Search Results (95)

A wheeled–legged robot has the advantage of stable and agile movement on flat ground and an excellent ability to overcome obstacles. However, when faced with a narrow footprint, there is a limit to its ability to move. We developed the control moment gyroscope (CMG) unicycle–legged robot to solve this problem. A scissored pair of CMGs was applied to control the roll balance, and the pitch balance was modeled as a double-inverted pendulum. We performed Linear Quadratic Regulator (LQR) control and model predictive control (MPC) in a system in which the control systems in the roll and pitch directions were separated. We also devised a method for controlling the rotation of the robot in the yaw direction using torque generated by the CMG, and the performance of these controllers was verified in the Gazebo simulator. In addition, forward driving control was performed to verify mobility, which is the main advantage of the wheeled–legged robot; it was confirmed that this control enabled the robot to pass through a narrow space of 0.15 m. Before implementing the verified controllers in the real world, we built a CMG test platform and confirmed that balancing control was maintained within $\pm 1^{\circ }$. Full article

Background: Human immunodeficiency virus and hypertension increasingly co-occur in South Africa. Despite publicly funded care, patients with multimorbidity face high out-of-pocket costs, yet limited evidence exists from the patient perspective. Purpose: To quantify the economic burden of comorbid HIV and hypertension, assess predictors of monthly out-of-pocket costs, and explore coping mechanisms. Methods: We conducted a cross-sectional analysis using patient-level data from the Mobile Phone Text Messages to Improve Hypertension Medication Adherence in Adults with HIV (MOPHADHIV trial) [Trial number: PACTR201811878799717], a randomized controlled trial evaluating short messages services adherence support for hypertension care in people with HIV. We calculated the monthly direct non-medical, indirect, and coping costs from a patient perspective, valuing indirect costs using both actual income and minimum wage assumptions. Generalized linear models with a gamma distribution and log link were used to identify cost determinants. Catastrophic expenditure thresholds (10–40% of monthly income) were assessed. Results: Among 683 participants, mean monthly total costs were ZAR 105.81 (USD 5.72) using actual income and ZAR 182.3 (USD 9.9) when valuing indirect costs by minimum wage. These time-related productivity losses constituted the largest share of overall expenses. Regression models revealed a strong income gradient: participants in the richest quintile incurred ZAR 131.9 (95% CI: 63.6–200.1) more per month than the poorest. However, this gradient diminished or reversed under standardized wage assumptions, suggesting a heavier proportional burden on middle-income groups. Other socio-demographic factors (gender, employment, education) not significantly associated with total costs, likely reflecting the broad reach of South Africa’s primary health system. Nearly half of the participants also reported resorting to coping mechanisms such as borrowing or asset sales. Conclusions: Comorbid HIV and hypertension impose substantial patient costs, predominantly indirect. Income disparities drive variation, raising equity concerns. Strengthening integrated human immunodeficiency virus—non-communicable diseases care and targeting financial support are key to advancing South Africa’s Universal Health Coverage reforms. Full article

3D object detection is a crucial technology in fields such as autonomous driving and robotics. As a direct representation of the 3D world, point cloud data plays a vital role in feature extraction and geometric representation. However, in real-world applications, point cloud data often suffers from occlusion, resulting in incomplete observations and degraded detection performance. Existing methods, such as PG-RCNN, generate semantic surface points within each Region of Interest (RoI) using a single grid size. However, a fixed grid scale cannot adequately capture multi-scale features. A grid that is too small may miss fine structures—especially problematic when dealing with small or sparse objects—while a grid that is too large may introduce excessive background noise, reducing the precision of feature representation. To address this issue, we propose an enhanced PG-RCNN architecture with a Multi-Scale Grid Attention Module as the core contribution. This module improves the expressiveness of point features by aggregating multi-scale information and dynamically weighting features from different grid resolutions. Using a simple linear transformation, we generate attention weights to guide the model to focus on regions that contribute more to object recognition, while effectively filtering out redundant noise. We evaluate our method on the KITTI 3D object detection validation set. Experimental results show that, compared to the original PG-RCNN, our approach improves performance on the Cyclist category by 2.66% and 2.54% in the Moderate and Hard settings, respectively. Additionally, our approach shows more stable performance on small object detection tasks, with an average improvement of 2.57%, validating the positive impact of the Multi-Scale Grid Attention Module on fine-grained geometric modeling, and highlighting the efficiency and generalizability of our model. Full article

Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article

Reanalysis air temperature data, characterized by temporal continuity but limited spatial resolution, are commonly downscaled to achieve higher spatial resolution to meet the demands of regional climatological studies and related research fields. However, when large spatial scale differences are involved, the adaptability of statistical downscaling models across different scales warrants further investigation. In this study, a stepwise downscaling method is proposed, employing multiple linear regression (MLR), Cubist regression tree, random forest (RF), and extreme gradient boosting (XGBoost) models to downscale the 3-hourly ERA5-Land reanalysis air temperature data at the resolution of 0.1° to that of 30 m. A comparative analysis was performed to evaluate the accuracy of downscaled ERA5-Land air temperature results obtained from the stepwise and the direct downscaling methods, based on observed air temperatures at meteorological stations and the spatial distribution of air temperature estimated by a remote sensing method. In addition, variations in the importance of driving factors across different spatial scales were examined. The results indicate that the stepwise downscaling method exhibits higher accuracy than the direct downscaling method, with a more pronounced performance improvement in winter. Compared with the direct downscaling method, the RMSE value of the MLR, Cubist, RF, and XGBoost models under the stepwise downscaling method were reduced by 0.48 K, 0.38 K, 0.48 K, and 0.50 K, respectively, at meteorological station locations. In terms of spatial distribution, the stepwise downscaling results demonstrate greater consistency with the estimated spatial distribution of air temperature, and it can capture air temperature variations across different land surface types more accurately. Furthermore, the stepwise downscaling method is capable of effectively capturing changes in the importance of driving factors across different spatial scales. These results generally suggest that the stepwise downscaling method can significantly improve the accuracy of air temperature downscaled from reanalysis data by adopting multiple resolutions as the intermediate downscaling process. Full article

Objective: Mitochondria drive cellular energy production and regulate key biological processes. High levels of heteroplasmic in mitochondrial DNA (mtDNA) variants can cause mitochondrial dysfunction and clinical symptoms. Third-generation sequencing overcomes the limitations of traditional mtDNA analysis methods, offering improved cost, throughput, and sensitivity. We developed an integrated approach for analyzing methylation patterns and genetic variations in mtDNA and ADME genes. Methods: We implemented Oxford Nanopore’s long-read sequencing with adaptive sampling (AS) to enrich enzymatically linearized mtDNA and absorption, distribution, metabolism, and excretion (ADME) genes without PCR amplification, enabling native sequencing in adipose-derived mesenchymal stem cells (AdMSC). Our custom algorithm preserved phase relationships between base modifications and sequence polymorphisms. Results: We identified differential methylation patterns in ADME genes correlating with specific genetic variants, suggesting epigenetic regulation of drug response. Adaptive sampling identifies a wider range of variant diversity, while whole genome sequencing (WGS) uncovers higher-frequency hotspots. Both methods offer complementary insights into mitochondrial heteroplasmy. In mtDNA, direct sequencing showed extensive hypomethylation, and low levels of non-CpG methylation were detected regardless of sequencing coverage depth. These sparse methylation patterns showed non-random distribution, correlating with functional regions and heteroplasmic sites. Conclusions: This study demonstrates the utility of adaptive sampling for the integrated analysis of mtDNA heteroplasmy and native base modifications, revealing widespread hypomethylation independent of coverage depth. The approach showcases the potential for combined pharmacoepigenomic and mitochondrial profiling in precision medicine, disease modeling, and therapeutic development. Full article

