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A deep understanding of the drivers behind aggregate carbon indicators, including carbon emissions and carbon intensity, is essential for achieving low-carbon economic growth. However, integrated analysis incorporating both indicators remains limited. Using a multi-region input-output model and structural decomposition analysis, we examine the determinants behind the changes in carbon indicators during 2000–2014. Our findings reveal the efficacy of global carbon reduction strategies, characterized by a decline in total emissions increase and an accelerated reduction in carbon intensity. Further decomposition shows that the carbon efficiency effect is the primary factor in reducing the carbon indicators by improving sectoral direct carbon intensity. The input structure effect is the second largest contributor, mainly resulting from the decarbonization of intermediate inputs in the supply chain. The final demand effect is the main impediment due to the steady growth of consumption and investment demand across economies.

This paper presents a hierarchical LiDAR simulation framework to address the challenges of accurately simulating LiDAR data in autonomous driving scenarios. The framework utilizes a homology mapping approach to integrate LiDAR responses hierarchically at three levels: the instantaneous power response, environmental optical channel response, and target reflection response of LiDAR. This allows for the dynamic coupling of LiDAR geometric and physical models with varying environmental parameters. By integrating an array of interactions intrinsic to the LiDAR system and its external environment, the proposed model can provide high-fidelity LiDAR point cloud simulations. The effectiveness of the simulated point clouds has been validated through extensive experiments using actual LiDAR data and detection algorithms trained on existing datasets. The experimental results show that the proposed method has the potential to improve the realism of LiDAR simulations and the accumulation of challenging perception data.

The growing demand for fresh products presents a formidable challenge for managers to design a supply chain that is both cost-effective and efficient while maintaining the freshness of the products. This article examines the use of blockchain technology as a solution for enhancing resource sharing and improving the efficiency and sustainability of fresh product supply chains. In particular, we propose a smart contract that deals with the intricacies of the facility location-routing optimization problem, taking into account both resource sharing and product freshness requirements. To tackle this problem, we propose a hybrid solution approach that integrates a greedy clustering algorithm and the YALMIP toolbox. Our computational experiments validate the effectiveness of the proposed model and the feasibility of the presented solution method. Our research findings reveal the potential of blockchain technology in the fresh product industry, underscoring its significance in the development of sustainable and efficient supply chains. Furthermore, the proposed model and adopted solution method contribute to the advancement of the fresh product supply chain and provide practical suggestions for managers and policymakers.

This paper presents a novel velocity estimation method for an articulated steering vehicle to improve the automation and safety of articulated steering vehicle. The estimation method process is done in three steps: First, the articulated steering vehicle's tire forces are calculated separately against articulated steering vehicle's asymmetric tire load transfers. Then the tire forces are used to build a concise dynamic model, which only needs to consider the single centroid's dynamics in a fixed direction of the vehicle body. Finally, an unscented Kalman filter estimator is developed further based on the articulated steering vehicle dynamics model for velocity estimation. The main contribution of this paper to the related literature lies in two aspects. On the one hand, by converting the velocity vectors of each vehicle body to a fixed coordinate system, a concise dynamic model is built to avoid complex calculations and high sensor costs. On the other hand, an additional moment equilibrium equation is introduced to assist the simplified estimator in calculating the asymmetric tire dynamics of articulated steering vehicle, which ensures the accuracy and universality of the method. The simulation results from ADAMS/MATLAB indicate that the proposed model can effectively reflect the dynamics of articulated steering vehicle even under obvious load transfer. In addition, the developed unscented Kalman filter estimator can obtain accurate and robust estimation results in several typical vehicle manoeuvres.

Visual semantic segmentation is a key technology to realize scene understanding for autonomous driving and its accuracy is affected by the light changes in images. This paper proposes a novel multi-exposure fusion approach to visual semantic enhancement of autonomous driving. Firstly, a multi-exposure image sequence is aligned to construct a stable image input. Secondly, high contrast regions of multi-exposure image sequences are evaluated by context aggregation network (CAN) to predict image weight map. Finally, the high-quality image is generated by weighted fusion of multi-exposure image sequences. The proposed approach is validated by using Cityscapes’ HDR dataset and real environment data. The experimental results show that the proposed method effectively restores lost features in the light changing images and enhances accuracy of subsequent semantic segmentation.

