Search Results (157)

In order to clarify the controlling effects of tectonic activity on hydrocarbon migration efficiency in the Permian–Triassic strata of the Luzhou area, Sichuan Basin, this study takes faults as the research objective. Using 3D seismic data, tectonic evolution records, and single-well test data, we systematically analyze the geometric characteristics, activity phases, classification by grade and type, and reservoir-controlling effects of faults. The results show that a total of 843 reverse faults have been identified in the study area. The major faults are distributed in a NE-SW trend, with eight planar combination styles developed, and the main cross-sectional styles are back-thrust and “Y”-shaped types. The faults experienced four phases of tectonic activity: Caledonian, Hercynian, Indosinian, and Yanshan–Himalayan. Among these, the Indosinian phase is the key formative phase, effectively connecting the source rocks and reservoirs. The faults are classified into three grades and four categories: source-connected faults, reservoir-modifying faults, damaging faults, and source-connected and damaging faults. Migration efficiency is jointly controlled by fault grade, activity phases, and the penetrated formations. Among them, third-order source-connected faults formed during the Indosinian phase exhibit the highest migration efficiency, while first-order damaging faults formed during the Yanshan phase tend to cause hydrocarbon dissipation. This study can provide a reference for hydrocarbon exploration and the prediction of favorable areas in the Luzhou area. Full article

Based on outer raceway control theory and a five-degree-of-freedom quasi-static model of angular contact ball bearings, a raceway wear model considering curvature radius variation is proposed, which couples the quasi-static model with a modified Archard wear formulation and a dynamic curvature radius update mechanism. As wear accumulates, the worn curvature radii are fed back into the quasi-static model to recalculate the raceway contact dynamic parameters. Taking the SKF 7012ACE/HCP4A spindle bearing as an example, the wear depth evolution and the variations of contact ellipse area, contact stress, sliding velocity, and wear coefficient with wear time are investigated under combined loads. The results indicate that as wear progresses, the raceway curvature radii increase, leading to a decrease in contact ellipse area but an increase in contact stress and sliding velocity, which in turn accelerates the wear process. The findings demonstrate that the degradation of raceway curvature radius has a cumulative and non-negligible influence on wear evolution and should be incorporated into bearing wear calculations for more accurate life prediction. Full article

Deploying unmanned aerial vehicles (UAVs) cooperatively with legged robots for disaster response and inspection requires autonomous docking on miniature walking platforms. This study addresses the problem of landing a foldable quadrotor onto the back of a trotting wheel-legged robot ($300\times 180$ mm) and subsequently taking off while carrying it as a payload. Four tightly coupled challenges distinguish this task from conventional mobile-platform landing: (i) an extremely small landing surface, (ii) gait-induced periodic vibrations at $2.5$ Hz, (iii) continuous platform translation at $0.3$–$0.8$ m/s, and (iv) surface docking that requires simultaneous position and attitude matching rather than mere point tracking. The proposed framework comprises four components: (1) a novel single-servo crank-rocker folding mechanism that reduces the folded body footprint by 48.5% and the maximum linear dimension from 590 mm to 309 mm (↓47.6%) compared with the prior dual-servo design; (2) a staged Continuous Fast Nonsingular Terminal Sliding Mode (CFNTSM) controller combined with a Gait-Frequency-Aware Finite-time Extended Observer (GFA-FEO); (3) a Feature-wise Linear Modulation Soft Actor-Critic (FiLM-SAC) residual reinforcement-learning policy conditioned on physical states and mission phase, with an adaptive trust weight $\lambda \left(t\right)$; and (4) a payload-adaptive takeoff strategy with parameter hot-switching to handle the twofold mass increase. Extensive Monte Carlo simulations and ablation studies across three experiment groups demonstrate that the proposed hierarchical framework achieves sub-centimetre (<10 mm) position accuracy and <3° attitude matching on a walking platform. Quantitatively, the full method reduces docking RMSE by 42% relative to the model-based CFNTSM + GFA-FEO controller without residual RL (4.2 vs. 7.2 mm) and reduces post-lock takeoff RMSE by 63% through FEO hot-switching (16.2 vs. 44.2 mm). Full article

