaster partitioned operation, asymmetric controllable distributed generator (CDG) regulation capacity, non-controllable distributed generator (NDG) fluctuation risks, and concentrated high-value loads cause significant inter-area power imbalances. Soft open points bridge this resource gap by integrating electric vehicle charging directly into soft open points via vehicle-to-grid (V2G) technology; the resulting electric vehicle-embedded soft open points (EV-SOPs) acquire storage-like energy transfer capability. This paper proposes a boundary–node coordinated optimization strategy for post-disaster RA operation, which integrates CDGs, NDGs, smart switches, and EV-SOPs. Firstly, the boundary dynamic updating model with a multi-homogeneity indicator—load importance, NDG fluctuation risk, and CDG flexibility—enables adaptive resource allocation. Secondly, the optimal operational model of RA is formulated considering the various characteristics of facilities and topology constraints. Thirdly, EV-SOP uncertainties in response reliability, discharge power, and energy capacity are characterized by Bernoulli, log-normal, and truncated normal distributions, reformulated into a tractable mixed-integer quadratically constrained programming via chance-constraint interval linear transformation, and solved by a sequential weight-based priority search with hot-start strategy. Case studies on the IEEE 123-bus system verify the effectiveness of the proposed method. Specifically, the dynamic boundary strategy reduces the comprehensive weighted index by up to 29.10%; physical feasibility truncation reduces EV-driven load loss from 3.2073 MW to 3.1038 MW; and the sequential weight-based priority search with hot-start strategy achieves a cone constraint satisfaction measure of 9.3175 × 10⁻⁷, confirming robust convergence. Full article

Multi-Stage Transmission Network Expansion Planning (MS-TNEP) is critical for adapting power grids to long-term renewable integration. However, the simultaneous incorporation of N-1 security, active power losses, and uncertainties regarding the spatial and temporal growth of power generation capacity imposes prohibitive computational complexity. This paper proposes a probabilistic MS-TNEP model evaluated over a 20-year horizon. To overcome this computational intractability, a hybrid decomposition framework is employed. The investment subproblem determines the discrete decisions for network investment via a metaheuristic, while the probabilistic operation subproblem utilizes linear programming to assess the operational feasibility of these decisions under multiple spatial and temporal growth of power generation capacity scenarios, active power losses, and N-1 contingencies. Furthermore, a novel Pack-Based Grey Wolf Optimizer (PBGWO) is introduced. The approach is validated on the Garver and the Southern Brazilian equivalent systems under multiple scenarios for the growth of both wind and conventional power generation capacity. Comparative analysis against the Genetic Algorithm, the standard Grey Wolf Optimizer, and the Whale Optimization Algorithm reveals that PBGWO is a highly competitive approach for MS-TNEP problems, consistently identifying the most cost-effective expansion plan. Full article

This paper presents a behavioral-level, pre-silicon analytical co-design framework for memristor read–write interfaces, intended to establish closed-form design rules that subsequently guide SPICE-level and silicon-level realizations. Memristor-based neural hardware requires interfaces that can program resistance states efficiently while suppressing read disturbance, yet existing designs typically rely on empirical tuning without closed-form analytical rules. We close this gap by deriving a single closed-form operating-window inequality $(v_{\mathrm{on}}<V_{\mathrm{rd}}<v_{\mathrm{off}},V_{\mathrm{wr}}\ge V_{\mathrm{wr}}^{min}\left(T_{\mathrm{wr}}\right))$ from the VTEAM state equation, embedding it in an Energy–Delay–Accuracy (EDA) cost function, and validating the resulting parameter set hierarchically up to MNIST-scale inference. The main finding is that this analytically derived parameter set simultaneously achieves a 96.08% set-cycle energy saving and 90.6% MNIST top-1 accuracy (1.2% below software baseline) under realistic D2D/C2C variability, with every measured number agreeing with its analytical prediction within 2%. The framework is instantiated with a two-phase over-threshold-write and sub-threshold-read timing strategy together with a mutually exclusive PMOS-NMOS path-isolation topology, evaluated through behavioral-level MATLAB simulation under linear-drift and VTEAM models. Behavioral simulation confirms each analytical bound within 2%: a 13.78× resistance window with $\le 0.008\%$ cycle-to-cycle drift, 5.01% read-current CV, and 30.94%/96.08% Reset/Set energy savings versus a no-separation baseline. Transistor-level non-idealities (slew rate, charge injection, RTN, retention aging, peripheral overhead) are bounded analytically; full SPICE/silicon validation is identified as immediate follow-up work. These results establish a reusable, analytically grounded reference design that bridges memristive device modeling, circuit-level interface implementation, and neural network-level usability. Full article

