Search Results (2,343)

Background: Moderate physical activity (PA) exerts powerful systemic and neuroprotective effects, reducing chronic disease risk and enhancing cognitive and psychological well-being. PA promotes brain plasticity by upregulating neurotrophic factors and stimulating neurogenesis. Given the established role of Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) in neuronal survival, differentiation, and anti-apoptotic signaling, we aimed to investigate whether moderate PA modulates the endogenous expression of PACAP and its specific receptor PAC1R in the DG and cerebellar cortex. Methods: To this end, twenty-four rats were distributed into sedentary or exercise groups. Immunohistochemical and Western blot analyses were performed to assess PACAP and PAC1R expression. Co-expression with doublecortin (DCX), a marker of immature neurons, was evaluated to explore the direct relationship between PACAP signaling and neurogenesis. Results: Our results showed that moderate PA induced a significant up-regulation of PACAP and PAC1R in both the DG and cerebellar cortex compared to sedentary controls. Moreover, high co-expression of PACAP and DCX was detected in these regions, suggesting an involvement of PACAP in exercise-induced neurogenic processes. Conclusions: These findings demonstrate that moderate physical activity is associated with enhanced PACAP/PAC1R signaling and DCX expression in neurogenic regions, warranting further investigation into its specific contribution to exercise-induced brain plasticity. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article

A compact quad-band multiple-input multiple-output (MIMO) antenna for terahertz communications is presented in this work. The proposed antenna consists of a truncated square patch with inverted-U-shaped and C-shaped slots. The operating frequencies of the proposed antenna are 0.38 THz, 0.43 THz, 0.61 THz, and 0.7 THz, with reflection coefficients of −13.8 dB, −22.1 dB, −27.3 dB, and −14.8 dB, respectively, and a −10 dB impedance bandwidth of 9 GHz, 18 GHz, 18 GHz, and 21 GHz, respectively. The peak gain values of a single element antenna at 0.38 THz, 0.43 THz, 0.61 THz, and 0.7 THz are 3.3 dB, 4.8 dB, 4.7 dB, and 5.5 dB, respectively. The dual-triangular MIMO configuration was investigated. The peak gains of the MIMO configurations at 0.38 THz, 0.43 THz, 0.61 THz, and 0.7 THz are 10.6 dB, 12.2 dB, 15.6 dB, and 15.2 dB, respectively. The envelope correlation coefficient (ECC) and the diversity gain (DG) of the proposed antenna were investigated and are presented herein. The proposed MIMO antenna demonstrates lower coupling and higher isolation at the operating frequency bands. Therefore, it is a suitable candidate for beyond 5G and 6G wireless communications applications, such as for nanodevices used in the internet of things and in wearables. Full article

Background: Early molecular changes on the facial skin surface during early adulthood remain insufficiently characterized. We integrated biophysical readouts with untargeted skin surface lipid (SSL) profiling to identify region-dependent, age-associated features in women with combination skin. Methods: Eighty healthy Chinese women were stratified into 22–28 years (n = 40) and 29–35 years (n = 40). Sebum was measured on the cheek and forehead; cheek elasticity, hydration (CM), transepidermal water loss (TEWL), pH, and tone indices were assessed under standardized conditions. SSLs from both regions were profiled by UPLC–QTOF–MS. Differential features were prioritized using OPLS-DA (VIP > 1) with univariate screening (p < 0.05; fold change > 2 or <0.5). Results: TEWL, CM, and pH were comparable between age groups, whereas the older group showed lower cheek elasticity and reduced sebum. Lipidomics revealed clearer remodeling on the cheek than the forehead: 30 and 59 differential SSL features were identified in the cheek and forehead, respectively. Cheek changes in the older group were characterized by lower ceramides (including acylceramides), TG/DG and long-chain fatty acids, alongside relatively higher cholesteryl esters. Conclusions: Conventional barrier indices remained largely stable across this age window, while cheek SSL profiles captured earlier molecular shifts, providing candidates for targeted validation and longitudinal follow-up. Full article

