Search Results (195)

Background/Objectives: Pregnancy represents a unique physiological state marked by extensive metabolic adaptations, particularly in lipid pathways essential for maternal adjustments, fetal development, and postpartum recovery. This study aimed to explore these changes through untargeted lipidomic profiling. Methods: This observational, comparative, non-interventional clinical study included 107 women, of which 65 were in the third trimester of pregnancy (mean age 27.9 ± 5 years) and 42 were in the early postpartum period (≤7 days, mean age 28.9 ± 5.9 years). Inclusion criteria were singleton, term pregnancies (37–41 weeks) with neonates weighing > 2500 g and no associated pregnancy-related pathologies; exclusion criteria included multiple gestation, use of lipid-altering medications, maternal age > 40 years, or diagnosed pregnancy complications. Plasma samples were analyzed using High-Performance Liquid Chromatography–Quadrupole Time-Of-Flight–Electrospray Ionization (positive mode)–Mass Spectrometry, data were processed with MetaboAnalyst 6.0 using multivariate and univariate analyses (Partial Least Squares–Discriminant Analysis, Volcano Plot, Random Forest, Receiver Operating Characteristic analysis), with statistical significance set at p < 0.05. Results: Multivariate analysis demonstrated a clear separation between groups with high predictive accuracy as reflected by strong classification metrics (Accuracy = 0.90, R² = 0.75, Q² = 0.68). Several discriminative lipids were consistently identified across statistical models, including 2-Methoxyestrone (AUC = 0.861), Eicosanedioic acid (AUC = 0.854), and Pregnenolone sulfate (AUC = 0.843). These biomarkers were further categorized into five major lipid classes: steroid hormones, long-chain fatty acids, lysophospholipids, ceramides/sphingolipids, and glycerolipids. Conclusions: Untargeted lipidomic profiling revealed distinct metabolic signatures that differentiate late pregnancy from early post-partum states. The identification of robust lipid biomarkers with high discriminative performance highlights their potential utility in maternal health monitoring, obstetric risk assessment, and postpartum recovery surveillance. Full article

In Underwater Wireless Sensor Networks (UWSNs) with mobile nodes, the mobility of the nodes leads to dynamic changes in the network topology. Thus, pre-established routing paths may become invalid and next-hop nodes may be unavailable due to link disruptions. This implies that routing decisions for mobile UWSNs that do not account for changes in the connectivity state of communication links cannot guarantee reliable packet delivery. In this study, a Q-learning-based link-aware routing (QLAR) protocol designed for mobile UWSNs is proposed. The proposed QLAR protocol introduces the Link Expiration Time (LET) into the reward function of the Q-learning algorithm as a critical decision metric, thereby guiding the agent to prioritize more stable communication links with longer expected lifetime. In addition, multiple decision metrics are dynamically predicted and updated by actively perceiving and acquiring information from neighbor nodes through periodic control packet interactions. To achieve a balance among these metrics, the Entropy Weight Method (EWM) is employed to adaptively adjust their weights in response to real-time network conditions. Comprehensive simulation results demonstrate that QLAR outperforms existing routing protocols in terms of various performance metrics under different scenarios. Full article

Massive Internet of Things (IoT) deployments face critical spectrum crowding and energy scarcity challenges. Energy harvesting (EH) symbiotic radio (SR), where secondary devices share spectrum and harvest energy from non-orthogonal multiple access (NOMA)-based primary systems, offers a sustainable solution. We consider long-term throughput maximization in an EHSR network with a nonlinear EH model. To solve this non-convex problem, we designed a two-layered optimization algorithm combining convex optimization with a deep reinforcement learning (DRL) framework. The derived optimal power, time allocation factor, and the time-varying environment state are fed into the proposed long short-term memory (LSTM) attention mechanism combined Deep Deterministic Policy Gradient, named the LAMDDPG algorithm to achieve the optimal long-term throughput. Simulation results demonstrate that by equipping the Actor with LSTM to capture temporal state and enhancing the Critic with channel-wise attention mechanism, namely Squeeze-and-Excitation Block, for precise Q-evaluation, the LAMDDPG algorithm achieves a faster convergence rate and optimal long-term throughput compared to the baseline algorithms. Moreover, we find the optimal number of PDs to maintain efficient network performance under NLPM, which is highly significant for guiding practical EHSR applications. Full article