Skeletal muscle satellite cells (SMSCs) are essential for embryonic myogenesis and postnatal muscle regeneration; however, their cellular heterogeneity and transcriptional dynamics during avian development remain largely unexplored. Here, we performed single-cell RNA sequencing (scRNA-seq) on 42,886 cells isolated from goose leg muscles across four embryonic stages (E13, E15, E18, and E23), with each stage comprising pooled tissues from four female embryos. Unbiased clustering resolved 22 transcriptionally distinct clusters representing six major cell types—satellite cells, myocytes, fibro-adipogenic progenitors, endothelial cells, immune cells, and Schwann cells—with satellite cells being the most abundant. Satellite cells were further subdivided into three functional states (quiescent, activated, and proliferative/differentiating), which followed a continuous, linear pseudotime trajectory from early to late embryonic stages. This trajectory was marked by a progressive downregulation of stemness-associated regulators (e.g., PAX7) and upregulation of myogenic commitment and differentiation factors (e.g., MYF5, MYOD1, and MYOG), faithfully mirroring chronological development. Cell–cell communication analysis revealed that quiescent satellite cells exhibited the most extensive intercellular signaling networks (e.g., FGFR, Ephrin, collagen, CADM), whereas activated and proliferative/differentiating cells showed progressively diminished communication capacity. Across developmental stages, the contribution intensities of key signaling pathways—including SEMA6, CDH, FGF, LAMININ, MK, MPZ, CADM, FN1, and COLLAGEN—varied significantly among satellite cell states, indicating state-specific responsiveness to microenvironmental cues. Collectively, these findings demonstrate that satellite cells dynamically coordinate extrinsic signal integration with intrinsic differentiation programs to achieve orderly myogenic progression. This study provides a high-resolution single-cell atlas of goose SMSC development, uncovering subpopulation heterogeneity, state-specific molecular signatures, and key signaling pathways, with important implications for avian muscle biology and genetic improvement of poultry. Full article

Disruptions in supply networks have caused many logistical and planning challenges in the last few years. The previous predictability of the shipping times of raw materials changed drastically due to various global issues, which affected many production areas, including the wood industry. This work is motivated by a case study of a Central European plywood production facility, where supply-side disruptions caused difficulties in meeting deadlines for downstream companies of the construction and furniture industry. As a result, the objective of production planners shifted towards mitigating the financial burden caused by cancellation penalties. Three MILP (Mixed-Integer Linear Programming) models and a genetic algorithm were developed to tackle the scheduling of a plywood production plant with raw material shipments and order deadlines. The novelty of the considered problem lies in the flexibility of swapping order deadlines from the same client, which was inspired by the real-life deals of the aforementioned company. The methods were tested on 120 benchmark instances of different sizes generated from real industrial data. The genetic algorithm terminated within 60 s for all instances and found the optimal or best-known solution in 71 of 80 short-horizon instances, while also remaining efficient on larger 30-day cases. As the solution approach is not specific to plywood production, it can be applied to scheduling problems in other fields as well, where similar disruptions can develop, and the production process features are covered by the Multi-Mode Resource-Constrained Project Scheduling Problem class. Full article

