Search Results (107,284)

The integration of 5G technology with cloud and edge computing is redefining the digital landscape by enabling ultra-fast connectivity, low-latency communication, and scalable solutions across diverse application domains. This paper investigates the synergistic impact of 5G on cloud-to-edge architectures, emphasizing its transformative role in revolutionizing sectors such as healthcare, smart cities, industrial automation, and autonomous systems. Key advancements in 5G—including Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), and Massive Machine-Type Communications (mMTC)—are examined for their role in enabling real-time data processing, edge intelligence, and IoT scalability. In addition to conceptual analysis, the paper presents simulation-based evaluations comparing 5G cloud-to-edge systems with traditional 4G cloud models. Quantitative results demonstrate significant improvements in latency, energy efficiency, reliability, and AI prediction accuracy. The study also explores challenges in infrastructure deployment, cybersecurity, and latency management while highlighting the growing opportunities for innovation in AI-driven automation and immersive consumer technologies. Future research directions are outlined, focusing on energy-efficient designs, advanced security mechanisms, and equitable access to 5G infrastructure. Overall, this study offers comprehensive insights and performance benchmarks that will serve as a valuable resource for researchers and practitioners working to advance next-generation digital ecosystems. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

The MA₂Z₄ family represents a class of two-dimensional materials renowned for their outstanding mechanical properties and excellent environmental stability. By means of elemental substitution, we designed two novel phases of ScSi₂N₄, namely β₁ and β₂. Their dynamical, thermal, and mechanical stabilities were thoroughly verified through phonon dispersion analysis, ab initio molecular dynamics (AIMD) simulations, and calculations of mechanical parameters such as Young’s modulus and Poisson’s ratio. Electronic structure analysis using both PBE and HSE06 methods further revealed that both the β₁ and β₂ phases exhibit metallic behavior, highlighting their potential for battery-related applications. Based on these outstanding properties, the climbing image nudged elastic band (CI-NEB) method was employed to investigate the diffusion behavior of Li, Na, and K ions on the material surfaces. Both structures demonstrate extremely low diffusion energy barriers (Li: 0.38 eV, Na: 0.22 eV, K: 0.12 eV), indicating rapid ion migration—especially for K—and excellent rate performance. The lowest barrier for K ions (0.12 eV) suggests the fastest diffusion kinetics, making it particularly suitable for high-power potassium-ion batteries. The significantly lower barrier for Na ions (0.22 eV) compared with Li (0.38 eV) implies that both β₁ and β₂ phases may be more favorable for fast-charging/discharging sodium-ion battery applications. First-principles calculations were applied to determine the open-circuit voltage (OCV) of the battery materials. The β₂ phase exhibits a higher OCV in Li/Na systems, while the β₁ phase shows more prominent voltage for K. The results demonstrate that both phases possess high theoretical capacities and suitable OCVs. Full article

Lumbar disc herniation is one of the primary causes of lower back pain, and its incidence has significantly increased with the development of industrialization. To assist in rehabilitation therapy, this paper proposes a flexible exoskeleton for active lumbar rehabilitation based on a 4-SPU/SP biomimetic parallel mechanism. By analyzing the anatomical structure and movement mechanisms of the lumbar spine, a four degree of freedom parallel mechanism was designed to mimic the three-axis rotation of the lumbar spine around the coronal, sagittal, and vertical axes, as well as movement along the z-axis. Using a 3D motion capture system, data on the range of motion of the lumbar spine was obtained to guide the structural design of the exoskeleton. Using the vector chain method, the display equations for the drive joints of the mechanism were derived, and forward and inverse kinematic models were established and simulated to verify their accuracy. The dynamic characteristics of the biomimetic parallel mechanism were analyzed and simulated to provide a theoretical basis for the design of the exoskeleton control system. A prototype was fabricated and tested to evaluate its maximum range of motion and workspace. Experimental results showed that after wearing the exoskeleton, the lumbar spine’s range of motion could still reach over 83.5% of the state without the exoskeleton, and its workspace could meet the lumbar spine movement requirements for daily life, verifying the rationality and feasibility of the proposed 4-SPU/SP biomimetic parallel mechanism design. Full article

