Search Results (1,404)

This study investigates the use of deep reinforcement learning techniques to improve traffic signal control systems through the integration of deep learning and reinforcement learning approaches. The purpose of a deep reinforcement learning architecture is to provide adaptive control via a reinforcement learning interface and deep learning for the representation of traffic queues with regards to signal timings. This has driven recent research, which has reported success in the use of such dynamic approaches. To further explore this success, we apply a deep reinforcement learning algorithm over a grid of 21 interconnected traffic signalized intersections and monitor its effectiveness. Unlike previous research, which often examined isolated or idealized scenarios, our model is applied to the real-world traffic network of Via “Prenestina” in eastern Rome. We utilize the Simulation of Urban MObility (SUMO) platform to simulate and test the model. This study has two main objectives: ensure the algorithm’s correct implementation in a real traffic network and assess its impact on public transportation, incorporating an additional priority reward for public transport. The simulation results confirm the model’s effectiveness in optimizing traffic signals and reducing delays for public transport. Full article

Breastfeeding is a complex biological system and a bidirectional physiological dialogue in which the infant may contribute to maternal breast health. This review synthesizes current evidence, clearly separating established findings from emerging hypotheses, to examine the possible infant-driven mechanisms that influence hormonal and immune homeostasis in the mammary gland. We evaluate how neonatal suckling coordinates interconnected hormonal reflexes and immune activity, and we explore the hypothesis that the retrograde flow of infant saliva to the breast tissue could activate maternal enzymatic defenses, particularly the xanthine oxidase and lactoperoxidase systems. We also consider the activation of antimicrobial peptides through direct contact at the nipple and areola, including cathelicidin and defensins, as well as the potential roles of fetal microchimerism and microbial transfer from the infant’s mouth in strengthening breast resilience. Although much of the evidence remains indirect and based on in vitro and animal models, the convergence of data supports a reformulated conceptual model that presents the infant as an active physiological partner rather than a passive recipient of milk. Recognizing this shift has important clinical implications for the prevention of inflammatory conditions such as mastitis, the improvement of breastfeeding support strategies, and the optimization of maternal and infant health outcomes. The review also identifies significant gaps in current knowledge and cautiously proposes hypotheses to explore these mechanisms. While preliminary, this framework offers an original perspective that may guide future research and open new paths in the study of human lactation biology. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article

Matroid is a mathematical structure that extends the concept of independence. The fuzzy graphic binary matroid serves as a generalization of linear dependence, and its properties are examined. Power grid networks, which manage the generation, transmission, and distribution of electrical energy from power plants to consumers, are inherently a complex system. A key objective in analyzing these networks is to ensure a reliable and uninterrupted supply of electricity. However, several critical issues must be addressed, including uncertainty in communication links, detection of redundant or sensitive circuits, evaluation of network resilience under partial failures, and optimization of reliability in interconnected network systems. To support this goal, the concept of a fuzzy graphic binary matroid is applied in the analysis of power grid communication network, offering a framework that not only incorporates fuzziness and binary conditions but also enables systematic identification of weak circuits, redundancy planning, and reliability enhancement. This approach provides a more realistic representation of operational conditions, ensuring better fault tolerance and improved efficiency of the grid. Full article