Distributed electric propulsion (DEP) systems offer a wide range of options for arranging the propulsion units on an aircraft. In most cases, the position of the propulsion systems is optimized for one specific flight phase, e.g., takeoff or cruise. Taking advantage of the high lift potential of the DEP also during descent and approach phases represents a challenge due to increased thrust. Energy harvesting propellers (EHPs) can be used to adapt the resulting thrust, by generation an additional drag force while regenerating a certain amount of energy back into the system. Therefore, the scaled flight demonstrator (SFD) e-Genius-Mod was modified to implement an energy harvesting powertrain in a DEP system. The energy harvesting wingtip propellers are integrated in a pusher configuration. It is possible to investigate different operation modes for recuperation, such as Windmilling and Opposite Pitch, by adjusting different propeller pitch angles. The electronics used for the wingtip propellers (WTPs) enable the control and measurement of the recuperation performance and furthermore to charge recuperated energy back into the battery. The energy harvesting system was tested in a wind tunnel to verify its functionality. In Windmilling mode, the maximum mean electrical power output is −25.7 W. In Opposite Pitch mode, the values were significantly higher, with a maximum mean electrical power of −184 W. This corresponds to up to seven times as much regenerated power in Opposite Pitch mode. Full article

Accurate prediction of forest fires is crucial for enhancing regional fire prevention and control. Existing models frequently rely on static factors such as weather and terrain, while insufficiently taking into account the Fuel Moisture Content (FMC), a critical internal factor that directly determines fire behavior. Instead, proxies like the Normalized Difference Vegetation Index (NDVI) are commonly employed, which weakens the physical foundation of predictions. This study assesses the marginal contribution of integrating dynamic FMC into fire prediction models. Concentrating on California, we developed a random-forest-based model that incorporates high-resolution FMC products retrieved by our team, along with meteorological, topographic, vegetation, and anthropogenic data. Through comparative experiments and SHapley Additive exPlanations (SHAP) analysis, we evaluated model improvements and the contribution mechanisms of key drivers. The results indicated that: (1) Incorporating FMC significantly enhanced model performance, with precision and specificity increasing by 3.93% and 3.60%, respectively, and the Area Under the Curve (AUC) showing improvements, suggesting heightened sensitivity in detecting actual fire occurrences. (2) SHAP analysis disclosed nonlinear effects and threshold dynamics: temperature was the dominant positive driver (the fire risk soared above 20 °C); FMC demonstrated a negative correlation with fire risk, with 100% serving as a potential threshold; elevation presented an inverted U-shaped pattern (the peak risk occurred at 1000–1500 m); and population density exhibited a shifting influence from positive to negative. (3) The monthly risk maps for California in 2023 captured the seasonal progression of fire risk and spatial patterns consistent with historical fire points. The fire risk map for 9 September 2020 also demonstrated consistency with the spatial distribution of the actual fire points on that day. This study validates that the integration of dynamic FMC strengthens the mechanistic foundation and early-warning capacity of fire prediction models, providing scientific backing for targeted fire management. Full article

Many advances in enhanced oil recovery (EOR) take advantage of the unique properties of nanomaterials to improve characterization of formation properties, achieve conformance control during flood operations, and extend the controlled release time of polymers. Magnetite nanoparticles (nMag) have been employed in these processes due to their low cost, low toxicity, and ability to be engineered to meet desired needs, especially with the application of a magnetic field. Similarly, silica dioxide (SiO₂) and aluminum oxide (Al₂O₃) nanoparticles have been evaluated for the delivery of scale and asphaltene inhibitors. However, the injection of nanoparticles into porous media comes with the risk of formation damage due to particle deposition, which can lead to increased injection pressures and reductions in permeability. The goal of this study was to develop a method to evaluate and assess nanoparticle formulations for their potential to cause formation damage. A screening apparatus was constructed to hold small sandstone discs (~2 mm) or cores (~2.5 cm) for rapid testing with minimal material use and the capability to be used with either aqueous brine solutions or non-polar solvents as the mobile phase. Image analysis of the disc and pressure measurements demonstrated increasing deposition of nMag and face-caking when the salinity was increased from 500 mg/L NaCl (8.56 mM) to API brine (2.0 M). Similarly, when the injected concentration of silica nanoparticles in 500 mg/L NaCl was increased from 1 to 10 wt%, the back pressure increased by 55 psi, and face-caking was observed. The screening test results were consistent with traditional core-flood tests and was able to be modified to accommodate organic liquid mobile phases. The screening test results closely matched nanoparticle transport and retention measured in sandstone cores, confirming the ability of the system to rapidly screen nanoparticle formulations for potential formation damage. Full article

