Landscape Pattern and Plant Diversity in an Arid Inland River Basin: A Structural Equation Modeling Approach Based on Multi-Source Data.

Biodiversity in arid river basins is highly climate-sensitive, yet the multi-pathway relations among the environment, landscape structure, connectivity, and plant diversity remain unclear. Framed by a scale-place-space sustainability perspective, we evaluated, in the Hotan River Basin (NW China), how the environmental factors affect plant diversity directly and indirectly via the landscape configuration and functional connectivity. We integrated Landsat images (2000, 2012, and 2023), 57 vegetation plots, topographic and meteorological data; computed the landscape indices and Conefor connectivity metrics (PC, IIC); and fitted a partial least squares structural equation model (PLS-SEM).

Research on supply chain resilience mechanism of AI-Enabled manufacturing enterprises -based on organizational change perspective.

Artificial intelligence (AI) is fundamentally reshaping supply chain operation modes and innovation paths, which has a significant impact on the development of supply chain resilience. This study utilizes panel data of Chinese A-share listed manufacturing companies from 2013 to 2022 to measure the application of AI through textual analysis and construct firm-level supply chain resilience indicators using factor analysis. The main findings suggest that AI greatly enhances supply chain resilience in the Chinese manufacturing industry, and this result holds in a series of robustness tests.

Neighboring Patch Density or Patch Size? Which Determines the Importance of Forest Patches in Maintaining Overall Landscape Connectivity in Kanas, Xinjiang, China.

The precise identification of priority areas for conservation based on connectivity can significantly enhance protection efficacy and mitigate biodiversity loss in fragmented landscapes. Priority area selection efforts are typically conducted in landscapes with a limited number of patches or simplified to focus on large patches, while landscapes with numerous patches are rarely explored. In this paper, we used a forest in Kanas, Xinjiang, China, as a case study to explore priority patches for conservation according to their contribution to maintaining overall landscape connectivity, as well as to assess how structural factors influence patch importance in connectivity, based on graph theory.

Design of e-commerce product price prediction model based on generative adversarial network with adaptive weight adjustment.

E-commerce platforms have amassed extensive transaction data, which serves as a valuable source for price prediction. However, the diversity of commodities poses challenges such as data imbalance, model overfitting, and underfitting. To address these issues, this paper presents an improved generative adversarial network model that integrates the strengths of Conditional Generative Adversarial Nets and the Wasserstein Generative Adversarial Network.

Can green location-oriented policies promote substantive green innovation by enterprises? Evidence from China's National Eco-Industrial Demonstration Parks.

Corporate green innovation is a key practice driving sustainable economic development. Exploring the effectiveness of green location-oriented policies in fostering substantial green innovation among micro-enterprises through a "point-to-area" approach holds practical importance. Drawing on the practice of China's National Eco-Industrial Demonstration Parks (NEDPs) and utilizing data from A-share listed companies in Shanghai and Shenzhen spanning the period from 2007 to 2022, this paper utilizes a staggered Difference-in-Differences methodology to examine the impact and mechanism of green location-oriented policies on corporate substantial green innovation.

Social expectations or self-regulation?-study on tourists' comity behavior in taking photos.

Introduction: During travel, photos capture beautiful moments and serve as a visual narrative. However, achieving a satisfactory photo in crowded tourist scenes relies on tourists' mutual cooperation and courtesy. This study aims to explore tourists' courteous behavior in photo-taking through normative activation theory.

Analysis of government subsidy strategies in the supply chain of science and technology innovation platform.

In the current era of technological advancements and intense global competition, innovation in Science and Technology (S&T) has become a crucial aspect of every country's development strategy, and the development of cutting-edge technologies in core areas is of utmost importance. Innovation platforms that offer services for core technologies can be classified into three operational models: Public Welfare Platforms, Social Enterprise Platforms, and Commercial Platforms, depending on their objectives. To accelerate the realization of S&T innovation, the government provides subsidies to guide the S&T innovation platform and increase the participation of innovation subjects.

A study on the spatiotemporal evolutionary pattern and influencing factors of sports venues in Xi'an.

Based on multi-source data, the spatio-temporal distribution characteristics, evolution trends, and driving factors of sports venues in Xi'an City were systematically explored in this work. Utilizing the Kernel Density Estimation (KDE) method, the distribution density of sports venues in 2010, 2015, and 2020 was scientifically calculated. Furthermore, the Multi-Scale Geographically Weighted Regression (MGWR) model was employed to analyse the factors influencing regional sports venues.

Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction.

To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Long Short-Term Memory Network (LSTM), and Frequency-Enhanced Channel Attention (FECA). The model aims to improve prediction accuracy and robustness for complex water quality dynamics, which is critical for environmental protection and sustainable water resource management. First, CEEMDAN and Sample Entropy (SE) were used to decompose raw water quality data into interpretable components and filter noise.

How does emotional attachment impact mobile phone recycling intention? Emotional process and contingencies.

Recycling behavior is highly complex and context-dependent, shaped by both rational and irrational factors. This study examines how emotional attachment discourages mobile phone recycling within the framework of endowment effect theory and the focus theory of normative conduct. Using survey data from 579 individuals in mainland China, the research employs stepwise regression and bootstrap approaches to test the proposed model.

A fast sand-dust video quality improvement method based on color correction and illumination compensation.