In the dual-carbon context, forestry green total factor productivity (FGTFP) serves as a key indicator of the quality and efficiency of forestry development. Based on New Economic Geography Theory, this study explores FGTFP and its spatial divergence under the constraint of carbon emissions. We analyzed panel data from 30 Chinese provinces between 2004 and 2022. The Directional Distance Function (DDF) model was applied to measure FGTFP, and the Global Malmquist–Luenberger (GML) model was applied to measure FGTFP’s decomposition index. The Dagum Gini coefficient was employed to analyze the degree of spatial divergence of FGTFP and identify its sources. Using Porter’s model and Sustainable Development Theory, the geo-detector was applied to examine the driving factors of FGTFP and its decomposition index. The study’s findings indicate that (1) FGTFP in China generally trended upward from 2004 to 2022, with significant heterogeneity observed at both interprovincial and regional levels; (2) Technological Improvement (TI) was the primary driver of FGTFP growth in the eastern, northeastern and central regions, while Efficiency Change (EC) was the key driver in the western region; (3) FGTFP exhibited distinct spatial divergence patterns in China, with hypervariable density as the primary source, followed by interregional differentiation, and regional differentiation contributing the least; and (4) green energy transition factors consistently showed a significant “two-factor enhancement effect” and a “non-linear enhancement trend”, while external environmental factors exhibited strong interaction effects but demonstrated a “non-linear weakening trend”. Therefore, it is essential to promote the green transformation of production modes, facilitate structural adjustments and upgrades in the forestry industry, enhance regional collaboration, and advance the “dual enhancement” of technological progress and efficiency. Additionally, leveraging regional comparative advantages will promote coordinated development. Full article

Net primary productivity (NPP) is a key indicator for assessing carbon fixation capacity. Understanding the mechanisms of carbon sequestration capacity of forest ecosystems is critical in the context of global climate change. Research on the influencing factors and driving mechanisms of NPP in forest areas of China is still insufficient, especially the lack of systematic analysis on the role of climate and phenology. Forest cover in China has been increasing in recent decades due to natural forest expansion and planted forests. It is significant to clarify the underlying drivers of the forest NPP in China. To address this issue, we collected annual NPP, biomass, phenology, temperature, and precipitation data in China from 2002 to 2021, then applied the general linear mixed effect model (GLMM) and Bayesian structural equation models to conduct a comprehensive analysis of the influencing factors of NPP. The results have shown that influencing factors all exert a significant positive influence on NPP through bivariate relationship analysis. The GLMM revealed that forest NPP was significantly positively affected by biomass, aridity index, temperature, and phenology. Among these, the aridity index (AI) (58.39%) and temperature (27.21%) were identified as having the highest contributions to NPP. The direct and indirect effects on NPP were evaluated using Bayesian structural equation models (SEMs), and the interactions between the factors and their comprehensive regulatory mechanisms on NPP were revealed. This study is crucial for understanding the impact of climate change on regulating forest carbon sequestration and providing strategies for effective forest management. Full article

With the advancement of lead–bismuth fast reactors, there has been increasing attention directed towards the design of and manufacturing technology for electromagnetic pumps employed to drive liquid lead–bismuth eutectic (LBE). These electromagnetic pumps are characterized by a simple structure, effective sealing, and ease of flow control. They exploit the excellent electrical conductivity of liquid metals, allowing the liquid metal to be propelled by Lorentz forces generated by the traveling magnetic field within the pump. To better understand the performance characteristics of electromagnetic pumps and master the techniques for integrated manufacturing and performance optimization, this study conducted fundamental research, development of key components, and the assembly of the complete pump. Consequently, an annular linear induction pump (ALIP) suitable for liquid lead–bismuth eutectic was developed. Additionally, within the lead–bismuth thermal experimental loop, startup and preheating experiments, performance tests, and flow-head experiments were conducted on this electromagnetic pump. The experimental results demonstrated that the output flow of the electromagnetic pump increased linearly with the input current. When the input current reached 99 A, the loop achieved a maximum flow rate of 8 m³/h. The efficiency of the electromagnetic pump also increased with the input current, with a maximum efficiency of 5.96% during the experiments. Finally, by analyzing the relationship between the flow rate and the pressure difference of the electromagnetic pump, a flow-head model specifically applicable to lead–bismuth electromagnetic pumps was established. Full article