Building upon a theoretical framework, this study utilized 126 field survey questionnaires from farmers in Shouyang County, Shanxi Province, China, coupled with corresponding farmland soil heavy metal monitoring data, to investigate the extent of heavy metal pollution and its mechanistic relationship with farmers’ behavior. The single-factor pollution index (P_i), Nemerow composite pollution index (P_N), and geographical detector were employed to assess pollution levels and elucidate the underlying mechanisms linking farmer practices to soil heavy metal accumulation. Analysis revealed that the mean concentrations of Cu, Ni, Cr, Pb, Cd, and Zn (25.54, 31.47, 98.50, 16.63, 0.16 and 76.92 mg/kg, respectively) in the farmland soil exceeded the background values for soil elements in Shanxi Province, whereas As (1.92 mg/kg) levels were lower. Assessment using P_i indicated that Cr, Pb, Cd, Ni, Cu, and Zn (1.78, 1.13, 1.55, 1.05, 1.07 and 1.21, respectively) were predominantly in a state of mild pollution. Similarly, the P_N (1.50) suggested an overall mild level of composite heavy metal pollution in the soil. Geographical detector(Geo-Detector) analysis demonstrated that the explanatory power (q-value) of interactions among factors-including agricultural film and fertilizer application intensity, farmland fragmentation degree, per capita annual household income, farmland area, and years engaged in farming-on soil heavy metal accumulation was significantly enhanced compared to that of individual behavioral factors. While individual farmers’ behaviors are associated with heavy metal accumulation, the interaction effects among multiple behaviors constitute the dominant factor influencing localized accumulation in farmland soil. Consequently, local authorities should enhance farmers’ requisite knowledge, skills, and practices for mitigating soil heavy metal accumulation through strategies such as promoting large-scale farming, implementing agricultural input reduction initiatives, and intensifying technical and environmental protection training. The Geo-Detector exhibits significant advantages in identifying nonlinear influencing factors and analyzing factor interactions, yielding more comprehensive insights compared to conventional linear models. Full article

We theoretically investigate topological transitions between coplanar and noncoplanar magnetic states in centrosymmetric itinerant magnets on a square lattice. A canonical effective spin model incorporating bilinear and biquadratic exchange interactions at finite wave vectors is analyzed to elucidate the emergence of multiple-Q magnetic orders. By taking into account high-harmonic wave-vector interactions, we demonstrate that a coplanar double-Q spin texture continuously evolves into a noncoplanar triple-Q state carrying a finite scalar spin chirality. The stability of these multiple-Q states is examined using simulated annealing as a function of the relative strengths of the high-harmonic coupling, the biquadratic interaction, and the external magnetic field. The resulting phase diagrams reveal a competition between double-Q and triple-Q states, where the noncoplanar triple-Q phase is stabilized through the cooperative effect of the high-harmonic and biquadratic interactions. Real-space spin textures, spin structure factors, and scalar spin chirality distributions are analyzed to characterize the distinct magnetic phases and the topological transitions connecting them. These findings provide a microscopic framework for understanding the emergence of noncoplanar magnetic textures driven by the interplay between two- and four-spin interactions in centrosymmetric itinerant magnets. Full article

The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols. Full article

We theoretically investigate the stability of double-Q square skyrmion crystals under uniaxial distortion. Using an effective spin model with frustrated exchange interactions and bond-dependent anisotropy in momentum space, we construct the low-temperature magnetic phase diagram via simulated annealing. Our results reveal that uniaxial distortion drives a phase transition from the skyrmion crystal to a single-Q conical spiral state when the ratio of exchange interactions parallel and perpendicular to the uniaxial axis is reduced to about 95%. We further find that topologically trivial double-Q states, which emerge in the low- and high-field regimes, are more robust against uniaxial distortion than the skyrmion crystal appearing in the intermediate-field regime. Finally, we examine the role of bond-dependent anisotropy and demonstrate that a finite relative magnitude of this anisotropy is crucial for stabilizing the skyrmion crystal, even under uniaxial distortion. These findings highlight the delicate interplay between lattice distortions and bond-dependent interactions in determining the stability of multiple-Q magnetic textures, and they provide useful guidance for experimental efforts to manipulate skyrmion crystal phases in centrosymmetric magnets. Full article

