Search Results (3,194)

This study investigates the impact of Environmental, Social, and Governance (ESG) performance on the green technological innovation (GTI) of Chinese A-share-listed companies, using data from 2009 to 2022. The findings indicate that strong ESG performance significantly enhances GTI, with this effect being more pronounced in state-owned firms and non-high-tech sectors, demonstrating heterogeneity across firm types. Mechanism analysis reveals that ESG performance facilitates GTI by mitigating financing constraints and boosting R&D investments. Moreover, the study identifies a non-linear relationship, wherein the effect of ESG on GTI varies with firm size and environmental regulation intensity, as confirmed through a threshold model. This study not only deepens the theoretical framework linking corporate ESG performance with GTI but also uncovers the practical mechanisms through which ESG performance drives GTI, providing both practical insights and theoretical foundations for governments to formulate corporate green transition policies. Full article

Background and Objectives: The aging population has led to a rise in cognitive impairments, including dementia, often associated with multimorbidity. Early diagnosis of cognitive decline is crucial, especially in primary care, where time constraints and the limitations of diagnostic tools may hinder accurate detection. This study aims to assess the cognitive functions of multimorbid patients using the Mini-Mental State Examination (MMSE) and the Lithuanian version of the General Practitioner Assessment of Cognition (LT-GPCOG). We hypothesized that the LT-GPCOG would perform similarly to the MMSE in suspecting cognitive impairments. Materials and Methods: This cross-sectional study, conducted from 2021 to 2022, included 796 patients aged 40–85, with arterial hypertension and at least one other chronic disease, recruited from seven Lithuanian primary health care centers. Cognitive function was assessed using the MMSE and LT-GPCOG, and statistical analyses were performed using SPSS to determine the association between cognitive impairment and various demographic and clinical variables. Results: Out of 796 participants, 793 completed the study. Cognitive impairment was suspected in 5.1% of participants based on MMSE and 4.2% based on the LT-GPCOG. Statistically significant associations were found between cognitive impairment and chronic obstructive pulmonary disease (COPD) (p = 0.008 and p = 0.003) in both tests and chronic kidney disease (CKD) (p = 0.005) while testing with the MMSE. Lower education and unemployment were also correlated with cognitive impairment (p = 0.008 and p < 0.001). Conclusions: The findings suggest that regular cognitive assessments should be integrated into the management of multimorbid patients, particularly those with COPD and CKD. The LT-GPCOG proved to be an efficient alternative to the MMSE in primary care settings, demonstrating comparable diagnostic accuracy. Further studies are also needed to assess the sensitivity and specificity of the LT-GPCOG test. Full article

In actual industry applications, the failure categories of practical equipment are usually a subset of laboratory conditions failure categories. Due to the strict constraints, partial transfer learning can address a more practical diagnostic scenario. In view of this, this paper proposes a target domain-specific classifier weight partial transfer adversarial network. Initially, the 1-D convolutional neural network is employed as the basic architecture. By training the domain discriminator and feature generator with an adversarial strategy, the recognition ability of the domain discriminant network and the feature extraction ability of the feature generation network can be enhanced. After that, a weighted learning strategy is introduced to guide the model to learn the cross-domain invariant feature. Also, a specific target domain classifier is utilized to redivide the target domain decision boundary to accurately classify the unlabeled target domain samples. Finally, five mainstream deep neural network methods are taken for comparison using the data from Western Reserve University and the motor-magnetic brake test designed by us. The results show that the proposed method reaches 90.18% and 96.53% classification accuracy on two datasets, respectively, which demonstrates superior performance compared with the state-of-the-art methods. Full article