The term environmental innovation system refers to an innovation network composed of enterprises, universities, and research institutions involved in the development and diffusion of environmental technology, with the participation of a government. An environmental innovation system not only exerts important impact on the achievement of carbon neutrality but also affects social and economic activities. Investigations on environmental innovation system performance constantly assume a single-stage independent system while ignoring its internal structure. However, such systems are composed of environmental innovation research and development (R&D) and environmental innovation conversion subsystems. A two-stage data envelopment analysis (DEA) model is developed in this study to analyze the efficiency of Chinese regional environmental innovation system by opening the “black box” and considering shared resources. Empirical results indicated that China presents high overall environmental innovation efficiency although some regions need to improve. Regions with low efficiencies in both environmental innovation R&D (EIR) and environmental innovation conversion (EIC) subsystems should expand their investment in and strengthen the management of environmental innovation resources. Regions with low EIR efficiency should improve the absorption and transformation of environmental innovation achievements. Regions with low EIC efficiency should increase investment in the commercialization of environmental innovation achievements and encourage green economy industries, such as new energy, art, tourism, and environmental protection.

Articulated wheeled loader vehicles have frequent rollover accidents as they operate in the complex outdoor environments. This article proposes an active anti-rollover control method based on a set of single-frame control moment gyro stabilizer installed on the rear body of the vehicle. The rollover dynamic model is first established for articulated wheeled loader vehicle with gyro stabilizer. The proposed control strategy is then applied in simulation to verify the rollover control effect on the vehicle under steady-state circumferential conditions. Finally, a home-built articulated wheel loader vehicle with gyro stabilizer is used to further verify the proposed control strategy. The results show that the vehicle can quickly return to the stable driving state and effectively avoid the vehicle rollover when a suitable anti-roll control moment can be provided by the gyro stabilizer. As a result, the articulated wheeled loader vehicle is able to operate safely in a complex outdoor environment.

Articulated wheel loaders in construction industry have poor lateral stability and high rate of accident due to their multibody structure and uneven working fields. In order to realise early rollover warning and active control, it is necessary to explore the mechanism of instability and the change rules of dynamics parameters. This paper proposes a novel dynamics rollover model with 6 degrees of freedom (DOF) for modelling the instability process of an articulated wheel loader. The proposed rollover model is compared with a virtual prototype and a 1:5 scale-down physical prototype under four typical working conditions to verify its reliability, and its accuracy is investigated via a rotation motion under a constant steering angle under the critical state of rollover. Finally, the dynamics rollover model is used to investigate the influence of different kinematic parameters on lateral stability of an articulated wheel loader with steady-state margin angle (SSMA) as stability index.

To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of the equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.The world's lightest head-mounted fluorescence microscope, weighing only 0.43 grams, has been developed and utilized to monitor neuronal activity in four deep brain regions simultaneously while the animal is freely behaving.

Polymers play a central role in controlling the crystallization of pharmaceuticals with effects as divergent as amorphous form stabilization and the acceleration of crystallization. Here, using pyrazinamide and hydrochlorothiazide as model pharmaceuticals, it is demonstrated that the same functional group interactions are responsible for these opposing behaviors and that whether a polymer speeds or slows a crystallization can be controlled by polymer solubility. This concept is applied for the discovery of polymers to maintain drug supersaturation in solution: the strength of functional group interactions between drug and polymer is assessed through polymer-induced heteronucleation, and soluble polymers containing the strongest-interacting functional groups with drug are shown to succeed as precipitation inhibitors.

Wheel loaders need an interactive interface to collect and display the posture information when they operate in a harsh environment. This paper proposes a visualization interface for posture monitoring of wheel loaders. It is driven by the data from the inertial measurement system based on attitude heading reference system, including a real-time three-dimensional animation display system created using OpenGL. The proposed system could be effectively used for monitoring the body posture of wheel loaders under the influence of the external environment. Experimental results are presented to show the good accuracy and real-time performance of the proposed monitoring system. Moreover, the proposed system could enhance vehicle safety, remote operability, and human–vehicle interaction.