This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with attention to forecast electricity prices, but they differ in the way the resulting forecasts are converted into operational decisions. The first method uses 24- to 48 h-ahead price forecasts within a mixed-integer linear programming rolling-horizon optimizer to compute the revenue-maximizing schedule for the following day. The second method uses an online twin delayed deep deterministic policy gradient controller that outputs a complete 24 h charge–discharge schedule once per day, using state information that includes battery state, recent price history, forecast prices, and forecast photovoltaic production. The control models are trained using historical data from 2019 to 2022, validated chronologically on 2023 data, and tested on the 2024 annual horizon, while the price forecaster is trained and validated on non-2024 data and evaluated on the held-out 2024 test period. In the realistic execution setting, schedules are planned using forecast photovoltaic production and implemented against actual photovoltaic production, while the day-ahead omniscience benchmark uses actual next-day prices and actual PV production as ideal scheduling inputs. The BiLSTM-MILP framework achieves EUR 10,928.7 over the 2024 test horizon, corresponding to 82.67% of the day-ahead omniscience benchmark. The online BiLSTM-TD3 controller achieves EUR 10,884.9, corresponding to 82.34% of the same benchmark and 99.60% of the BiLSTM-MILP revenue, while outperforming a rule-based baseline by 34.9%. These results show that online deep reinforcement learning can approach the performance of explicit mathematical optimization in day-ahead PV-battery trading while substantially improving over simple rule-based operation. Overall, the results indicate that BiLSTM-based forecasts can support both optimization-based and reinforcement-learning-based day-ahead control for the examined PV-battery system. Full article

Background: Ovarian cancer (OVCA) remains the most lethal gynecologic malignancy, with poor prognosis largely due to late-stage diagnosis and therapy resistance. Pyroptosis, a pro-inflammatory form of programmed cell death, has recently emerged as a regulator of tumor progression and immune regulation. This study aimed to systematically profile pyroptosis-related genes and identify robust biomarkers for OVCA. Methods: Microarray data from the GSE54388 dataset were analyzed to characterize pyroptosis-related gene expression. Immune cell infiltration was assessed using xCell, and pathway enrichment was performed via Gene Set Enrichment Analysis (GSEA). Weighted Gene Co-expression Network Analysis (WGCNA) identified hub genes, followed by Gene Ontology (GO) and Reactome enrichment. Machine learning algorithms (Support Vector Machine, XGBoost, and Generalized Linear Model) were employed for feature selection and biomarker identification. Validation was conducted across independent bulk and scRNA-seq datasets, with GEPIA2 used to compare OVCA and normal samples and KMplot for survival analysis. Results: OVCA samples showed significantly reduced infiltration of CD4⁺ and CD8⁺ T cells, mast cells, monocytes, neutrophils, and immature dendritic cells compared to normal samples. GSEA revealed enrichment of cell cycle-related pathways, implicating pyroptosis-related genes as key regulators of mitotic progression. From 1097 differentially expressed genes, 22 pyroptosis-related DEGs (PYRDEGs) were identified, with nine hub genes (CASP1, CEP55, CHMP4C, HTRA1, IL18, MELK, PKM, PTX3, TNFSF13B) strongly associated with OVCA. Functional enrichment linked these genes to cytokinesis, inflammasome activity, and immune signaling. Machine learning consistently identified CEP55 as the core biomarker, demonstrating high diagnostic accuracy (AUC up to 0.972) and significant upregulation in OVCA samples. Correlation analysis linked CEP55 expression to altered immune cell populations, including positive associations with Th1 and class-switched memory B-cells and negative associations with iDCs, Tregs, and M2 macrophages. CEP55 was highly expressed across bulk and scRNA-seq datasets (cancer epithelial and CD8+ TEMRA cells) and negatively correlated with overall survival (OS) and progression-free survival (PFS). Conclusions: Pyroptosis-related genes play pivotal roles in OVCA pathogenesis. CEP55 emerges as a promising biomarker for early detection and a potential therapeutic target, bridging cell cycle regulation with immune modulation. Full article

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article