Search Results (6,277)

This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries. Full article

In order to solve the problems that existing tomato maturity detection methods struggle to take into account both common tomato and cherry tomato varieties in complex field environments (such as light change, occlusion, and fruit overlap) and the model size being too large, this paper proposes a lightweight tomato maturity detection model based on improved YOLO11, named GFS-YOLO11. In order to achieve a lightweight network, we propose the C3k2_Ghost module to replace the C3K2 module in the original network, which can ensure a feature extraction capability and reduce model computation. In order to compensate for the potential feature loss caused by the light weight, this paper proposes a feature-refining module (FRM). After embedding each feature extraction module in the trunk network, it improves the feature expression ability of common tomato and cherry tomato in complex field environments by means of depth-separable convolution, multi-scale pooling, and channel attention and spatial attention mechanisms. In addition, in order to further improve the detection ability of the model for tomatoes of different sizes, the SPPFELAN module is also proposed in this paper. In combining the advantages of SPPF and ELAN, multiple parallel SPPF branches are used to extract features of different levels and perform splicing and fusion. To verify the validity of the method, this study constructed a dataset of 1061 images of common and cherry tomatoes, covering tomatoes in six ripened categories. The experimental results show that the performance of the GFS-YOLO11 model is significantly improved compared with the original model; the P, R, mAP50, and MAP50-95 increased by 5.8%, 4.9%, 6.2%, and 5.5%, respectively, and the number of parameters and calculation amount were reduced by 35.9% and 22.5%, respectively. The GFS-YOLO11 model is lightweight while maintaining high precision, can effectively cope with complex field environments, and more conveniently meet the needs of real-time maturity detection of common tomatoes and cherry tomatoes. Full article

This study investigates the problem of tracking the trajectory of a dynamic positioning (DP) ship under sudden surges of elevated sea states. First, the tracking problem is reformulated as an error calibration problem through the introduction of fully actuated system (FAS) approaches, thereby simplifying controller design. Second, a predefined-time control term is designed to maintain the convergence time of the trajectory tracking error within a specified range; however, the upper bound of the perturbation must be estimated in advance. The high sea state during operation can result in an abrupt change in the upper bound of disturbance, thereby affecting the control accuracy and stability of the system. Therefore, a linear control matrix is developed to eliminate the system’s dependence on the estimation of the upper bound of disturbance following smooth switching, thereby achieving control decoupling and providing a conservative switching time. Additionally, a nonlinear reduced-order expansion observer (RESO) is constructed for feedforward compensation. The stability of the system is demonstrated using the Lyapunov function, indicating that the selection of appropriate poles can theoretically enhance the system’s convergence with greater control accuracy and robustness after switching. Finally, the effectiveness of the proposed method is validated through simulations and comparative experiments. Full article

Magnetic-shielding technologies play a crucial role in the field of ultra-sensitive physical measurement, medical imaging, quantum sensing, etc. With the increasing demand for the accuracy of magnetic measurement, the performance requirements of magnetic-shielding devices are also higher, such as the extremely weak magnetic field, gradient, and low-frequency noise. However, the conventional method to improve the shielding performance by adding layers of materials is restricted by complex construction and inherent materials noise. This paper provides a comprehensive review about the enhancement of magnetic shielding in three aspects, including low-noise materials, magnetization control, and active compensation. The generation theorem and theoretical calculation of materials magnetic noise is summarized first, focusing on the development of spinel ferrites, amorphous, and nanocrystalline. Next, the principles and applications of two magnetization control methods, degaussing and magnetic shaking, are introduced. In the review of the active magnetic compensation system, the forward and inverse design methods of coil and the calculation method of the coupling effect under the ferromagnetic boundary of magnetic shield are explained in detail, and their applications, especially in magnetocardiography (MCG) and magnetoencephalogram (MEG), are also mainly described. In conclusion, the unresolved challenges of different enhancement methods in materials preparation, optimization of practical implementation, and future applications are proposed, which provide comprehensive and instructive references for corresponding research. Full article