Due to their low permeability, the location of natural fractures is key to the successful development of buried hill reservoirs. Due to the high degree of rock fragmentation and strong absorption of seismic waves at the top of buried hill formations, it is hard to identify the distribution of natural fractures inside a buried hill using conventional seismic methods. To overcome this difficulty, this study proposes a natural fracture identification technology for buried hill reservoirs that combines a continuum damage mechanics model with finite element numerical simulation. A 3D numerical solution workflow is established to determine the natural fracture distribution in target buried hill reservoirs. By constructing a geological model of a block, reconstructing the orogenic history, developing a 3D finite element model, and performing numerical simulations, the multi-stage orogenic processes experienced by buried hill reservoirs and the resultant natural fracture formation are replicated. This approach yields 3D numerical results of natural fracture distribution. Using the G-Block in the Zhongyuan Oilfield as a case study, the natural fracture distribution in a buried hill reservoir composed of mixed lithologies, including marble and Carboniferous formations, within the faulted G6-well group is analyzed. The results include plane views of the contour of damage variable SDEG, which represents the fracture distribution within the subsurface layer at 600 m intervals below the buried hill surface, as well as a vertical sectional view of the contour of SDEG’s distribution along specified well trajectories. By comparison with the results of the fracture distribution obtained with logging data, a consistency of 87.5% is achieved. This indicates the reliability of the numerical results for natural fractures obtained using the technology presented here. Full article

Vibration-based structural health monitoring methods have been widely applied in the field of damage identification. This paper proposes an intelligent diagnostic approach that integrates a multi-scale convolutional neural network with a multi-head attention mechanism (MSCNN-MHA) for the structural health monitoring of jacket-type offshore platforms. Through numerical simulations, acceleration response signals of three-pile and four-pile jacket platforms under random wave excitation are analyzed. Damage localization studies are conducted under simulated crack and pitting corrosion cases. Unlike previous studies that often idealize damage by weakening structural parameters or removing components, this study focuses on small-scale damage forms to better reflect real engineering conditions. To verify the noise resistance of the proposed method, noise is added to the original signals for further testing. Finally, experiments are conducted on the basic structure of the jacket-type offshore platform, simulating small-scale crack and pitting damage under sinusoidal and pulse excitation, to further evaluate the applicability of the method. Compared to previous CNN and MSCNN-based approaches, the results of this study demonstrate that the MSCNN-MHA method achieves higher accuracy in identifying and locating minor damage in jacket-type offshore platforms. Full article

The dynamic behavior of quartz flexure accelerometers remains a subject of ongoing investigation, particularly in areas such as theoretical modeling, standardization, calibration methodology, and performance evaluation. To address the limitation of conventional static calibration models in accurately representing accelerometer responses under dynamic acceleration excitation, a dynamic calibration model is proposed. A mathematical model is first developed based on the physical mechanism of the accelerometer, characterizing its intrinsic dynamic response. Simulation-based analysis demonstrates that the proposed dynamic model offers significantly improved accuracy compared to traditional static approaches. Furthermore, a dynamic calibration method leveraging a dual-axis precision centrifuge is designed and validated. The results confirm that the proposed approach enables the precise calibration of quartz flexure accelerometers in accordance with the dynamic model. The calibration of the dynamic parameter yields a relative standard deviation of −0.048%. Full article

Microseismic monitoring has become a critical technology for hydraulic fracturing in unconventional oil and gas reservoirs, owing to its high temporal and spatial resolution. It plays a pivotal role in tracking fracture propagation and evaluating stimulation effectiveness. However, the automatic picking of first-arrival times and accurate source localization remain challenging under complex noise conditions, which constrain the reliability of fracture parameter inversion and reservoir assessment. To address these limitations, we propose a hybrid approach that combines optimized variational mode decomposition (OVMD), wavelet thresholding denoising (WTD), and an adaptive fractal box-counting dimension algorithm for enhanced first-arrival picking and source localization. Specifically, OVMD is first employed to adaptively decompose seismic signals and isolate noise-dominated components. Subsequently, WTD is applied in the multi-scale frequency domain to suppress residual noise. An adaptive fractal dimension strategy is then utilized to detect change points and accurately determine the first-arrival time. These results are used as inputs to a particle swarm optimization (PSO) algorithm for source localization. Both numerical simulations and laboratory experiments demonstrate that the proposed method exhibits high robustness and localization accuracy under severe noise conditions. It significantly outperforms conventional approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT). The proposed framework offers reliable technical support for dynamic fracture monitoring, detailed reservoir characterization, and risk mitigation in the development of unconventional reservoirs. Full article