China has entered a period of urban renewal, with the focus shifting from large-scale incremental construction to both upgrading existing building quality and adjusting incremental structures. There are three main types of urban renewal: demolition and reconstruction, comprehensive improvement, and organic renewal. The latter systematically optimizes and enhances urban functions, spaces, and culture through gradual renovation methods and is, therefore, suitable for use in ancient cities. To promote organic renewal, the problem of limited space resources must first be addressed, which can be resolved to a certain extent by the moderate development of underground spaces; preliminary evaluations of the development potential are also required. In consideration of the demands of organic renewal, we constructed a novel indicator system for evaluating underground space development potential (USDP) in ancient cities that assesses two dimensions: development demand and development suitability. A multi-factor comprehensive evaluation method was adopted to quantify the indicators of USDP, taking Shaoxing Ancient City (SAC) as the case study. According to the USDP evaluation, SAC can be divided into four kinds of areas: high-potential, general-potential, low-potential, and prohibited development areas. High-potential areas accounted for 16.38% of the total evaluation area and were primarily concentrated in or near key locations: train transit stations (Shaoxing Railway Station), public service facilities, evacuated land, and cultural and tourism facilities around historic districts (Shusheng Guli Historical and Cultural Street). The proposed development strategies for these areas included the interconnection of metro stations, redevelopment of relocation-related and vacated land, construction of underground cultural corridors, and supplementation of parking facilities. For developed underground spaces with low utilization efficiency, functional renewal and management improvement measures were put forward. Our method of evaluating the USDP of ancient cities and the strategies proposed to optimize the utilization of underground space can provide reference examples for SAC and other similar ancient cities. Full article

In reconfigurable radio frequency (RF) microsystems, the interconnect structure critically affects high-frequency signal integrity, and the accuracy of electromagnetic (EM) modeling directly determines the overall system performance. Conventional neural network-based surrogate models mainly focus on minimizing numerical errors, while neglecting essential physical constraints, such as causality and passivity, thereby limiting their applicability in both time and frequency domains. This paper proposes a physics-constrained Neuro-Transfer surrogate model with a broadband output architecture to directly predict S-parameters over the 1–50 GHz range. Causality and passivity are enforced through dedicated regularization terms during training. Furthermore, a particle swarm optimization (PSO)-based multi-objective intelligent optimization framework is developed, incorporating fixed-weight normalization and a linearly decreasing inertia weight strategy to simultaneously optimize the S11, S21, and S22 performance of a quasi-coaxial TSV composite structure. Target values are set to −25 dB, −0.54 dB, and −24 dB, respectively. The optimized structural parameters yield prediction-to-simulation deviations below 1 dB, with an average prediction error of 2.11% on the test set. Full article

The increasing integration of renewable energy sources (RES) and rising energy demand have created challenges in maintaining stability in interconnected power systems, particularly in terms of frequency, voltage, and tie-line power. While traditional load frequency control (LFC) and automatic voltage regulation (AVR) strategies have been widely studied, they often fail to address the complexities introduced by RES and nonlinear system dynamics such as boiler dynamics, governor deadband, and generation rate constraints. This study introduces the Arithmetic Optimization Algorithm (AOA)-optimized PI(1+DD) controller, chosen for its ability to effectively optimize control parameters in highly nonlinear and dynamic environments. AOA, a novel metaheuristic technique, was selected due to its robustness, efficiency in exploring large search spaces, and ability to converge to optimal solutions even in the presence of complex system dynamics. The proposed controller outperforms classical methods such as PI, PID, I–P, I–PD, and PI–PD in terms of key performance metrics, achieving a settling time of 7.5 s (compared to 10.5 s for PI), overshoot of 2.8% (compared to 5.2% for PI), rise time of 0.7 s (compared to 1.2 s for PI), and steady-state error of 0.05% (compared to 0.3% for PI). Additionally, sensitivity analysis confirms the robustness of the AOA-optimized controller under ±25% variations in turbine and speed control parameters, as well as in the presence of nonlinearities, demonstrating its potential as a reliable solution for improving grid performance in complex, nonlinear multi-area interconnected power systems. Full article

This study introduces an efficient, all-aqueous emulsion-templating strategy for fabricating highly tunable yeast immobilization carriers with superior biocatalytic performance. Utilizing cellulose nanocrystals (CNCs) to stabilize dextran/polyethylene glycol (Dex/PEG) water-in-water emulsions, an architecture-controlled void is obtained by crosslinking the PEG-rich phase with variable concentrations of polyethylene glycol diacrylate (PEGDA) (10–25 wt%). This approach successfully yielded macroporous networks, enabling precise tuning of void diameters from 10.4 to 6.6 μm and interconnected pores from 2.2 to 1.4 μm. The optimally designed carrier, synthesized with 15 wt% PEGDA, featured 9.6 μm voids and robust mechanical strength (0.82 MPa), and facilitated highly efficient yeast encapsulation (~100%). The immobilized yeast demonstrated exceptional fermentation activity, remarkable storage stability (maintaining > 95% productivity after 4 weeks), and high reusability (85% activity retention after seven cycles). These enhancements are attributed to the material’s excellent water retention capacity and the provision of a stable microenvironment. This green and straightforward method represents a significant advance in industrial cell immobilization, offering unparalleled operational stability, protection, and design flexibility. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