Gas cloud imaging with uncooled infrared spectroscopy is influenced by ambient temperature, complicating the quantitative detection of gas concentrations in open environments. To solve the aforementioned challenges, the paper analyzes the main factors influencing detection errors in uncooled infrared spectroscopy gas cloud imaging and proposes a temperature correction method to address them. Firstly, to mitigate the environmental effects on the radiative temperature output of uncooled infrared detectors, a snapshot-based, multi-band infrared temperature compensation algorithm incorporating environmental awareness was developed. This algorithm enables precise infrared radiation prediction across a wide operating temperature range. Validation tests conducted over the full temperature range of 0 °C to 80 °C demonstrated that the prediction error was maintained within ±0.96 °C. Subsequently, temperature compensation techniques were integrated, resulting in the development of a comprehensive uncooled infrared spectroscopy gas cloud imaging detection method. Ultimately, the detection limits for SF6, ethylene, cyclohexane, and ammonia were enhanced by 50%, 33%, 25%, and 67%, respectively. Full article

Distributed generation and sector coupling are key factors for economic decarbonization. Because gas networks have a large storage capacity, they have attracted the attention of power engineers to use them to increase the flexibility and security of supply in the presence of renewable and distributed energy resources. This paper makes the first attempt to integrate the electricity and gas systems to fill available gas storage facilities with synthetic natural gas on a large scale. This synthetic natural gas can then be used to operate gas turbines and to compensate for the fluctuating production of renewable energy sources. The LINK-holistic architecture, which integrates renewable and distributed energy resources, is used in this work. It facilitates sector coupling, which means power-to-gas and gas-to-power, throughout the entire power grid and at the customer level. This work is limited to investigating the power-to-gas process at the prosumer level. The electricity surplus of rooftop PVs is used to produce synthetic natural gas, fed into the gas grid after covering the local gas load. The behaviors of the electricity and gas grids are investigated. Results show that electricity prosumers may also become prosumers of synthetic natural gas. The current unidirectional gas grids should be upgraded with compressors at pressure reduction groups to turn them bidirectional, allowing synthetic natural gas storage in the existing large gas storage appliances after considering the pipes’ linepack effect. The proposed solution could make it possible to fill the underground storage plants in summer, when the electricity and synthetic natural gas production exceed electrical and gas demand, respectively. Full article

Mixed stands of tree species with complementary traits can modulate stand growth and timber quality. At the Fengshushan Forest Farm, mixtures of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) with Liquidambar formosana Hance, Schima superba Gardner & Champ., Elaeocarpus sylvestris (Lour.) Poir., Cinnamomum camphora (L.) Presl, and Chinese fir monoculture were established. Differences in stand growth and timber quality among these mixtures were assessed and a test was conducted to evaluate the factors influencing the mixture effects. The average diameter at breast height, tree height, stand volume, and individual tree annual increment of mixtures generally exceeded those of Chinese fir monocultures but not necessarily those of broad-leaved monocultures. When the net interaction between species was complementary, overyielding in mixtures occurred (RP_fir,bl > 0), which was influenced significantly by stand density, soil properties, and timber quality. The timber quality and wood production of Chinese fir were enhanced by mixture with some broad-leaved species, with reduced slenderness and knottiness in mixtures, and notable increases in medium- (average outturn rates of 56.13%) and large-diameter (11.71%) timber in C. lanceolata/C. camphora mixture. The growth and timber quality of Chinese fir are largely promoted when grown mixed with broad-leaved species. Overyielding at the stand level occurs where Chinese fir compensates for the underperformance of broad-leaved trees. Full article