The research focuses on the methodology of preparing a concept and application scenarios for alternative sources of energy in transportation. The ideas and interpretations are not strictly limited to automobile transport but also reach into other areas of using and processing energy. The conceptual approach to the choice of sources, with the aim of securing the efficient use of energy conversion, is illustrated on the model embodiment embedded in a passenger car; the relevant presentation is centered on an experiment focusing on the linear arrangement of a driven electromagnetic generator. This involves generating and collecting energy for not only the accumulation of electrical energy using relatively independent systems, but also for direct use within driving needs. In the modeled example, the supplied energy is assumed to be in a range of constant power from p = 10 W to 50 kW (200 kW). The given example of the design of the choice of energy conversion sources and the use of generators in a passenger car shows the possibilities, limitations, and variants for demonstrating the requirements relating to a simple driving mode. The application of a linear or cylindrical internal combustion engine is considered for a specific set mode of the car. Variants of suitable uses of the accumulated energy in compressed air are proposed. The use of light and thermal forms of energy is considered for additional forms. As an experimental example, the use of generators derived from vibration harvesters is shown. The proposed energy generation arrangement can be controlled and optimized for specific transport tasks. The generation and accumulation of energy can be employed in the form of electrical energy, as kinetic energy for direct use in driving, or to accumulate in compressed air for later use. Solar energy can be used directly or can be accumulated. The combustion unit can serve as a source of kinetic energy or also to store energy for further use. The concept of alternative sources is based on known methods of use in other industries. The model combination of resources and its simple analysis in the concept of resource selection is demonstrated on an example of an application in passenger cars. Full article

Linear spatial data are primarily used in Geographic Information Systems (GISs) to represent spatial data in the form of roads, rivers, railways, and utility lines. Linear spatial data are mostly composed of one-dimensional linear elements, incorporating geometric attributes such as location, direction, and length, as well as the interconnections of these elements. In the case of roads, this information is used to map and analyze traffic data, such as vehicle movements, on the road network. This study aims to propose an area-based spatial analysis method that allows for the flexible application of analysis scales using individual vehicle data, as opposed to node and link generation for linear road networks. The analysis focused on nine expressways, conducting a microscopic analysis of speed-homogeneous sections. The final analysis showed that out of 375 cells, 91 cells in the final 12 division cells did not meet the homogeneity criteria. This discrepancy was ascertained to be due to vehicles decelerating or accelerating when entering or exiting highways at ramps or interchanges, not due to directional speed differences but lane-specific speed variations. The final cells with large speed deviations were found to be influenced by connections to highway on-ramps or off-ramps. In contrast, sections with small speed variations within a cell were influenced by traffic factors such as connection points and traffic volume, which hindered normal driving. As a result, this study validated that traffic information from highways, typically provided as linear data, could be divided into cells based on real-time GPS speed data and presented on an area-based scale. While dividing regions based on fixed intervals does not pinpoint exact speed change points, this study found that reasonable segmentation is possible based on spatial size and speed-homogeneous sections. Full article

Dual-port direct drive wave energy power generation systems (DP-DDWEPGS) have received widespread attention due to their smooth and zero-free output power, compared to single-port direct drive wave energy power generation systems (SP-DDWEPGS) which have the disadvantage of large out-put power fluctuations. To further enhance the performance of the DP-DDWEPGS, optimal power capture control is proposed to achieve maximum power point tracking. Meanwhile, a multiport converter is applied to the DP-DDWEPGS to solve the problem caused by an excessive number of switching devices in the overall system converter. The multiport converter fulfills all the functional requirements of the DP-DDWEPGS while reducing the number of switching devices. However, switch multiplexing of the multiport converter also introduces coupling relationships between each port and the wave force exhibits time-varying characteristics, necessitating advanced control methods with superior fast-tracking capability. Therefore, in this paper, a decoupling duty cycle optimization model predictive control for DP-DDWEPGS is proposed. Based on the characteristics of switching multiplexing, NSC finite control set model predictive control (FCS-MPC) decouples the current prediction and the cost function, reduces the number of candidate voltage vectors in each operation, and shortens the operation time by 70%. To address the issues of high ripple value and increased error due to decoupling in FCS-MPC, duty cycle optimization control is added, greatly reducing the fluctuations in electromagnetic force and power of the permanent magnet linear generator (PMLG). Based on the established simulation model, the feasibility and superiority of the multiport converter and decoupling duty cycle optimization model predictive current control method are verified. Full article