As distributed generators (DGs) and flexible adjustable loads (FALs) further penetrate distribution networks (DNs), to reduce regulation complexity compared with traditional centralized control frameworks, DGs and FALs in DNs should be packed in several clusters to enable their dispatch to become standard in the industry. To mitigate the negative influence of DGs’ and FALs’ spatiotemporal distribution and uncertain output characteristics on dispatch, this paper proposes an intelligent dynamic cluster partitioning strategy for DNs, from which the DN’s resources and loads can be intelligently aggregated, organized, and regulated in a dynamic and optimal way with relatively high implementation efficiency. An environmental model based on the Markov decision process (MDP) technique is first developed for DN cluster partitioning, in which a continuous state space, a discrete action space, and a dispatching performance-oriented reward are designed. Then, a novel random forest Q-learning network (RF-QN) is developed to implement dynamic cluster partitioning by interacting with the proposed environmental model, from which the generalization and robust capability to estimate the Q-function can be improved by taking advantage of combining deep learning and decision trees. Finally, a modified IEEE-33-node system is adopted to verify the effectiveness of the proposed intelligent dynamic cluster partitioning and regulation strategy; the results also indicate that the proposed RF-QN is superior to the traditional deep Q-learning (DQN) model in terms of renewable energy accommodation rate, training efficiency, and portioning and regulation performance. Full article

Marine by-products represent an important source of bioactive lipids with potential applications in nutraceuticals and functional foods. This study provides a biochemical and lipidomic characterization of oils derived from sardine, monkfish, grey mullet roe, squid, and anchovy by-products, assessing how the extraction method influences their lipid and antioxidant profiles. Fatty acids were quantified by GC-FID, antioxidant compounds by HPLC-DAD, and untargeted lipidomics by TIMS-HRMS. A total of 228 lipid species were identified, predominantly triglycerides (TGs) and diglycerides (DGs), accounting for approximately 69% of the annotated lipidome. Grey mullet roe oils exhibited the highest levels of long-chain PUFAs (EPA, DHA) and antioxidants (α-tocopherol 205–469 mg/Kg, lutein 10–125 mg/Kg, and squalene 1004–6049 mg/Kg), whereas squid oils showed high n-3/n-6 proportions. The extraction method strongly affected lipid integrity. Supercritical CO₂ extraction with ethanol (SFE–SE) preserved the greatest proportion of PUFA-rich TGs, yielding ~27–28 g EPA + DHA per 100 g oil, while wet reduction and mechanical pressing produced lower PUFA levels (~22 g/100 g) and increased hydrolysis/oxidation-associated lipids. PCA and PLS-DA revealed clear clustering driven by species and extraction class, with PUFA-containing TGs and DGs identified as major discriminating lipids. These results highlight the critical role of extraction conditions in determining the nutritional and functional value of marine oils and support the valorization of marine by-products in high-value applications. Full article

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications. Full article

Wireless Power Transfer (WPT) has become a pivotal technology, enabling the battery-free operation of Internet of Things (IoT) and biomedical devices while supporting environmental sustainability. This review provides a comprehensive analysis of microstrip patch antennas (MPAs) operating at the 5.8 GHz Industrial, Scientific, and Medical (ISM) band, emphasizing their advantages over the more commonly used 2.4 GHz band. A detailed and systematic classification framework for MPA architectures is introduced, covering single-element, multi-band, ultra-wideband, array, MIMO, wearable, and rectenna systems. The review examines advanced optimization methodologies, including Defected Ground Structures (DGS), Electromagnetic Bandgap (EBG) structures, Metamaterials (MTM), Machine Learning (ML), and nanomaterials, each contributing to improvements in gain, bandwidth, efficiency, and device miniaturization. Unlike previous surveys, this work offers a performance-benchmarked classification specifically for 5.8 GHz MPAs and provides a quantitative assessment of key trade-offs, such as efficiency versus substrate cost. The review also advocates for a shift toward Power Conversion Efficiency (PCE)-centric co-design strategies. The analysis identifies critical research gaps, particularly the ongoing disparity between simulated and experimental performance. The review concludes by recommending multi-objective optimization, integrated antenna-rectifier co-design to maximize PCE, and the use of advanced materials and computational intelligence to advance next-generation, high-efficiency 5.8 GHz WPT systems. Full article