The improvement of safety conditions on hazardous rock slopes in civil work, mining and quarrying, and urban environments can be achieved through the use of explosives for the removal of unstable rock elements and final profiling. This technique is often applied because, in most cases, drill and blast operations, where they can be used, are cheaper and faster than other techniques and require fewer subsequent maintenance interventions. Blasting represents a suitable and effective solution in terms of different geometries, rock formation types, access to site, safety, and the long-term durability of results. The primary purpose of this approach is the improvement of the safety conditions of sites, depending on their local features, as well as the safety of workers, so that the blasting scheme, geometry, and firing can be carefully adapted, thus imposing relevant limitations on the operating techniques. All these constraints associated with complex logistics make it difficult to standardize the demolition technique, due to different situations in terms of extension, location, fracturing state, and associated traffic risk. Considering the significant number of influencing factors for both the rock mass features and for the topography, the present research has been necessarily validated through the analysis of several case histories, thus on an experiential basis focusing on some simple control parameters to help engineers and practitioners regarding the first design and control of blasting schemes. Full article

A distributed adaptive fuzzy event-triggered bipartite formation tracking control scheme is proposed for switched nonlinear multi-agent systems (MASs) with function constraints on states. Fuzzy logic systems (FLSs) are used to identify uncertain items. To improve the transient performance of the system, a fixed-time prescribed performance function (FTPPF) is introduced to make the formation error converge to a prescribed boundary range within a fixed time. Considering that the state constraint boundary is restricted by multiple pieces of information (historical state, topological relationship, neighbor agent output, leader signal and time), a tan-type barrier Lyapunov function (BLF) is constructed to address the challenges brought by the state function constraint. The shortcoming of the “explosion of complexity” is compensated by fusing the backstepping control and command filter. To mitigate the communication burden while ensuring a steady-state performance, a distributed event-triggered fixed-time bipartite formation control scheme is proposed. Finally, the performance of the proposed control method is verified by an MAS consisting of four followers and one leader. Full article

The article deals with the properties of fault detection filters when applying their structure to a class of linear, continuous-time systems, with dynamics being specified by the system matrix of the ostensible Metzler structure. The proposed solution is reduced to the use of diagonal stabilization in the synthesis of the state observer and uses the decomposition of the ostensible Metzler matrix. The approach creates a unified framework that covers the compactness of parametric constraints on Metzler matrices and their quadratic stability. Due to the complexity of such constraints, the design conditions are formulated using sharp linear matrix inequalities. For potential application in network control structures, the problem is formulated and solved for linear discrete-time ostensible positive systems. Finally, a linearized model of the B747-100/200 aircraft is used to validate the proposed method. The numerical solution and simulation results show that the proposed approach provides superior sensitivity of the fault detection filter in detecting faults, compared to synthesis methods that do not guarantee the positivity of the filter gain. Full article

A communication network integrating multiple modes can effectively support the sustainable development of next-generation wireless communications. Integrated sensing, communication, and power transfer (ISCPT) represents an emerging technological paradigm that not only facilitates information transmission but also enables environmental sensing and wireless power transfer. To achieve optimal beamforming in transmission, it is crucial to satisfy multiple constraints, including quality of service (QoS), radar sensing accuracy, and power transfer efficiency, while ensuring fundamental system performance. The presence of multiple parametric constraints makes the problem a non-convex optimization challenge, underscoring the need for a solution that balances low computational complexity with high precision. Additionally, the accuracy of channel state information (CSI) is pivotal in determining the achievable rate, as imperfect or incomplete CSI can significantly degrade system performance and beamforming efficiency. Deep reinforcement learning (DRL), a machine learning technique where an agent learns by interacting with its environment, offers a promising approach that can dynamically optimize system performance through adaptive decision-making strategies. In this paper, we propose a DRL-based ISCPT framework, which effectively manages complex environmental states and continuously adjusts variables related to sensing, communication, and energy harvesting to enhance overall system efficiency and reliability. The achievable rate upper bound can be inferred through robust, learnable beamforming in the ISCPT system. Our results demonstrate that DRL-based algorithms significantly improve resource allocation, power management, and information transmission, particularly in dynamic and uncertain environments with imperfect CSI. Full article