This paper presents a reinforcement learning (RL)-based methodology for the online fine-tuning of PD controller gains, with the goal of bridging the gap between simulation-trained controllers and real-world quadrotor applications. As a first step toward real-world implementation, the proposed approach applies a Deep Deterministic Policy Gradient (DDPG) algorithm—an off-policy actor–critic method—to adjust the gains of a quadrotor attitude PD controller during flight. The RL agent was initially trained offline in a simulated environment, using MATLAB/Simulink 2024a and the UAV Toolbox Support Package for PX4 Autopilots v1.14.0. The trained controller was then validated through both simulation and experimental flight tests. Comparative performance analyses were conducted between the hand-tuned and RL-tuned controllers. Our results demonstrate that the RL-based tuning method successfully adapts the controller gains in real time, leading to improved attitude tracking and reduced steady-state error. This study constitutes the first stage of a broader research effort investigating RL-based PID, LQR, MRAC, and Koopman-integrated RL-based PID controllers for real-time quadrotor control. Full article

Simulators are an effective tool for improving tram driver training. In urban rail transportation, the fidelity of reproducing the driver’s working environment is crucial due to the high diversity of vehicle models. This study presents a structured assessment model for evaluating the mapping of a tram driver’s console in a universal simulator. The model is based on expert judgment and utilizes fuzzy logic to evaluate four key criteria: perspective, button placement, functionality, and time required to locate safety buttons. A group of 30 experts, including experienced tram drivers and technical specialists, assessed the fidelity of the simulated consoles for three tram types: Solaris Tramino S105p, Moderus Gamma LF 06 AC, and Škoda 16T RK. The results enable the classification of console fidelity levels (low, moderate, high) and support the identification of design inconsistencies. The proposed model provides a standardized tool for assessing simulator realism, which can be applied by transport operators, manufacturers, and training centers to improve simulator configurations. Researchers may also use the model as a methodological framework for further evaluation studies involving human–machine interface fidelity. Full article

Controlling dry matter intake (DMI) is one strategy to reduce feed costs and increase efficiency. Including fat at a high concentrate level can increase the energy density of diets fed to ruminants, thus reducing DMI further. Therefore, the objective of this study was to evaluate the effects on fermentation and nutrient digestion of including different fat sources when high-concentrate diets with high-fat inclusion are used under simulating precision feeding in continuous culture. We hypothesized that incorporating different fat sources into the aforementioned program can improve nutrient utilization without affecting rumen fermentation. Four treatments were randomly assigned to eight continuous cultures in a randomized complete block design and ran for two periods of 10 d. Diets included a high concentrate level (HC; 65% DM) with high-fat inclusion starting with a 3% basal level of fat in the diet as the control (0% added fat; CON) and 9% fat in the diet (6% added poultry fat, PF; 6% added coconut oil, CO; and (6% added soybean oil, SO). Data were analyzed using the MIXED procedure of SAS with repeated measures. The DM, OM, NDF, and ADF digestibility coefficients (dCs) were higher for PF and CO, followed by SO and then CON. Starch and FA dCs were higher for different fat sources than for the CON. The total VFA concentration was higher for CON. There was a reduction in acetate and propionate with different fat sources. The mean culture pH and NH₃N were the highest for CO, followed by PF, then SO, and CON. The protozoa population was higher for CON than for the other fat treatments, followed by CO, PF, and SO. These results suggest that simulated precision feeding using continuous culture fermenters with high-concentrate diets up to 65% and high fat up to 6% can improve nutrient digestion approximately to 15% with changes in fermentation rate and profiles. Full article

Accurate parameter identification in dynamic models of robotic arms is essential for performing high-performance control and energy-efficient procedures. However, classic methods often encounter difficulties when modeling nonlinear, high-dimensional systems, particularly in the presence of real-world uncertainties. To address these challenges, this study focuses on identifying mass center positions and inertia matrix elements in a six-jointed industrial robotic arm and comparing the influence of optimized algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum-behaved Particle Swarm Optimization (QPSO). The robot’s kinematic model was validated by comparing it with actual motion data, utilizing a high-precision neural network to ensure accuracy before conducting a dynamic analysis. A comprehensive dynamic model was created using Computer-Aided Optimization (CAO) in SolidWorks Premium 2023 to simulate realistic mass parameters, thereby validating the model’s reliability in a practical setting. The real (Referenced) and optimized dynamic models of the robot arm were validated using trajectory tracking simulations under sliding mode control (SMC) to assess the impact of the optimized model on the robot’s performance metrics. Results indicate that QPSO estimates inertia and mass center parameters with Mean Absolute Percentage Errors (MAPE) of 0.76% and 0.43%, outperforming PSO significantly and delivering smoother torque profiles and greater resilience to external disturbances. Full article

