Search Results (1,792)

The advent of 5G/6G technologies and the pervasive deployment of IoT devices are driving the emergence of demanding applications that necessitate ultra-low latency, high bandwidth, and significant computational power. Traditional cloud computing models fall short in meeting these stringent requirements. To address this, Software-Defined Edge Networks (SDENs) have emerged as a promising architecture, yet efficiently managing their heterogeneous and geographically distributed resources poses substantial challenges for optimal application provisioning. In response, this paper proposes a novel framework for intelligent task offloading, which reframes the intricate multi-component application task offloading problem as a Virtual Network Embedding (VNE) challenge within a SDEN environment. We introduce a comprehensive model where complex applications are represented as Virtual Network Requests (VNRs). In this model, each VNR consists of virtual nodes that demand specific computing and storage resources, as well as virtual links that demand specific bandwidth and must adhere to maximum tolerable delay constraints. To dynamically solve this NP-hard VNE problem in the face of stochastic VNR arrivals and dynamic network conditions, we leverage Deep Reinforcement Learning (DRL). Specifically, a Soft Actor-Critic (SAC) agent is employed at the SDN controller. This agent learns a sequential decision-making policy for mapping virtual nodes to physical edge servers and virtual links to network paths. To guide the agent towards efficient resource utilization, we define the reward for each successful embedding as the long-term revenue-to-cost ratio. By learning to maximize this reward, the agent is naturally driven to find economically viable allocation strategies. Comprehensive simulation experiments demonstrate that our SAC-based VNE approach significantly outperforms other baselines across key metrics, affirming its efficacy in dynamic SDEN environments. Full article

The Flexible Job-shop Scheduling Problem (FJSP), a pivotal NP-hard challenge in intelligent manufacturing, has been increasingly addressed by Deep Reinforcement Learning (DRL) methods. However, existing approaches face a dilemma: Proximal Policy Optimization (PPO) ensures stability but suffers from conservative exploration, while Soft Actor–Critic (SAC) enhances exploration but lacks stability in discrete scheduling spaces. To resolve this trade-off, this study proposes PPO-Graph Explorer, a novel framework that integrates a Graph Isomorphism Attention Network (GIAN) with an Entropy-Adjusted PPO (EAE-PPO). Unlike generic Graph Transformers, our GIAN employs a structure-aware hybrid design specifically tailored for FJSP’s disjunctive graph topology. EAE-PPO introduces a structured exploration curriculum that enables the agent to mimic aggressive search behaviors early in training without sacrificing on-policy stability. Extensive experiments on standard benchmarks (Brandimarte, Hurink, Dauzère–Pérès) demonstrate our method’s superiority. Compared to state-of-the-art DRL baselines, it achieves an average makespan gap reduction of 5.1 percentage points with zero statistical outliers. Qualitative analysis further reveals an 8.95% reduction in makespan on representative instances, accompanied by a significant increase in average machine utilization from 89.0% to 98.1%. Full article

Understanding embryonic development is fundamental to improving captive breeding protocols and supporting conservation strategies for threatened fish species. Prochilodus vimboides is a Neotropical freshwater fish for which detailed information on early ontogeny remains scarce. This study aimed to characterize the embryonic and early larval development of P. vimboides under captive conditions. Broodstock were hormonally induced to reproduce, and extrusion occurred between 209 and 230 degree-hours after induction at 21.49 ± 0.15 °C. Embryonic development was monitored at regular intervals after fertilization (AF) using freshly collected eggs examined under a stereomicroscope. The principal developmental stages were identified, namely zygote, cleavage, including morula and blastula, gastrula, organogenesis, and hatching. Fertilized oocytes exhibited marked hydration and formation of a large perivitelline space at 15 min AF. More than 50% of embryos reached the two-blastomere stage by 20 min AF, and cleavage continued until 2 h 14 min AF. The gastrula stage was observed at 3 h 23 min AF, blastopore closure occurred at 11 h 47 min AF, and organogenesis began at 12 h 55 min AF. Complete hatching occurred at 22 h 04 min AF, and larvae subsequently initiated yolk sac absorption without cannibalistic behavior. These findings provide a species-specific developmental framework that supports captive production and conservation efforts for P. vimboides in the Paraíba do Sul River Basin. Full article