The article presents the results of a manipulability analysis and optimal remote center of motion (RCM) frame placement based on the manipulability index for a simplified model of a planar laparoscopic surgical robot system with a control-based RCM constraint. The paper presents an analytical kinematic model of the planar laparoscopic surgical robot system, a method for computing a manipulability objective taking into account the RCM constraint, and a formulation of the optimization problem used to establish the optimal placement of the system with respect to the task. The presented results are backed by a sensitivity analysis measuring the influence of parameter uncertainty on the value of the objective. Full article

We consider a network of a residential heating system (RHS) composed of two types of agents: a prosumer and a consumer. Both are connected to a community heating system (CHS), which supplies non-intermittent thermal energy for space heating and domestic hot water. The prosumer utilizes a combination of solar thermal collectors and CHS heat, whereas the consumer depends entirely on the CHS. Any excess heat generated by the prosumer can either be stored on-site or fed back into the CHS. Weather conditions, modeled as a common noise term, affect both agents simultaneously. The prosumer’s objective is to minimize the expected discounted total cost, taking into account storage charging and discharging losses as well as uncertainties in future heat production and demand. This leads to a stochastic optimal control problem addressed through dynamic programming techniques. Scenario-based analyses are then performed to examine how different parameters influence both the value function and the resulting optimal control strategies. For a common noise coefficient ${\sigma }_0=0.4$, the prosumer incurs an approximate $16.08\%$ increase in the aggregated discounted cost from the case of no common noise. For a discharging efficiency ${\eta }_E={\displaystyle \frac{1}{0.9}}$, the maximum aggregated discounted cost increases by approximately $1.85\%$ as compared to the perfect discharging efficiency. Similarly, for a charging efficiency ${\eta }_E=0.9$, we observe an approximate $1.94\%$ increase in the aggregated discounted cost as compared to a perfect charging efficiency. Furthermore, we derive insights into the maximum expected discounted investment that a consumer would need to make in renewable technologies in order to transition into a prosumer. Full article

Lipid nanoparticles (LNPs) are now the go-to method for delivering genetic medicines, backed by real-world use in patients. Things like which fats they are made of, their shape at the molecular level, how ingredients mix, and how they are built, matter a lot. This review attempts to take a close look at how different components, such as ionizable lipids, auxiliary lipids (DSPC, DOPE), cholesterol, and PEG-based lipids, affect the bioavailability of LNPs. It also focuses on key functions of LNPs, including packaging genetic material, escaping cellular traps, spreading in the body, and remaining active in the blood. New data show that lipids with the right handedness and highly sensitive chiroptical quality control can sharpen delivery accuracy and boost transport rates, turning stereochemistry into a practical design knob. Rather than simply listing results, we examine real-world examples that are already used to regulate gene expression, enhance mRNA expression, splenic targeting, and show great potential for gene repair, protein replacement, and DNA base-editing applications. Also, recent advances in AI-based designs for LNPs that take molecular shape into account and help speed up modifications to lipid arrangements and mixture configurations are highlighted. In summary, this paper presents a practical and scientific blueprint to support smarter production of advanced LNPs used in genetic medicine, addressing existing obstacles, balanced with future opportunities. Full article

Agricultural non-point source pollution (AGNPSP) is one of the core challenges facing global water environment management. Existing research mainly focuses on post-event estimation of pollution loads and source analysis, while studies on proactive risk warning for watershed non-point source pollution are relatively limited, especially those that integrate with agricultural production practices. Therefore, this study takes the River Tongyang Watershed as the research object and establishes a fertilization warning and regulation model based on short-term meteorological data. First, it simulates the migration and transformation processes of pollutants within the watershed under different meteorological conditions and analyzes their spatiotemporal evolution characteristics. Then, combined with real-time water quality monitoring data at the lake inlet, it calculates the residual environmental capacity for pollutants in the river water. Finally, based on this environmental capacity and the farmland area, it back-calculates the maximum safe fertilization amount for each plot under different meteorological scenarios to achieve precise fertilization management. When the planned fertilization amount does not exceed this maximum safe value, environmental risks are within a controllable range; if exceeded, fertilization should be proportionally reduced to prevent non-point source pollution. The results indicate that this model can accurately predict the concentration trends of non-point source pollutants and can develop differentiated fertilization strategies based on rainfall scenarios. The “fertilization determined by water” decision-making framework established in this study provides a technically significant pathway for shifting watershed agricultural non-point source pollution management from passive treatment to active prevention. Full article