Decarbonizing the industrial sector is essential to achieving net-zero targets and ensuring a sustainable future. Carbon–Hydrogen–Oxygen Symbiosis Networks (CHOSYN) are a set of interconnected hydrocarbon-processing plants that optimize the synergistic use of mass and energy resources in pursuit of both environmental objectives and profitability enhancement. However, this interconnectedness also introduces fragility, arising from technical and administrative dependencies among the participating facilities. In this work, a systematic framework is introduced to incorporate resilience assessment and sustainability enhancement within CHOSYNs. A CHOSYN representation is developed for a proposed industrial cluster, where processes are linked through interceptor units, which facilitate the exchange and conversion of carbon-, hydrogen-, and oxygen-based streams to meet demands. A multi-objective optimization framework is formulated with four competing goals: minimizing cost, minimizing net CO₂ emissions, maximizing internal CO₂ utilization, and minimizing the number of interceptors’ processing steps. The augmented $\epsilon$-constraint method is used to generate a Pareto front that captures the trade-offs among these objectives. To complement the synthesis, a resilience assessment framework is applied to evaluate network performance under disruption by incorporating inter-plant dependencies and modeling disruption propagation. The results show that even under worst-case scenarios, integration through CHOSYN can achieve significant gains in CO₂ utilization and reductions in raw material procurement requirements. Resilience analysis adds an important dimension by quantifying the economic impacts of disruptions to both highly connected and sparsely connected yet critical nodes, revealing vulnerabilities not evident from topology alone. Full article

Under the high penetration of new energy, multi-machine interconnected grid-connected inverters (GCIs) are prone to lose stability due to the interaction with the power grid. To enhance their adaptability to the grid, a stability improvement method for multi-machine interconnected GCI systems with flexible control bandwidth design is proposed. First, based on the design principles of the converter control bandwidth, the transfer function models of the voltage outer loop and current inner loop are constructed, and the inner and outer loop control bandwidths that meet the requirements are designed. Secondly, to mitigate the adverse effects caused by the interaction of control bandwidths among multi-machine interconnected inverters, By analyzing the influence mechanism of the inner and outer loop bandwidths on the stability of the multi-machine interconnection system under different ratios, a flexible control bandwidth allocation scheme for different inverters in the multi-machine interconnection scenario is designed., thereby enhancing the stability of multi-machine-interconnected GCI through differentiated control bandwidth design. Unlike methods that add extra control loops, the method proposed in this paper does not require sampling new physical variables or modifying the control structure. Instead, it only necessitates adjusting the controller parameters of the multi-machine interconnection system, specifically the optimized distribution of bandwidth, to enhance the stability of the multi-machine interconnection system. Finally, simulation results are presented to verify the correctness and effectiveness of the proposed control method. Full article