Optimizing traffic performance using artificial intelligence (AI) has consistently been a prominent direction in the development of intelligent transportation systems. While numerous studies have proposed methodologies for integrating cooperative connected and autonomous vehicles (CCAVs) with traffic signal systems via V2X communication, they often rely on simplified control strategies or lack effective coordination between signal timing and vehicle behavior. In this study, we propose a novel, integrated traffic signal control strategy combined with CAVs using deep reinforcement learning. Our key differentiation lies in the simultaneous optimization of signal phases using the Soft Actor–Critic (SAC) algorithm and the regulation of CCAVs via cooperative adaptive cruise control and Green Light Optimal Speed Advisory. This dual approach allows the signal controller to leverage rich state information from CAVs and the road infrastructure, enabling more anticipatory and cooperative decisions. The proposed approach is implemented and evaluated through various scenarios using the Simulation of Urban MObility (SUMO) platform. The results demonstrate the superior learning performance and robustness of the proposed model. Specifically, our proposed model achieves a significant reduction in average vehicle waiting time by up to over 80% compared to baseline models under high-demand scenarios (4800–6000 veh/h). These findings underscore the critical importance of joint optimization in future intelligent transportation systems, paving the way for more resilient urban traffic management. Full article

The periostracum is the outermost shell layer and the first produced during shell formation in molluscs. This organic layer isolates the extrapallial space from the external environment and provides a scaffold for subsequent calcification. In cephalopods with an internal shell, some organic shell structures are putatively homologous to the periostracum of other molluscan groups. However, neither their detailed structure nor their mode of formation has been described, leaving the extent of this homology unresolved. To address this issue, we investigated the morphology and formation of the organic layer of the dorsal shield and the gladius in embryos of the cuttlefish Sepia officinalis Linnaeus, 1758, and the squid Loligo vulgaris Lamarck, 1798, respectively, using light microscopy and transmission electron microscopy. In both species, the periostracum forms within a periostracal groove located along the lateral and anterior margins of the shell sac. As in other molluscs, secretions from columnar cells at the bottom of the groove produce a dense layer, while a translucent layer is subsequently added beneath it through secretions from cuboidal cells. The main difference is the absence of both a pellicle and of the specialized glandular cells that typically secrete it at the bottom of the periostracal groove. Full article

Recovery of scandium from chloride-bearing process liquors formed during titanium–magnesium production remains constrained by trace-level metal content and chemically aggressive solution matrices. Within the present study, the retention behaviour of Sc³⁺ species in strongly acidic chloride media was examined through batch-mode interaction with gel-type sulfonated cation exchangers, namely KU-2-8, Lewatit SP112H, Purosorb SAC140H, and Purolite C-150H. Quantitative evaluation of sorption efficiency was performed by calculating equilibrium uptake (q_e), phase distribution factor (K_d), and percentage recovery (R). Under identical liquid–solid ratios, the Lewatit SP112H matrix exhibited superior affinity toward dissolved scandium, achieving q_e = 179.82 mg/g and K_d = 172.41 mL/g. Equilibrium fitting procedures revealed that scandium uptake by Purosorb SAC140H conforms to monolayer-type retention described by the Langmuir formalism (R² = 0.9786), whereas sorption on Lewatit SP112H proceeds over energetically non-uniform sites and is more adequately represented by Freundlich and Dubinin–Radushkevich approximations. The observed retention characteristics establish a selection framework for ion-exchange media applicable to scandium concentration from acidic chloride hydrometallurgical streams. Full article

Platinum group metals (PGMs) are strategic metals, and recycling PGMs in spent automobile exhaust catalysts (SACs) is a key path to alleviate the contradiction between resource supply and demand. This paper proposes a new Cu-Ni capture process and conducts research on the recovery of PGMs from SACs. Through the binary phase diagram analysis of Cu, Ni and PGMs and the thermodynamic calculation of the system reduction reaction, the feasibility of this technology was theoretically confirmed. Experimental results show that under the conditions of a temperature of 1450 °C, a holding time of 90 min, a Cu-Ni ratio of 1:1, and a basicity of 0.58, the recovery rates of Pt, Pd, and Rh reached 99.2%, 99.34%, and 98.48% respectively. Combined with orthogonal experiments, it was verified that temperature is the most influential factor on the recovery rate, and the four-stage capture mechanism of “initial diffusion—droplet aggregation—sedimentation and wetting—slag–metal separation” was clarified. This process reduces the melting temperature and provides new technology for green and efficient recycling of PGMs. Full article