The growing body of research on children’s understanding of extraordinary minds has demonstrated that children believe in the persistence of mental functioning after death. However, beyond the continuity of mind, the supernatural conception of death often involves the concept of the disembodied mind, which transcends the constraints of the physical body, possessing supernatural mental capacities. The current study investigated whether children differentiate between a dead agent’s mind and ordinary minds in terms of their perceptual and information-updating capacities. In a location-change false-belief task, which involved a story of a mouse protagonist that was either eaten by an alligator or not, 4- to 6-year-old Korean children (N = 114) were asked about the mental states of the protagonist, an ordinary adult (mom), and God. The results showed (1) older children’s tendency to respond in a way that differentiated (the living) mom from the dead protagonist, (2) an increasing trend of differentiating God’s super-knowingness from ordinary minds with age, and (3) inconclusive evidence regarding children’s differential responses to the dead versus living protagonist. This study suggests that children are not predisposed to view dead agents as possessing a disembodied and supernatural mind, highlighting the importance of cultural learning in the development of such religious concepts. Full article

The sustainability of the maritime industry relies on the effective implementation of global regulations, a task primarily managed by nation-states within the International Maritime Organization (IMO). This paper investigates the barriers to implementing international maritime regulations, centering on the IMO’s role in supporting member states, focusing on three themes: people, patterns, and particularities. Survey and interview data from maritime stakeholders identified key challenges: insufficient personnel training, financial limitations, and state interest in maritime affairs. These findings highlight the importance of skilled personnel, the need for adaptable processes amid resource constraints, and the unique challenges faced by states due to their political and regulatory differences. This paper further examines the IMO’s initiatives to support compliance, including technical guidance, capacity-building, and funding. However, issues persist, especially with enforcement and industry resistance to regulatory changes. This study suggests that enhanced regional monitoring, mandatory reporting, and greater involvement of non-state actors could improve compliance. It emphasizes the importance of targeted resource allocation to address the unique needs of individual states. This study also provides valuable insights into the challenges of maritime regulation implementation and highlights the indispensable role of the International Maritime Organization (IMO) in facilitating implementation efforts. Full article

With the strategic background of accelerating the transformation of the low-carbon economy in China, how to better help the new energy automobile industry realize green and high-quality development under the goal of “dual-carbon” with the strengthening of science and technology has become one of the most important issues nowadays, and it is of great significance to explore the relationship between financial technology (fintech) and the environmental, social, and governance (ESG) performance of the new energy automobile (NEV) industry. Using panel data from NEV companies listed on the Shanghai and Shenzhen A-share markets between 2011 and 2022, this study applies text mining techniques to construct a fintech index and analyze the transmission mechanisms through which fintech influences ESG performance. The findings show that fintech directly improves ESG outcomes for NEV companies, a result that remains robust across a series of validation tests. The analysis reveals that fintech reduces financing constraints and enhances corporate environmental information disclosure, which in turn drives better ESG performance. Furthermore, the impact of fintech is particularly pronounced in state-owned enterprises, large-scale firms, and technologically advanced NEV companies, as evidenced by heterogeneity analysis. This study provides empirical insights into fintech’s role in advancing sustainable development in the NEV sector, offering guidance for policymakers and industry stakeholders aiming to align technological progress with environmental and social governance objectives. Full article

This paper proposes a prescribed performance tracking control scheme for a category of nonlinear stochastic time-delay systems with input saturation and state asymmetric time-varying constraints. First, to solve the non-differentiable problem caused by input saturation, a smooth nonlinear function was utilized to approximate the saturation function. A nonlinear mapping technique was employed to transform the constrained problem into a bounded convergence problem. The time-delay problem was then solved by constructing the corresponding Lyapunov–Krasovskii function. The error feedback controller was constructed by combining the backstepping technique, the dynamic surface technique, the neural network approximation technique, and the adaptive control method. Based on stochastic mean-square stability theory, all signals in the closed-loop system are proven to be bounded under the designed control scheme. Also, this scheme ensures that the system states always stay within the constraint range, and the tracking error meets the prescribed performance constraint. Finally, the feasibility and superiority of the proposed control scheme were validated through simulation. Full article