Background: Andexanet alfa is a specific antidote for factor Xa (FXa) inhibitors. It is licensed to treat patients under FXa inhibitor therapy with life-threatening bleeding. Concomitantly, volume expanders are used to compensate for blood loss and maintain circulation. The competitive binding of andexanet to FXa inhibitors may be disrupted due to hemodilution, as shown by laboratory assays with high sample dilution. This study investigated the efficacy of andexanet for the reversal of FXa inhibitors under hemodilution. Methods: Blood from 10 healthy volunteers was anticoagulated with rivaroxaban and subsequently treated with four different volume expanders (Ringer’s solution, 4% gelatine, 5% and 20% human albumin (HA)) at two dilution levels (20% and 50%). After anticoagulation and hemodilution, andexanet was added according to the high-dose protocol. Blood samples were analyzed using a Russell’s viper venom (RVV) test on a Clot Pro^® device, a thrombin generation assay, a fully automated coagulation analyzer and a chromogenic anti-FXa activity assay. Results: After anticoagulation, the median rivaroxaban concentration was 272 ng/mL (IQR 254–353). Anticoagulation with rivaroxaban caused a significant impairment of all coagulation parameters, which was further aggravated by hemodilution. After the administration of andexanet, coagulation parameters in anticoagulated samples were reversed to near baseline in all groups. Andexanet administration decreased the rivaroxaban plasma concentration in all groups to a median of <10 ng/mL. In the anticoagulated, non-hemodiluted samples, anti-FXa activity was reduced by 98%. The anti-FXa activity in the anticoagulated, hemodiluted samples was reduced by approximately 96% in the 20% diluted samples and by about 93% in the 50% diluted samples. Conclusions: Our data indicate that FXa inhibitor reversal with andexanet is about 5% less effective with 50% hemodilution than in non-hemodiluted samples. Full article

In electromechanical systems, backlash in gear trains can lead to a degradation in control performance. We propose a drive–anti-drive mechanism to address this issue. It consists of two DC motors that operate in opposite directions. One motor acts as the drive, while the other serves as the anti-drive to compensate for the backlash. This work focuses on switching between the drive and anti-drive motors, controlled by a switched-mode PID controller. Simulation results on an inverted pendulum demonstrate that the proposed scheme effectively compensates for backlash, improving position accuracy and control. This switched controller approach enhances the performance of electromechanical systems, particularly where gear backlash poses challenges to closed-loop performance. Full article

There exists an inaccurate measurement problem in permanent magnet synchronous motors (PMSMs) due to low motor speed operation, high temperatures and humid environments, which will degrade the motion performance and stability of PMSMs. In this study, a model reference adaptive system without position measurement is presented in a PMSM to improve the output performance with external disturbance suppression caused by an environmental change. Firstly, a PI adaptive estimation law is designed to identify the motor speed. Then, a optimization method based on the sliding mode variable structure technique is proposed to realize the stability augmentation of the speed loop by using the parametric fuzzy logic design. To reject the current loop noise, an extended Kalman filter (EKF) is adopted to compensate the input signal in the current loop. The effectiveness of this proposed method is verified via a numerical simulation in the case of different speeds and external loads. Full article

This study evaluated whether tree object segmentation using remote sensing techniques could be effectively conducted according to the green structures of urban forests. The remote sensing techniques used were handheld LiDAR and UAV-based photogrammetry. The data collected from both methods were merged to complement each other’s limitations. The green structures of the study area were classified into three types based on the distance between canopy trees and the presence of shrubs. The ability to individually classify trees within each of the three types of green structures was then evaluated. The evaluation method was to assess the success rate by comparing the actual number of trees, which were visually counted in the field, with the number of tree objects classified in the study. To perform semantic segmentation of tree objects, a preprocessing step was conducted to extract only the data related to tree structures from the data collected through remote sensing techniques. The preprocessing steps included data merging, noise removal, separation of DTM and DSM, and separation of green areas and structures. The analysis results showed that tree object recognition was not efficient when the green structures were complex and mixed, and the recognition rate was highest when only canopy trees were present, and the canopies did not overlap. Therefore, when observing in high-density areas, the semantic segmentation algorithm’s variables should be adjusted to narrow the object recognition range, and additional observations in winter are needed to compensate for areas obscured by leaves. By improving data collection methods and systematizing the analysis methods according to the green structures, the object recognition process can be enhanced. Full article