Background/Objectives: The gut–liver axis plays a central role in cholesterol homeostasis, linking intestinal absorption, microbial metabolites, and hepatic lipid regulation. Dysregulation of this axis contributes to hypercholesterolemia and cardiometabolic risk, beyond classical cholesterol synthesis pathways. This study evaluated a novel multi-botanical formulation (MIX) that combines Gastrodia elata, Black Garlic, Primula veris, and Emblica officinalis (AMLA) to integrate modulation of cholesterol metabolism through intestinal and hepatic mechanisms. Methods: Individual extracts were chemically characterised for polyphenols, flavonoids, polysaccharides, S-allyl-L-cysteine (SAC), and tannins. Caco-2 cells were treated with varying doses to determine optimal concentrations and for viability, transepithelial electrical resistance, and permeability analysis. Supernatants post-intestinal passage were applied to HepG2 cells under high-glucose conditions to assess viability, oxidative stress, SRC/ERK-MAPK signalling, cholesterol synthesis (HMGR), LDL uptake, PCSK9–LDLR–SREBP-2 axis, and bile acid production. Results: MIX enhanced intestinal barrier integrity (TEER, tight junctions, permeability) and preserved cell viability compared with single extracts. In HepG2 cells, MIX demonstrated synergistic effects: it reduced HMGR expression by 83–90% relative to individual extracts, increased LDLR expression by 43–97%, suppressed PCSK9 by up to 92%, and lowered total cholesterol and LDL uptake more effectively than RYRF. MIX also amplified bile acid production and free cholesterol excretion, indicating improved hepatic clearance pathways. SRC and ERK-MAPK signalling were favourably modulated, supporting hepatocyte survival under metabolic stress. Conclusions: The multi-botanical formulation exerts complementary and synergistic effects on intestinal absorption and hepatic cholesterol regulation, integrating suppression of cholesterol synthesis, enhanced LDL clearance, and stimulated elimination via bile acids. These findings highlight the potential of the MIX formulation to modulate metabolically induced cholesterol dysregulation, supporting further in vivo and clinical investigation. Full article

In the power grid scheduling process, load forecasting serves as the foundation for ensuring stability and economic dispatch. It not only optimizes resource allocation but also strengthens the system’s productivity and stability, helps prevent potential risks, and ensures the reliability and safety of power supply. Therefore, a predictive soft actor–critic network (PreSAC-Net) algorithm is proposed, which aims to reduce grid operating costs and enhance system stability through an enhanced load forecasting model and an optimized scheduling strategy. First, the load forecasting is performed using a sequential feature fusion model with gated recurrent attention and diffusion (SeqFusion-GRAD), which integrates gated recurrent units (GRU), attention mechanisms, and generative diffusion models to strengthen time-series modeling and accurately predict household electricity loads. Second, a multidimensional data fusion technique incorporates meteorological and other relevant factors into household load data, improving the forecast accuracy and robustness. Furthermore, the scheduling optimization is conducted with the soft actor–critic (SAC) algorithm, which explores scheduling schemes to minimize cost under multiple constraints. The integrated approach not only balances the electricity supply and demand effectively but also supports the sustainable development of intelligent grids. Based on the experimental results, the proposed method significantly enhances power system operational efficiency and stability. Full article

Unmanned aerial vehicle (UAV) guidance systems must operate reliably under significant uncertainties, such as sensor noise, target maneuvers, and environmental disturbances. Traditional guidance methods like proportional navigation (PN), while computationally efficient, often struggle to maintain performance under such challenging conditions. To overcome these limitations, this study proposes a robust UAV guidance framework based on deep reinforcement learning (DRL), specifically utilizing the soft actor–critic (SAC) algorithm. The UAV–target tracking problem is formulated as the Markov decision process (MDP) for both two-dimensional (2D) and three-dimensional (3D) scenarios. A deep neural network policy is trained in noisy environments to generate acceleration commands that minimize the zero-effort miss (ZEM). Extensive numerical simulations conducted using the OpenAI Gym validate effectiveness of the proposed method under previously unseen initial conditions and increased noise levels. The results demonstrate that the SAC-based policy achieves higher tracking success rates than the PN, particularly under strict terminal conditions and observation noise. Full article

Extant birds and the earliest dinosaurs may share fundamental metabolic features essential for aerobic exercise, suggesting that the extraordinary physical performance typical of avian species originated when dinosaurs first appeared during the Carnian Pluvial Episode (CPE). This physiological adaptation is complemented by hyperactive mitochondria that exhibit high oxygen consumption and low reactive oxygen species production. Molecular genomics of fossils, the so-called “Jurassic Genome,” indicates that these early dinosaurs possessed compact genomes, 50–60% the size of the human genome, and small cells, implying a highly stringent metabolic regime. We suggest that hyperactive mitochondria, closely associated with compact genomes and small cells, drive theropod adaptation to the hot, dry, and hypoxic environments of the Late Triassic period, ultimately enabling their ecological dominance. Early dinosaurs such as Herrerasaurus are hypothesized to have possessed advanced physiological traits shared with modern birds, including hyperactive mitochondria, compact genomes, small cells, and a developing air-sac system. Collectively, these features most likely may have contributed to exceptional metabolic capacity, locomotor performance, and adaptation to the harsh environment of the CPE. Full article