Objectives: This study aimed to assess the intestinal permeation behaviors of andrographolide (AG) and 14-deoxy-11,12-didehydroandrographolide (DDAG), diterpene lactones from Andrographis paniculata extract (APE), pure AG, and three distinct source APEs. The effects of different solvents were also investigated. Methods: Solubility investigation was performed using APE. APEs and pure AG were prepared as liqui-masses, cohesive mixtures of APE, solvents, and solid carriers. PXRD, in vitro release, and ex vivo intestinal permeation using the non-everted gut sac method were investigated. Results: Solubility of AG and DDAG in N-methyl-2-pyrrolidone (NMP) > NMP/diethylene glycol monoethyl ether (DG) mixtures > DG. PXRD indicated that crystallinity loss of liqui-mass was affected by solvent’s solvency capacity. The release behaviors of AG and DDAG in phosphate buffer from pure AG and APEs varied depending on their solid state. The release efficiencies of AG and DDAG from liqui-mass systems increased significantly. The apparent permeability (P_app) of AG from pure AG was 0.11 ± 0.05 ×10⁻⁵ cm·s⁻¹, which was 11–25 times less than that of APEs. The P_app of DDAG from various APEs was comparable, ranging between 5.95 and 7.37 × 10⁻⁵ cm·s⁻¹. The presence of a solvent, specifically NMP, in liqui-mass significantly enhanced the release rate and permeation flux. The P_app of AG and DDAG from liqui-mass increased by factors of 1.0–2.3 and 1.1–2.7, respectively. Conclusions: This study is the first to emphasize the differences in the release and intestinal permeation characteristics of AG and DDAG from APEs. These findings offer essential insights into the intestinal permeation behavior of diterpene lactones, along with a straightforward mechanistic strategy for enhancement. Full article

The increasing demand for energy combined with depleting conventional energy sources has led to the evolution of distributed generation using renewable energy sources. Integrating these distributed generations with the existing grid is a complicated task, as it risks the stability and synchronisation of the system. Microgrids (MG) have evolved as a concrete solution for integrating these DGs into the existing system with the ability to operate in either grid-connected or islanded modes, thereby improving reliability and increasing grid functionality. However, owing to the intermittent nature of renewable energy sources, managing the energy balance and its coordination with the grid is a strenuous task. The hierarchical control structure paves the way for managing the dynamic performance of MGs, including economic aspects. However, this structure lacks the ability to provide effective solutions because of the increased complexity and system dynamics. The incorporation of artificial intelligence techniques for the control of MG has been gaining attention for the past decade to enhance its functionality and operation. Therefore, this paper presents a critical review of various artificial intelligence (AI) techniques that have been implemented for the hierarchical control of MGs and their significance, along with the basic control strategy. Full article

This study addresses the practical demand for facial recognition of pigs in the food safety and insurance industries, tackling the challenge of low recognition accuracy caused by complex farming environments, occlusions, and similar textures. To this end, we propose an enhanced model, DGS-YOLO, based on YOLOv11n, designed to achieve precise facial recognition of group-raised young pigs. The core improvements of the model include the following: (1) replacing standard convolutions with dynamic convolutions (DMConv) to enhance the network’s adaptive extraction capability for critical detail features; (2) designing a C3k2_GBC module with a bottleneck structure to replace the C3k2 neck, enabling more efficient capture of multi-scale contextual information; (3) introducing the SimAM parameter-free attention mechanism to optimize feature focusing; (4) employing the Shape-IoU loss function to mitigate the impact of bounding box geometry on regression accuracy. Experiments on self-built datasets demonstrate that DGS-YOLO achieves 4%, 2.1%, and 2.3% improvements in accuracy, recall, and mAP50, respectively, compared to the baseline model YOLOv11n. Furthermore, its overall performance surpasses that of Faster R-CNN and SSD in comprehensive evaluation metrics. Especially in limited sample scenarios, the model demonstrates strong generalization ability, with accuracy and mAP50 further increased by 20.1% and 10.3%. This study provides a highly accurate and robust solution for animal facial recognition in complex scenarios. Full article

Background: Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder characterized by cutaneous and subcutaneous neurofibromas, which impact quality of life. Dermoscopy-guided high-frequency ultrasound (DG-HFUS) integrates dermoscopy with 33 MHz ultrasound, enabling precise lesion localization and reproducible measurements. Objective: To characterize neurofibromas in NF1 patients using DG-HFUS and identify imaging parameters for diagnosis, monitoring, and treatment planning. Methods: 14 genetically confirmed NF1 patients underwent DG-HFUS imaging (Dermus SkinScanner). 100 neurofibromas were assessed for size, location, shape, contours, surface, echogenicity, global echogenicity, and posterior acoustic features. Results: Lesions were dermal (79%) or subcutaneous (21%), round (28%), ovoid (63%), or spiked (9%). Mean vertical and lateral diameters were 5.37 ± 2.66 mm and 2.28 ± 1.39 mm. All were hypoechoic; 62% homogeneous, 38% heterogeneous. Margins were well-defined in 57% and poorly defined in 43%. Posterior enhancement occurred in 3% and shadowing in 10%. Conclusions: DG-HFUS provides a detailed, reproducible assessment of neurofibromas, supporting differential diagnosis, surgical planning, and longitudinal monitoring. The evaluated imaging parameters offer objective insights for optimizing NF1 management. Future developments, including 3D reconstruction and AI-assisted analysis, may further enhance its clinical utility. Full article