Background: Primary implant stability is a critical factor for successful osseointegration and long-term implant success. This study investigates the impact of drilling protocol modifications on primary stability, considering different bone qualities and implant macro-designs, lengths, and diameters. Material and Methods: Three implant designs—two parallel-walled and one tapered—were tested with diameters ranging from 3.4 to 5.2 mm and lengths from 7.5 to 14.5 mm. Implants were placed in polyurethane foam blocks simulating different bone densities (10, 15, 25, and 35 PCF). A standard drilling protocol was used in all groups, with modifications based on bone quality: overpreparation in dense bone and underpreparation in softer bone. Primary stability was evaluated using insertion torque (IT). The optimal IT range was defined as 25–50 Ncm, based on clinical guidelines for immediate loading. The influence of drilling protocol adaptations on stability parameters was assessed. Results: Insertion torque was primarily influenced by bone density and implant diameter, with implant length playing a minor role. In dense bone (D1, D2), underpreparation improved torque values, especially in smaller implants, while overpreparation reduced them. The highest torques occurred with 5.2 mm implants, sometimes exceeding 80 Ncm. Standard protocols did not consistently achieve optimal torque across implant types. In soft bone (D3), underpreparation—particularly with tapered implants—was modestly beneficial. In very soft bone (D4), none of the protocols reliably reached the desired torque range. Conclusions: Adapting drilling protocols to bone density improves insertion torque, especially with wider implants and in denser bone. Underpreparation is generally more effective than overpreparation. However, in very soft bone, neither implant geometry nor drilling adaptations reliably achieve optimal primary stability, highlighting the need for additional strategies. Full article

Picea neoveitchii Mast., a critically endangered spruce species endemic to China, is classified as a national second-level key protected wild plant and listed as critically endangered (CR) on the International Union for Conservation of Nature (IUCN) Red List. Its habitat features complex forest light environments, and global climate change coupled with environmental pollution has increased regional nitrogen deposition, posing significant challenges to its survival. This study explores the effects of light–nitrogen interactions on the leaf functional traits of Picea neoveitchii Mast. seedlings by simulating combinations of light intensities (100%, 70%, and 40% full sunlight) and nitrogen application levels (0, 10, and 20 g N·m ⁻²·a⁻¹, where g N·m⁻²·a⁻¹ denotes grams of nitrogen applied per square meter per year). We examined changes in morphological traits, anatomical structures, photosynthetic physiology, and stress resistance traits. Results indicate that moderate shading (70% full sunlight) significantly enhances leaf morphological traits (e.g., leaf length, leaf area, and specific leaf area) and anatomical features (e.g., mesophyll tissue area and resin duct cavity area), improving light capture and stress resistance. Medium- to high-nitrogen treatments (10 or 20 g N·m⁻²·a⁻¹) under moderate shading further increase photosynthetic efficiency, stomatal conductance, and antioxidant enzyme activity. According to the comprehensive membership function evaluation, the L2N0 (70% full sunlight, 0 g N·m⁻²·a⁻¹) treatment exhibits the most balanced performance across both growth and stress-related traits. These findings underscore the critical role of light–nitrogen interactions in the growth and adaptability of Picea neoveitchii Mast. leaves, offering a scientific foundation for the conservation and ecological restoration of endangered plant populations. Full article

In recent years, computational intelligence techniques have significantly contributed to the automation and optimization of trading strategies. Despite the increasing sophistication of predictive models, classical technical indicators such as dual Simple Moving Averages (2-SMA) remain popular due to their simplicity and interpretability. This work proposes an adaptive trading system that combines the 2-SMA strategy with a learning-based metaheuristic optimizer known as the Learning-Based Linear Balancer ($L{B}^2$). The objective is to dynamically adjust the strategy’s parameters to maximize returns in the highly volatile cryptocurrency market. The proposed system is evaluated through simulations using historical data of the BTCUSDT futures contract from the Binance platform, incorporating real-world trading constraints such as transaction fees. The optimization process is validated over 34 training/test splits using overlapping 60-day windows. Results show that the $L{B}^2$-optimized strategy achieves an average return on investment (ROI) of 7.9% in unseen test periods, with a maximum ROI of 17.2% in the best case. Statistical analysis using the Wilcoxon Signed-Rank Test confirms that our approach significantly outperforms classical benchmarks, including Buy and Hold, Random Walk, and non-optimized 2-SMA. This study demonstrates that hybrid strategies combining classical indicators with adaptive optimization can achieve robust and consistent returns, making them a viable alternative to more complex predictive models in crypto-based financial environments. Full article