As a next-generation immersive service, holographic video enables users to move freely within a virtual world. This imposes stringent requirements on wireless networks. Given the massive bandwidth capacity inherent to visible light, visible light communication (VLC) can effectively meet the transmission requirements of holographic video and is an ideal wireless technology for next-generation indoor immersive services. However, VLC channels are highly dependent on Line-of-Sight (LoS) links. Due to user mobility, traditional VLC systems relying on fixed-orientation Photodetectors (PDs) often suffer from severe channel fading, which significantly degrades the transmission performance. In this paper, we propose an indoor VLC holographic video transmission architecture supporting rotatable PDs, utilizing rotatable PDs mounted on Head-Mounted Displays (HMDs) to assist in holographic video transmission. To minimize the total transmission delay of all users, we address the holographic video transmission problem by jointly optimizing the transmit power allocation of VLC Access Points (APs) and the pitch and roll angles of the users’ PDs. By formulating the problem as a Markov Decision Process (MDP), we address it using a novel Deep Reinforcement Learning (DRL) strategy leveraging the Soft Actor–Critic (SAC) architecture. Simulation results demonstrate that the proposed scheme reduces the overall latency by up to 29.6% compared to the benchmark schemes. Furthermore, the convergence speed of the algorithm is improved by 35% compared to traditional deep reinforcement learning algorithms such as Deep Deterministic Policy Gradient (DDPG). Full article

The large-scale adoption of electric vehicles (EVs) improves transport sustainability but creates severe peak-time stress on distribution grids. In PV-assisted charging networks, station operators must jointly decide retail charging prices and energy-storage dispatch under uncertain demand and generation conditions. This paper develops a distributed federated deep reinforcement learning framework for multi-station scheduling, where each station trains a local soft actor–critic (SAC) policy and only model parameters are exchanged with a global aggregator. To better adapt prices to local supply–demand conditions, we introduce a sales-factor-based correction mechanism that links the announced price to demand pressure and storage status. The objective combines station revenue, operating expenses, and user-discomfort-related penalties under operational constraints. Simulation results on a five-station setting show stable convergence and consistent gains over benchmark methods, with profit improvements of 3.90–39.00%. The framework keeps raw operational data local and supports collaborative optimization across stations. Full article

This research addresses the coupling issue between speed tracking and vertical posture in articulated unmanned mining trucks within unstructured environments. An extension theory-based coordinated control strategy is proposed, incorporating both articulation joint safety and vehicle stability. The control framework employs extension theory to classify operational modes based on articulation angle and velocity deviation. For longitudinal motion, active disturbance rejection control (ADRC) is adopted to mitigate the influence of varying payload mass and road slope on speed tracking performance. For vertical dynamics, a soft actor–critic (SAC) algorithm regulates active suspension to improve ride comfort. Both simulations and hardware-in-the-loop testing results demonstrate the superiority of the proposed strategy: coordinated control maintains speed tracking error below 4%, reduces body acceleration by 16.1%, 11.9%, and 17.5%, and improves articulation angle oscillations by 12.6%, 14.6%, and 15.1% across scenarios, confirming the strategy’s enhanced performance over conventional single-loop control approaches. Full article

Background: Non-anastomotic pseudoaneurysm formation due to very late prosthetic graft failure after femoropopliteal bypass is exceptionally rare. Case Presentation: We describe a 73-year-old blind man who presented with rapid enlargement of a mid-thigh mass on the left side, associated with skin necrosis. His history included advanced atherosclerosis with bilateral superficial femoral artery occlusion and prior femoropopliteal bypasses: a right-sided great saphenous vein graft (2006) and a left-sided Dacron^® graft (2008). Computed tomography angiography revealed a giant pseudoaneurysm originating from the mid-portion of the left bypass graft (13.8 × 16.5 cm) with active contrast extravasation and distal popliteal artery occlusion, as well as a large, well-defined lateral thigh lipoma. Open surgery revealed structural graft disruption within the prosthetic body and a large chronic pseudoaneurysm sac containing organized thrombus. En bloc pseudoaneurysm excision and graft exclusion without reconstruction were performed, followed by soft-tissue reconstruction. The postoperative course was uneventful, with complete wound healing by four weeks and no ischemic symptoms during 18 months of follow-up. This exceptionally late prosthetic graft complication underscores the need for long-term surveillance in patients with lower-limb bypass grafts. Conclusions: This case highlights that prosthetic graft failure may occur very late and present insidiously. Recognition of this rare complication is essential for timely diagnosis and individualized surgical management. Full article