Energy saving and emission reduction are increasingly important. This paper studies a two-stage network based on the DEA-SBM model to study the two aspects. This study evaluates their efficiency, including benchmarks for improvement, of 27 regions in China from 2009 to 2013. First, the efficiency of each DMU in each stage can be obtained by the proposed model. Second, by combining the two stages, the integrated efficiency of each region can be calculated. The regions are also grouped geographically. The empirical results show that: (i)Beijing and Tianjin are the best in terms of energy system, while Gansu and Ningxia perform best in terms of environment system. (ii)From the perspective of geographical area in China, the eastern area are the best for stage one, while the western area is the best for stage two. (iii)In terms of integrated efficiency, the western area performs worst.

Human stem cell-derived blastoids display similar morphology and cell lineages to normal blastocysts. However, the ability to investigate their developmental potential is limited. Here, we construct cynomolgus monkey blastoids resembling blastocysts in morphology and transcriptomics using naive ESCs. These blastoids develop to embryonic disk with the structures of yolk sac, chorionic cavity, amnion cavity, primitive streak, and connecting stalk along the rostral-caudal axis through prolonged in vitroculture (IVC). Primordial germ cells, gastrulating cells, visceral endoderm/yolk sac endoderm, three germ layers, and hemato-endothelial progenitors in IVC cynomolgus monkey blastoids were observed by single-cell transcriptomics or immunostaining. Moreover, transferring cynomolgus monkey blastoids to surrogates achieves pregnancies, as indicated by progesterone levels and presence of early gestation sacs. Our results reveal the capacity of in vitrogastrulation and in vivoearly pregnancy of cynomolgus monkey blastoids, providing a useful system to dissect primate embryonic development without the same ethical concerns and access challenges in human embryo study.

•The new total factor carbon emission productivity (TFCEP) is proposed.•TFCEP takes into account the link between energy inputs and carbon emissions.•TFCEP of China’s industrial sectors from 2013 to 2016 is investigated.•TFCEP was driven by technological progress and technological deterioration.

Purpose Resource scheduling is the study of how to effectively measure, evaluate, analyze, and dispatch resources in order to meet the demands of corresponding tasks. Aiming at the problem of resource scheduling in the private cloud environment, the purpose of this paper is to propose a resource scheduling approach from an efficiency priority point of view.Design/methodology/approach To measure the computational efficiencies for the resource nodes in a private cloud environment, the data envelopment analysis (DEA) approach is incorporated and a suitable DEA model is proposed. Then, based on the efficiency scores calculated by the proposed DEA model for the resource nodes, the 0-1 programming technique is introduced to build a simple resource scheduling model.Findings The proposed DEA model not only has the ability of ranking all the decision-making units into different positions but also can handle non-discretionary inputs and undesirable outputs when evaluating the resource nodes. Furthermore, the resource scheduling model can generate for the calculation tasks an optimal resource scheduling scheme that has the highest total computational efficiency.Research limitations/implications The proposed method may also be used in studies of resource scheduling studies in the environments of public clouds and hybrid clouds.Practical implications The proposed approach can achieve the goal of resource scheduling in private cloud computing platforms by attaining the highest total computational efficiency, which is very significant in practice.Originality/value This paper uses an efficiency priority point of view to solve the problem of resource scheduling in private cloud environments. [ABSTRACT FROM AUTHOR]