Dual-functional radar–communication systems are extensively employed for the detection and control of unmanned aerial vehicle groups and play crucial roles in scenario monitoring. In this study, we address the downlink precoding problem in large-scale multi-user multiple-input multiple-output dual-function radar–communication systems equipped with low-resolution quantized digital-to-analog converters. To tackle this issue, we develop a weighted optimization framework that minimizes the mean squared error between the transmitted symbols and their estimates while satisfying specific radar performance requirements. Due to the complexity introduced by discrete constraints, we decompose the original problem into three sub-problems to reduce computational burden. Furthermore, we propose a dynamic projection refinement algorithm within the alternating direction method of multiplier framework to efficiently solve these sub-problems. Numerical experiments demonstrate that our proposed method outperforms existing state-of-the-art techniques, particularly in terms of bit error rate in low signal-to-noise ratio scenarios. Full article

To address the challenges faced by fuel cell hybrid electric tractors (FCHETs) equipped with a battery and supercapacitor, including the complex coordination of multiple energy sources, low power allocation efficiency, and unclear optimal energy consumption, this paper proposes two energy management strategies (EMSs): one based on hierarchical instantaneous optimization (HIO) and the other based on multi-dimensional dynamic programming with final state constraints (MDDP-FSC). The proposed HIO-based EMS utilizes a low-pass filter and fuzzy logic correction in its upper-level strategy to manage high-frequency dynamic power using the supercapacitor. The lower-level strategy optimizes fuel cell efficiency by allocating low-frequency stable power based on the principle of minimizing equivalent consumption. Validation using a hardware-in-the-loop (HIL) simulation platform and comparative analysis demonstrate that the HIO-based EMS effectively improves the transient operating conditions of the battery and fuel cell, extending their lifespan and enhancing system efficiency. Furthermore, the HIO-based EMS achieves a 95.20% level of hydrogen consumption compared to the MDDP-FSC-based EMS, validating its superiority. The MDDP-FSC-based EMS effectively avoids the extensive debugging efforts required to achieve a final state equilibrium, while providing valuable insights into the global optimal energy consumption potential of multi-energy source FCHETs. Full article

The ability to segment moving objects from three-dimensional (3D) LiDAR scans is critical to advancing autonomous driving technology, facilitating core tasks like localization, collision avoidance, and path planning. In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. Our method processes sequential range images, employing depth pixel difference convolution (DPDC) to improve the efficacy of dilated convolutions, thus boosting spatial information extraction from range images. Additionally, we incorporate Bayesian filtering to impose posterior constraints on predictions, enhancing the accuracy of motion segmentation. To handle the issue of uneven object scales in range images, we develop a novel edge-aware loss function and use a progressive training strategy to further boost performance. Our method is validated on the SemanticKITTI-based LiDAR MOS benchmark, where it significantly outperforms current state-of-the-art (SOTA) methods, all while working directly on two-dimensional (2D) range images without requiring mapping. Full article

In challenging environments such as disaster aid or forest rescue, unmanned aerial vehicles (UAVs) have been hampered by inconsistent or even denied global navigation satellite system (GNSS) signals, resulting in UAVs becoming incapable of operating normally. Currently, there is no unmanned aerial vehicle (UAV) positioning method that is capable of substituting or temporarily replacing GNSS positioning. This study proposes a reliable UAV top-down absolute positioning method (RTAPM) based on a monocular RGB camera that employs joint optimization and visual–inertial assistance. The proposed method employs a bird’s-eye view monocular RGB camera to estimate the UAV’s moving position. By comparing real-time aerial images with pre-existing satellite images of the flight area, utilizing components such as template geo-registration, UAV motion constraints, point–line image matching, and joint state estimation, a method is provided to substitute satellites and obtain short-term absolute positioning information of UAVs in challenging and dynamic environments. Based on two open-source datasets and real-time flight experimental tests, the method proposed in this study has significant advantages in positioning accuracy and system robustness over existing typical UAV absolute positioning methods, and it can temporarily replace GNSS for application in challenging environments such as disaster aid or forest rescue. Full article