This paper presents an intelligent operational strategy that performs the coordinated dispatch of active and reactive power from PV distributed generators (PV DGs) and Distributed Static Compensators (D-STATCOMs) to support secure and economical operation of active distribution networks. The problem is formulated as a nonlinear optimization problem that explicitly represents the P and Q control capabilities of Distributed Energy Resources (DER), encompassing small-scale generation and compensation units connected at the distribution level, such as PV generators and D-STATCOM devices, adjusting their reference power setpoints to minimize daily operating costs, including energy purchasing and DER maintenance, while satisfying device power limits and the voltage and current constraints of the grid. To solve this problem efficiently, a parallel version of the Population Continuous Genetic Algorithm (CGA) is implemented, enabling simultaneous evaluation of candidate solutions and significantly reducing computational time. The strategy is assessed on the 33- and 69-node benchmark systems under deterministic and uncertainty scenarios derived from real demand and solar-generation profiles from a Colombian region. In all cases, the proposed approach achieved the lowest operating cost, outperforming state-of-the-art metaheuristics such as Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA), while maintaining power limits, voltages and line currents within secure ranges, exhibiting excellent repeatability with standard deviations close to 0.0090%, and reducing execution time by more than 68% compared with its sequential counterpart. The main contributions of this work are: a unified optimization model for joint P–Q control in PV and D–STATCOM units, a robust codification mechanism that ensures stable convergence under variability, and a parallel evolutionary framework that delivers optimal, repeatable, and computationally efficient energy management in distribution networks subject to realistic operating uncertainty. Full article

Collaborative sensing between low-altitude remote sensing and ground-based mobile mapping lays the theoretical foundation for multi-platform 3D data fusion. However, point clouds collected from Airborne Laser Scanners (ALSs) remain scarce due to high acquisition and annotation costs. In contrast, while autonomous driving datasets are more accessible, dense annotation remains a significant bottleneck. To address this, we propose Ground to Altitude (GTA), a weakly supervised domain generalization (DG) framework. GTA leverages sparse autonomous driving data to learn robust representations, enabling reliable segmentation on airborne point clouds under zero-label conditions. Specifically, we tackle cross-platform discrepancies through progressive domain-aware augmentation (PDA) and cross-scale semantic alignment (CSA). For PDA, we design a distance-guided dynamic upsampling strategy to approximate airborne point density and a cross-view augmentation scheme to model viewpoint variations. For CSA, we impose cross-domain feature consistency and contrastive regularization to enhance robustness against perturbations. A progressive training pipeline is further employed to maximize the utility of limited annotations and abundant unlabeled data. Our study reveals the limitations of existing DG methods in cross-platform scenarios. Extensive experiments demonstrate that GTA achieves state-of-the-art (SOTA) performance. Notably, under the challenging 0.1% supervision setting, our method achieves a 6.36% improvement in mIoU over the baseline on the SemanticKITTI → DALES benchmark, demonstrating significant gains across diverse categories beyond just structural objects. Full article

Automated insulin delivery (AID) systems depend on their actuators’ behavior since saturation limits, rate constraints, and hardware degradation directly affect the stability and safety of glycemic regulation. In this paper, we conducted an actuator-centric evaluation of five control strategies: Nonlinear Model Predictive Control (NMPC), Linear MPC (LMPC), Adaptive MPC (AMPC), Proportional-Integral-Derivative (PID), and Linear Quadratic Regulator (LQR) in three physiologically realistic scenarios: the first combines exercise and sensor noise to test for stress robustness; the second tightens the actuation constraints to provoke saturation; and the third models partial degradation of an insulin actuator in order to quantify fault tolerance. We have simulated a full virtual cohort under the two-actuator configurations, DG3.2 and DG4.0, in an effort to investigate generation-to-generation consistency. The results detail differences in the way controllers distribute insulin and glucagon effort, manage rate limits, and handle saturation: NMPC shows persistently tighter control with fewer rate-limit violations in both DG3.2 and DG4.0, whereas the classical controllers are prone to sustained saturation episodes and delayed settling under hard disturbances. In response to actuator degradation, NMPC suffers smaller losses in insulin effort with limited TIR losses, whereas both PID and LQR show increased variability and overshoot. This comparative analysis yields fundamental insights into important trade-offs between robustness, efficiency, and hardware stress and demonstrates that actuator-aware control design is essential for next-generation AID systems. Such findings position MPC-based algorithms as leading candidates for future development of actuator-limited medical devices and deliver important actionable insights into actuator modeling, calibration, and controller tuning during clinical development. Full article