The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator. Full article

With the gradual increase in public awareness of ecological environmental protection, how to manage the increasingly tight supply of natural ecological energy and resources and the more evident greenhouse effect, how to properly treat and deal with the relationship between people, engineering and the ecological environment during the construction phase of engineering projects as well as how to evaluate the degree of environmental friendliness and sustainable development ability of engineering projects will become an urgent issue. Stakeholders in engineering projects must seriously consider these vital issues. Existing studies on the evaluation of engineering–ecology sustainable development capacity mainly focus on the regional and industry levels, with less research focusing on the sustainability of individual engineering projects; furthermore, they are primarily concentrated on the qualitative evaluation perspective. In order to refine these shortcomings, based on the viewpoint of the ecosystem of construction projects, this paper integrates the concept of whole life cycle with the ecological footprint measurement model and defines the notion of the ecological footprint of the whole life cycle of construction projects. Subsequently taking the above concept as the foundation and making the ecological footprint of various activities throughout the life cycle of a construction project a specific study object, the research establishes the ecological footprint measurement model of the whole life cycle of the project, and comprehensively evaluates the impacts on the surrounding environment, which include the consumption of energy resources, CO₂ and the absorption of solid wastes across the whole life cycle of the project. We then measure the sustainable development ability of engineering projects by comparing the ecological footprint with the ecological carrying capacity of a certain surrounding region. Finally, the practicability and reliability of the model is verified through the example’s application. Thus, the results of the study have significant theoretical and practical implications: (1) the introduction of the ecological footprint addresses the gap about the quantitative evaluation of the sustainability of individual engineering projects from a micro perspective; (2) it compensates for the shortcomings of other evaluation methods that only evaluate a single element, such as only CO₂, resources, energy or solid waste and so on; and (3) stakeholders can use the measured model to quantitatively assess the sustainability of new projects or urban renewal projects, providing strong support for project feasibility studies and project-establishment. Full article

The traditional clan-based structure of village society is gradually disintegrating due to rural population outflow and excessive tourism development. The construction of a diversified development model oriented toward the needs of local villagers is key to sustainable development and the preservation of cultural heritage. In this study, a case analysis of the Ding, Shijiagou, and Yanjing Villages in Shanxi Province was conducted. The research approach of “cognitive recognition–spatial structure–social connections” was used to integrate cognitive maps and space syntax with social network analysis to quantitatively analyze the cognitive elements in these villages, with the establishment of a collective cognitive map, global integration value maps, and villagers’ intention element cognitive networks based on functional types. The spatial cognition patterns and social network structures of these villages were explored and analyzed in relation to the complex interactions between villages from the perspectives of spatial cognitive preference, spatial accessibility, and social interaction to compensate for the limitations of using a single analytical approach. Differences in village cognition patterns were found—the efficient use of different types of space is influenced by spatial cognitive preferences; the degree of spatial integration affects the distribution of functional spaces, with high-integration areas tending to overlap with high-cognition spaces and featuring strong accessibility and connectivity; the density and stability of social networks significantly influence spatial cognition and the interaction patterns in villages; high-density networks enhance the frequency of interactions between villagers and the effectiveness of space utilization; and differentiation in spatial cognition among different groups may reduce village space integration, thereby influencing the overall social functions and cultural heritage of the village. Therefore, it is crucial to enhance the connectivity and influence of key nodes, optimize spatial layouts and social network structures, and introduce modern elements while preserving traditional culture, an approach that can promote social interaction and spatial vitality in the villages. This study provides a scientific basis and serves as a reference for the construction of sustainable development models for villages. Full article

This study investigates a data fusion method for underwater multi-inertial navigation based on topological distribution constraints, aimed at improving the positional accuracy of navigation systems on ships, and generating an underwater position reference. First, the state equation of single-axis rotational inertial navigation system (SRINS) is introduced to compensate for the equivalent gyroscope zero bias caused by gravity and magnetic field. Second, a flexible lever error equation based on the influence of flexural deformation angles between SRINSs is proposed. Third, by using the position difference between SRINSs as a measurement, the state and measurement equations of a centralized Kalman filter are analyzed. We conducted two sets of car experiments to verify the proposed data fusion method and a data acquisition system was used to synchronously collect measurement data from three SRINSs. Experimental results show that the proposed method can effectively improve overall positioning accuracy, with the root mean square (RMS) of longitude error reduced by approximately 8.4360%, latitude error RMS reduced by approximately 6.9174%, and overall positioning error RMS reduced by approximately 9.9492%. In certain conditions where other positioning methods are unavailable, such as underwater navigation, the proposed RINSs data fusion method can provide a highly reliable position reference. Full article