Carbonate gas reservoirs with edge and bottom water contain abundant reserves, making them key production targets in the Tarim Basin, Sichuan Basin, Ordos Basin, and other petroleum provinces. Water invasion may occur in the middle and late development stages of such reservoirs, leading to reduction of gas displacement efficiency and gas recovery. In this paper, a pore-scale water-gas immiscible flow model is established by coupling the fluid flow equation and the gas-water contact (GWC) tracking equation. The process of gas displacement with water is simulated in the heterogeneous porous media generated by the quartet structure generation set (QSGS). Finally, the mechanisms of remaining gas distribution and formation are analyzed, and the variation mechanism of microscopic gas displacement efficiency is discussed. The results are obtained in three aspects. First, the remaining gas is distributed at the blind end, in the pore-throat and as clusters, with their proportions and scales jointly controlled by microscopic pore structures, wettability and capillary number. The remaining gas can be further produced by changing the production pressure differential to disturb the original pressure system and gas expansion, so as to improve the microscopic gas displacement efficiency. Second, the microscopic gas displacement efficiency is closely related to the gas flow process. Formation or expansion of each water flow path may cause rapid increase of water cut and slows down the increase of gas displacement efficiency. Third, the microscopic pore structure and wettability are the inherent features of the gas reservoir, so the capillary number can be optimized to change the mode of GWC advancement, and then to effectively improve the microscopic gas displacement efficiency. It is concluded that for real gas wells, the evolution of mechanical mechanisms of GWC advancement should be revealed depending upon the microscopic pore structure and wettability of the reservoir, and then the optimal capillary number can be determined. Furthermore, clarifying the pore-scale water-gas flow characteristics and physical mechanism of microscopic gas displacement provides guidance for the planning of enhanced gas recovery.

Wheel loaders are prone to lateral tilting accidents due to their complex working environment, variable structure frame and the large offset of the centroid position. The accidents may seriously threaten the driver’s safety. This paper proposes a steady-state margin (SSM) angle as a new instability threat indicator to analyse lateral stability of wheel loaders. Firstly, the structural characteristics and the rollover process of a wheel loader are presented, and the SSM angle is defined. Secondly, on the assumption that the tyres are rigid, the calculation process of the SSM angle is described in detail, then the SSM angle of the XG953 wheel loader is computed. A virtual prototyping simulation was performed under the environment of ADAMS. The maximum relative error between the theoretical calculation and simulated result was 4.55%, within the permissible speed range. When changing the tyre parameters to normal tyre parameters, the relative error became larger. Finally, an indirect method is proposed to deal with the influence of tyres on the SSM angle and reduce the relative error.

An optical trap is used to stop chromosome movement in spermatocytes from an insect and a flatworm and to stop pole movement in PtK cells. The forces required are much smaller than previously believed.

Relative concentrations of Streptococcus mutansand Streptococcus sanguisare important parameters in the study of dental caries, but current methods of measuring these concentrations are time consuming and prone to inaccuracies. We investigate the use of Raman spectroscopy for measuring relative concentrations of these two bacterial species in solid mixtures. To our knowledge, this is the first time Raman spectroscopy has been used to analyze bacterial mixtures rather than to identify the species of a pure colony. Mixtures of the two streptococcal species in various ratios are measured for 200 s using a home-built Raman microscope. Spectral correlations with bacterial content were identified via partial least-squares analysis. The relative concentrations of S. mutansin subsequent samples are predicted with a root mean squared error below 5. In clinical plaque samples, this sort of accuracy would enable discrimination between normal and dangerously elevated levels of S. mutans. Samples with and without salivary proteins are predicted with equal accuracy. This result shows the potential of Raman spectroscopy for analyzing mixed populations of bacteria, such as those that occur in oral plaques. © 2004 Society of Photo-Optical Instrumentation Engineers.

Lane detection algorithms play a key role in Advanced Driver Assistance Systems (ADAS), which are however unable to achieve accurate lane recognition in low-light environments. This paper presents a novel deep network structure, namely LLSS-Net (low-light images semantic segmentation), to achieve accurate lane detection in low-light environments. The method integrates a convolutional neural network for low-light image enhancement and a semantic segmentation network for lane detection. The image quality is firstly improved by a low-light image enhancement network and lane features are then extracted using semantic segmentation. Fast lane clustering is finally performed by using the KD tree models. Cityscapes and Tusimple datasets are utilized to demonstrate the robustness of the proposed method. The experimental results show that the proposed method has an excellent performance for lane detection in low-light roads.