Natural sand extraction for concrete manufacturing is a global issue for ecological balance and environmental concerns. This study introduced three mixes with three newly developed sand types to replace natural sand in concrete manufacturing. Additionally, three more mixes were made by incorporating optimized 10% silica fume. The durability of the prepared mixes was evaluated at high temperatures of (150–750 °C) at the interval of 150 °C and against immersion in a 5% sulfuric acid solution for 28, 56, 91, and 182 days, respectively. The study’s results reported the stability of the samples up to 300 °C, and then the fall of the samples started at 450 °C. Severe damage in the samples was formed at about 600 °C, and finally, a total collapse was seen at 750 °C. From (150 to 750 °C), the mix TYPE-3SSFC with a sustainable sand combination (50% recycled sand + 45% desert sand + 5% crumb rubber) and 10% silica fume showed better resistance than the other mixes. The compressive strength in the mix TYPE-3SSFC was 20.6%, 16.3%, 14.7%, 21.3%, 26.5%, and 43.2% higher than the mix TYPE-3SC with 10% silica fume. The mix TYPE-3SSFC with optimized 10% silica fume content showed better resistance against 5% sulfuric acid solution than those without silica fume. By morphological analysis, the mix TYPE-3SSFC showed that the interface improved due to the dense interconnectivity of the concrete mix between the crumb rubber paste and silica fume content. A dense calcite crystal was also seen in the mixture, which confirmed the study’s results. The mix with TYPE 2-Sand (100% recycled sand) revealed inferior results, low stability, and high damage. Thus, 100% recycled sand is not recommended for structural concrete. Full article

Quadratic programming (QP) formulations are widely used in optimal investment portfolio selection, a central problem in financial decision-making. In practice, asset allocation decisions operate at two interconnected levels: the strategic level, which allocates the budget across major asset classes, and the tactical level, which distributes the allocation within each class to individual securities or instruments. This study evaluates the Frank–Wolfe (FW) algorithm as a computationally alternative to a QP formulation implemented in CVXPY and solved using OSQP (CVXPY–OSQP solver) for tactical investment portfolio optimization. By iteratively solving a linear approximation of the convex objective function, FW offers a distinct approach to portfolio construction. A comparative analysis was conducted using a tactical portfolio model with a small number of stock assets, assessing solution similarity, computational running time, and memory usage. The results demonstrate a clear trade-off between the two methods. While FW can produce portfolio weights closely matching those of the CVXPY–OSQP solver at lower and feasible target returns, its solutions differ at higher returns near the limits of the feasible set. However, FW consistently achieved shorter execution times and lower memory consumption. This study quantifies the trade-offs between accuracy and efficiency and identifies opportunities to improve FW’s accuracy through adaptive iteration strategies under more challenging optimization conditions. Full article

Audio systems with unbalanced connections are susceptible to interference from ground loops, which manifests as hum and noise. This paper introduces and evaluates a novel passive Audio Interference Suppressor in Analog Audio Interface (AISAAI) designed to mitigate this problem. The AISAAI circuit is inserted between an audio device’s rectifier ground and its protective earth terminal, creating an optimized impedance path that reduces interference while ensuring safety. This approach is analyzed within a proposed Analog Audio Interconnection System (AAIS) framework. Experimental results show that common-mode voltages from protective earth potential differences are the primary source of interference. The optimized AISAAI suppressor achieves a consistent 15–30 dB reduction in measured audio interference across the audio band, regardless of the interconnect cable characteristics. This study confirms AISAAI as an effective solution for reducing ground loop noise in unbalanced audio systems and underlines the usefulness of the AAIS model for systemic analysis. Full article

Enhanced lubrication is critical for improving gear wear resistance. Current research on surface textures has overlooked the fundamental role of structural connectivity. Inspired by biological scales, a biomimetic hexagonal texture (BHT) was innovatively designed for tooth flanks, featuring dual-orientation grooves (perpendicular and inclined to the rolling-sliding direction) with bidirectional interconnectivity. This design synergistically combines hydrodynamic effects and directional lubrication to achieve tribological breakthroughs. A lubrication model for line contact conditions was established. Subsequently, the texture parameters were then optimized using response surface methodology and numerical simulations. FZG gear tests demonstrated the superior performance of the optimized BHT, which achieved a substantial 82.83% reduction in the average wear area ratio and a 25.35% decrease in tooth profile deviation variation. This indicated that the biomimetic texture can effectively mitigate tooth surface wear, thereby extending the service life of gears. Furthermore, it significantly improves thermal management by enhancing convective heat transfer and lubricant distribution, as evidenced by a 7–11 °C rise in bulk lubricant temperature. This work elucidates the dual-mechanism coupling effect of bio-inspired textures in tribological enhancement, thus establishing a new paradigm for gear surface engineering. Full article