Compaction quality control in earthworks and pavements still relies mainly on density-based acceptance referenced to laboratory Proctor tests, which are costly, time-consuming, and spatially sparse. Lightweight dynamic cone penetrometer (LDCP) provides rapid indices, such as $q_{d0}$ and $q_{d1}$, yet acceptance thresholds commonly depend on ad hoc, site-specific calibrations. This study develops and validates a supervised machine learning framework that estimates $q_{d0}$, $q_{d1}$, and $Z_c$ directly from readily available soil descriptors (gradation, plasticity/activity, moisture/state variables, and GTR class) using a multi-campaign dataset of $n=360$ observations. While the framework does not remove the need for the standard soil characterization performed during design (e.g., W, ${\gamma }_{d,\mathrm{field}}$, and $R{C}_{\mathrm{SPC}}$), it reduces reliance on additional LDCP calibration campaigns to obtain device-specific reference curves. Models compared under a unified pipeline include regularized linear baselines, support vector regression, Random Forest, XGBoost, and a compact multilayer perceptron (MLP). The evaluation used a fixed 80/20 train–test split with 5-fold cross-validation on the training set and multiple error metrics ($R^2$, RMSE, MAE, and MAPE). Interpretability combined SHAP with permutation importance, 1D partial dependence (PDP), and accumulated local effects (ALE); calibration diagnostics and split-conformal prediction intervals connected the predictions to QA/QC decisions. A naïve GTR-average baseline was added for reference. Computation was lightweight. On the test set, the MLP attained the best accuracy for $q_{d1}$ ($R^2=0.794$, RMSE $=5.866$), with XGBoost close behind ($R^2=0.773$, RMSE $=6.155$). Paired bootstrap contrasts with Holm correction indicated that the MLP–XGBoost difference was not statistically significant. Explanations consistently highlighted density- and moisture-related variables (${\gamma }_{d,\mathrm{field}}$, $R{C}_{\mathrm{SPC}}$, and W) as dominant, with gradation/plasticity contributing second-order adjustments; these attributions are model-based and associational rather than causal. The results support interpretable, computationally efficient surrogates of LDCP indices that can complement density-based acceptance and enable risk-aware QA/QC via conformal prediction intervals. Full article

Background/Objectives: Since its emergence, COVID-19—caused by the novel coronavirus SARS-CoV-2—has affected millions globally and led to over 1.2 million deaths in the United States alone. This global impact, coupled with the emergence of five new human coronaviruses over the past two decades, underscores the urgency of understanding its pathogenic mechanisms at the molecular level—not only for managing the current pandemic but also preparing for future outbreaks. Small non-coding RNAs (sncRNAs) critically regulate host and viral gene expression, including antiviral responses. Among the molecular regulators implicated in antiviral defense, the microRNA-processing enzyme Drosha has emerged as a particularly intriguing factor. In addition to its canonical role, Drosha also exerts a non-canonical, interferon-independent antiviral function against several RNA viruses. Methods: To investigate this, we employed q/RT-PCR, Western blot, and immunocytochemistry/immunofluorescence in an immortalized normal human lung/bronchial epithelial cell line (NuLi-1), as well as a human colorectal carcinoma Drosha CRISPR knockout cell line. Results: In this study, we observed a striking shift in Drosha isoform expression following infection with multiple SARS-CoV-2 variants. This shift was absent following treatment with the viral mimetic poly (I:C) or infection with other RNA viruses, including the non-severe coronaviruses HCoV-OC43 and HCoV-229E. We also identified a distinct alteration in Drosha’s cellular localization post SARS-CoV-2 infection. Moreover, Drosha ablation led to reduced expression of SARS-CoV-2 genomic and sub-genomic targets. Conclusions: Together, these observations not only elucidate a novel aspect of Drosha’s antiviral role but also advance our understanding of SARS-CoV-2 host–pathogen interactions, highlighting potential therapeutic avenues for future human coronavirus infections. Full article

Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with a fivefold increase in stroke risk. Direct oral anticoagulants (DOACs), including apixaban, are now the preferred therapy for stroke prevention in patients with non-valvular AF (NVAF). However, interindividual variability in drug response and safety remains a major challenge, particularly in elderly patients with comorbidities and polypharmacy. Genetic polymorphisms in drug-metabolizing enzymes and transporters may contribute to variability in apixaban exposure and bleeding risk. This study aimed to evaluate the association of polymorphisms in ABCB1, CYP3A4, and CYP3A5 with steady-state plasma concentrations of apixaban (Cssmin) and hemorrhagic complications in elderly patients with NVAF. Methods: This cross-sectional study included 197 patients (mean age 83 ± 8 years; 67% women) with NVAF treated with apixaban (5 mg twice daily). Genotyping of ABCB1 (rs1045642, rs2032582, rs1128503), CYP3A4*22 (rs35599367), and CYP3A5*3 (rs776746) was performed using allele-specific real-time PCR. Cssmin of apixaban was determined by high-performance liquid chromatography coupled with tandem mass spectrometry. Associations with bleeding events were evaluated. Results: Bleeding events were recorded in 40 patients (20.3%). An association signal was observed for ABCB1 rs1045642, where carriers of the CC genotype had a higher risk of bleeding compared with alternative alleles (OR = 2.805; 95% CI: 1.326–5.935; p = 0.006). After correction for multiple testing, the association remained significant only under the log-additive model (OR = 1.93 per C allele; 95% CI: 1.17–3.20; q = 0.0275; p_adj = 0.044), while recessive and codominant effects did not withstand Bonferroni adjustment. No significant associations were observed for rs2032582, rs1128503, CYP3A4*22, or CYP3A5*3. None of the studied polymorphisms, including rs1045642, significantly affected Cssmin. Concomitant therapy, particularly with antiarrhythmic drugs and statins (rosuvastatin), also increased bleeding risk. Conclusions: The findings highlight the potential contribution of ABCB1 rs1045642 and specific drug–drug interactions to the risk of hemorrhagic complications in elderly NVAF patients receiving apixaban. Full article

In the highly competitive manufacturing environment, customers are increasingly demanding punctual, flexible, and customized deliveries, compelling enterprises to balance profit, energy efficiency, and production performance while seeking new scheduling methods to enhance dynamic responsiveness. Although deep reinforcement learning (DRL) has made progress in dynamic flexible job shop scheduling, existing research has rarely addressed profit-oriented optimization. To tackle this challenge, this paper proposes a novel multi-objective dynamic flexible job shop scheduling (MODFJSP) model that aims to maximize net profit and minimize makespan on the basis of traditional FJSP. The model incorporates uncertainties such as new job insertions, fluctuating due dates, and high-profit urgent jobs, and establishes a multi-agent collaborative framework consisting of “job selection–machine assignment.” For the two types of agents, this paper proposes adaptive state representations, reward functions, and variable action spaces to achieve the dual optimization objectives. The experimental results show that the double deep Q-network (DDQN), within the multi-agent cooperative framework, outperforms PPO, DQN, and classical scheduling rules in terms of solution quality and robustness. It achieves superior performance on multiple metrics such as IGD, HV, and SC, and generates bi-objective Pareto frontiers that are closer to the ideal point. The results demonstrate the effectiveness and practical value of the proposed collaborative framework for solving MODFJSP. Full article

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Multiple-Q magnetic states encompass a broad class of noncollinear and noncoplanar spin textures generated by the superposition of spin density waves. In this study, we theoretically explore the emergence of vortex crystals formed by multiple-Q spin density waves on a two-dimensional triangular lattice with $D_{3\mathrm{h}}$ point group symmetry. Using simulated annealing applied to an effective spin model, we demonstrate that the synergy among the easy-plane single-ion anisotropy, the biquadratic interaction, and the out-of-plane Dzyaloshinsky–Moriya interaction defined in momentum space can give rise to a variety of double-Q and triple-Q vortex crystals. We further examine the role of easy-plane single-ion anisotropy in triple-Q vortex crystals and show that weakening the anisotropy drives topological transitions into skyrmion crystals with skyrmion numbers $\pm 1$ and $\pm 2$. The influence of an external magnetic field is also analyzed, revealing a field-induced phase transition from vortex crystals to single-Q conical spirals. These findings highlight the crucial role of out-of-plane Dzyaloshinskii–Moriya interactions in stabilizing unconventional vortex crystals, which cannot be realized in systems with purely polar or chiral symmetries. Full article

The Cognitive Satellite–Terrestrial Network (CSTN) is an important infrastructure for the future development of 6G communication networks. This paper focuses on a potential communication scenario, where satellite users (SUs) dominate and are selected as the primary users, and terrestrial base station users (TUs) are the secondary users. Additionally, each terrestrial base station owns multiple antennae, and the interference of TUs to SUs in the CSTN is limited to a low level or below. In this paper, based on the observation of diversity and the time-varying characteristics of a variety of user requirements, a multi-agent deep Q-network algorithm under interference limitation (MADQN-IL) was proposed, where the power of each antenna in the base station is allocated to maximize the total system throughput while meeting the interference constraints in the CSTN. In our proposed MADQN-IL, the base stations play the role of intelligent agents, and each agent selects the antenna power allocation and cooperates with other agents through sharing system states and the total rewards. Through a simulation comparison, it was discovered that the MADQN-IL algorithm can achieve a higher system throughput than the adaptive resource adjustment (ARA) algorithm and the fixed power allocation methods. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article