In the face of depleting fossil energy and the imperative of sustainable development, there is a compelling drive towards advancing renewable energies. In this context, sustainable and predictable alternatives, like marine energy, gain prominence. Marine energy presents a cleaner option devoid of the adverse effects associated with fossil fuels, playing a crucial role in environmental sustainability by safeguarding coastlines against erosion. This study focuses on Astara Port in the Caspian Sea, exploring the utilization of wave energy converters (WECs). The originality of this study’s research lies in exploring WECs’ dual role in energy generation and coastal protection. Using MIKE21 software simulations, the impact of number, location, arrangement, and orientation of WECs across various scenarios was investigated, including two WEC number scenarios (11 and 13), three structural placement scenarios (north, front, and south of the port), two structural arrangement scenarios (linear and staggered), two port layout scenarios (original layout and modified layout), and two orientation scenarios for the structures (facing north-east, which is the dominant wave direction, and facing southeast). The results show a remarkable decrease in the significant wave height behind WECs, notably with 13 staggered devices facing dominant waves (from northeast), reducing the significant wave height Hs by 23–25%. This setup also shows the highest wave height reduction, notably 36.26% during a storm event. However, linear WEC setup offers more extensive coastline protection, covering 47.88% of the model boundary during storms. Furthermore, the 11 staggered WECs facing southeast (SE) arrangement had the lowest sediment accumulation at 0.0358 m over one year, showing effective sedimentation mitigation potential. Conversely, the 13 linear WECs facing northeast (NE) had the highest accumulation at 0.1231 m. Finally, the proposed port design redirects high-velocity flow away from the port entrance and removes rotatory flow, reducing sediment accumulation near the harbor entrance. Full article

Ocean wave energy is a new type of clean energy. To improve the power generation and wave energy conversion efficiency of the direct-drive wave power generation system, by addressing the issue of large output errors and poor system stability commonly associated with the currently used PID (proportional, integral, and derivative) control methods, this paper proposes a maximum power control method based on BP (back propagation) neural network PID control. Combined with Kalman filtering, this method not only achieves maximum power tracking but also reduces output ripple and tracking error, thereby enhancing the system’s control quality. This study uses a permanent magnet linear generator as the power generation device, establishes a system dynamics model, and predicts the main frequency of irregular waves through the Fast Fourier Transform method. It designs a desired current tracking curve that meets the maximum power strategy. On this basis, a comparative analysis of the control accuracy and stability of three control methods is conducted. The simulation results show that the BP neural network PID control method improves power generation and exhibits better accuracy and stability. Full article

Several strategies for navigation in unfamiliar environments have been explored, notably leveraging advanced sensors and control algorithms for obstacle recognition in autonomous vehicles. This study introduces a novel approach featuring a redesigned joystick equipped with stepper motors and linear drives, facilitating WiFi communication with a four-wheel omnidirectional electric vehicle. The system’s drive units integrated into the joystick and the encompassing control algorithms are thoroughly examined, including analysis of stick deflection measurement and inter-component communication within the joystick assembly. Unlike conventional setups in which the joystick is tilted by the operator, two independent linear drives are employed to generate ample tensile force, effectively “overpowering” the operator’s input. Running on a Raspberry Pi, the software utilizes Python programming to enable joystick tilt control and to transmit orientation and axis deflection data to an Arduino unit. A fundamental haptic effect is achieved by elevating the minimum pressure required to deflect the joystick rod. Test measurements encompass detection of obstacles along the primary directions perpendicular to the electric vehicle’s trajectory, determination of the maximum achievable speed, and evaluation of the joystick’s maximum operational range within an illuminated environment. Full article