Arthur Conan Doyle’s most famous creation, Sherlock Holmes, is a determined rationalist, yet Doyle was himself a convert to spiritualism. Doyle’s interest in spiritualism informs four, somewhat neglected Gothic tales written during the last decades of the century: “The Winning Shot” (1883); “John Barrington Cowles” (1884); the short novel The Parasite (1894); and “Playing with Fire” (1900). These narratives are notable not only because they respond to the contemporary fascination with spiritualism, but because, in doing so, they explore (sometimes explode) the gendered assumptions of a heteronormative and patriarchal society, which carried over into the close, if erroneous, association of women with the powers of mediumship and mesmerism. Doyle complicates this binary: in his own stories, he presents women as victims of spiritualist power as well as manipulators of it. And while his fictional women do sometimes use that power for their own, self-serving ends, they also use it as a means of taking control back in a male-dominated world. While fascinating in itself, I argue, Doyle’s creation of a Gothicized spiritualism reflects a nuanced engagement with the gendered politics of his historical moment, as the “New Woman” sought to assert herself over the domestic ideology of the day. Full article

Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future. Full article

The ironless tubular permanent magnet synchronous linear motor (TPMSLM) is in high demand for high-precision servo control applications due to its advantages of having zero cogging effect and high dynamic response. However, its electromagnetic field analysis model has not yet been perfected. This paper aims to accurately predict the magnetic field distribution and electromagnetic performance parameters of an ironless TPMSLM. Taking the axially magnetized ironless TPMSLM as an example, and disregarding the influence of the armature magnetic field on the air gap magnetic field, a simplified analytical model of the TPMSLM is established in the cylindrical coordinate system based on the equivalent magnetization current method (EMC), and the analytical formula for the air gap magnetic flux density is then derived. Subsequently, by applying electromagnetic field theory and the analytical formula for the magnetic flux density in the air gap, analytical expressions for the back electromotive force (back EMF) and thrust are derived, reducing analytical complexity while maintaining accuracy. The accuracy and practicality of the proposed analytical formulas are validated through comparisons with finite element analysis (FEA) and experimental prototypes. This analytical approach facilitates the optimization of linear motor parameters and the study of thrust fluctuation suppression, thereby laying the foundation for high-precision servo control of linear motors. Full article

The improvement of comfort in the human–robot interaction for care recipients is a significant challenge in the development of nursing robots. The existing methods for enhancing comfort largely depend on subjective comfort questionnaires, which are prone to unavoidable errors. Additionally, traditional passive movement control approaches lack the ability to adapt and effectively improve care recipient comfort. To address these problems, this paper proposes an active, personalized intelligent control method based on neural networks. A muscle activation prediction model is established for the piggyback transfer robot, enabling dynamic adjustments during the care process to improve human comfort. Initially, a kinematic analysis of the piggyback transfer robot is conducted to determine the optimal back-carrying trajectory. Experiments were carried out to measure human–robot contact forces, chest holder rotation angles, and muscle activation levels. Subsequently, an Online Sequential Extreme Learning Machine (OS-ELM) algorithm is used to train a predictive model. The model takes the contact forces and chest holder rotation angle as inputs, while outputting the latissimus dorsi muscle activation levels. The Genetic Algorithm (GA) is then employed to dynamically adjust the chest holder’s rotation angle to minimize the difference between actual muscle activation and the comfort threshold. Comparative experiments demonstrate that the proposed ELM-GA-based active control method effectively enhances comfort during the piggyback transfer process, as evidenced by both subjective feedback and objective measurements of muscle activation. Full article

Finite-time thermodynamics (FTT) describes the study of thermodynamic processes that take place in finite time. Due to the finite-time requirement, in general the system cannot move from equilibrium state to equilibrium state. As a consequence, excess entropy is generated, available work is reduced, and/or the maximally achievable efficiency is not achieved; minimizing these negative side-effects constitutes an optimal control problem. Particularly challenging are processes and cycles that involve phase transitions of the working fluid material or the target material of a synthesis process, especially since most materials reside on a highly complex energy landscape exhibiting alternative metastable phases or glassy states. In this perspective, we discuss the issues and challenges involved in dealing with such materials when performing thermodynamic processes that include phase transitions in finite time. We focus on thermodynamic cycles with one back-and-forth transition and the generation of new materials via a phase transition; other systems discussed concern the computation of free energy differences and the general applicability of FTT to systems outside the realm of chemistry and physics that exhibit cost function landscapes with phase transition-like dynamics. Full article