Sand-dust weather seriously reduces the effectiveness of computer vision equipment acquisition. To solve this problem, a fast sand-dust video quality improvement method based on color correction and illumination compensation is proposed in this paper. The mapping function strategy designed in the paper has two methods for dealing with sand-dust video frames.

DM-YOLO: improved YOLOv9 model for tomato leaf disease detection.

In natural environments, tomato leaf disease detection faces many challenges, such as variations in light conditions, overlapping disease symptoms, tiny size of lesion areas, and occlusion between leaves. Therefore, an improved tomato leaf disease detection method, DM-YOLO, based on the YOLOv9 algorithm, is proposed in this paper. Specifically, firstly, lightweight dynamic up-sampling DySample is incorporated into the feature fusion backbone network to enhance the ability to extract features of small lesions and suppress the interference from the background environment; secondly, the MPDIoU loss function is used to enhance the learning of the details of overlapping lesion margins in order to improve the accuracy of localizing overlapping lesion margins.

Research on digital empowerment, innovation vitality and manufacturing supply chain resilience mechanism.

As global manufacturing competition increasingly emphasizes supply chain resilience, enhancing the risk resistance of manufacturing supply chains through digital empowerment has become a critical priority. This study leverages the opportunities of the digital economy to deeply investigate the mechanisms that enhance supply chain resilience. Using data from China' s A-share listed manufacturing companies from 2012 to 2020, a research framework is constructed based on information asymmetry and transaction cost theories.

Synchronization of complex dynamical networks with saturated delayed impulsive control.

This paper considers the local exponential synchronization problem for a type of complex dynamical networks (CDNs) with both system delay and coupled delay using saturated delayed impulsive control. Based on the methods of average impulsive interval (AII), average impulsive delay (AID) and average impulsive estimation (AIE), a Razumikhin-type inequality with hybrid delayed impulses (which include delayed impulses and delay-free impulses) is derived. This inequality includes pure delayed impulsive inequalities.

Dynamic simulation of policy-driven green technology innovation networks: Digital empowerment and collaborative efficiency.

To delve into the intricate dynamics of green innovation, it is imperative to establish a policy-driven green innovation network and optimize its multi-entity collaborative mechanism. Given the dynamic complexity of a technological innovation network composed of multiple entities, this paper examines the interactions among four subsystems based on system dynamics (SD) simulation: resource input, innovation performance, policy-driven, and digital empowerment subsystem. Furthermore, we analyze the combined effects of policy-driven initiatives and the role of digital platforms in facilitating innovation efficiency based on empirical evidence.

Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices.

Leaf water content (LWC) is a vital indicator of crop growth and development. While visible and near-infrared (VIS-NIR) spectroscopy makes it possible to estimate crop leaf moisture, spectral preprocessing and multiband spectral indices have important significance in the quantitative analysis of LWC. In this work, the fractional order derivative (FOD) was used for leaf spectral processing, and multiband spectral indices were constructed based on the band-optimization algorithm.

Dynamic strategy for low-carbon supply chain considering retailers competition and technological innovation.

This paper discusses how managers adjust their strategies to allocate relevant resources more effectively and maximize economic benefits when major technological changes are predicted for the future. For a supply chain system consisting of a single manufacturer and two competing retailers as the research object. First, random stop model is applied to portray the impact of technological innovation on the decision-making of supply chain members.

Impacts of chemical fertilizer reduction on grain yield: A case study of China.

Reducing fertilizer usage is a crucial measure for achieving high-quality development in Chinese agriculture. Utilizing panel data from 31 Chinese provinces spanning from 2004 to 2019, this study empirically analyzes the dynamic relationship between fertilizer application and grain production, exploring the underlying mechanisms. The study findings reveal that the application of fertilizers maintains a positive impact on grain production.

Assessing the impact of dust events on the Holiday Climate Index in the Taklimakan Desert region.

As a significant sector within the tourism industry, desert tourism has developed rapidly in recent years, contributing significantly to local economic development. On the other hand, desert tourism is constantly influenced by the desert climate, characterized by high temperatures, aridity, and dust events. This study examines and analyzes the impact of dust events on the Holiday Climate Index (HCI) using an improved methodology.

Measurement of carbon emissions and responsibility sharing for the industrial sector in Zhejiang, China.

Carbon reduction is imperative for achieving carbon peaking and neutrality. Accordingly, it is important to determine which industrial sectors have more responsibility in this matter. Based on data from Zhejiang's input-output tables, this study applies the Input-Output method to measure and compare the carbon emissions of 42 industrial sectors in Zhejiang Province from 2002 to 2017, and then assesses the carbon emission attributes of each industrial sector, and ultimately determines the responsibility for carbon emission reduction from the perspectives of the producers and consumers.

The impact of industrial robot adoption on corporate green innovation in China.

Green innovation plays a crucial role in transforming economic models and achieving sustainable development in enterprises. As an important embodiment of artificial intelligence technology, how industrial robots can effectively promote the green transformation of enterprises has become an important issue. This paper examines the impact and mechanisms of industrial robot adoption on corporate green innovation, as well as its heterogeneous effects.

Quantile regression for static panel data models with time-invariant regressors.

This paper proposes two new weighted quantile regression estimators for static panel data model with time-invariant regressors. The two new estimators can improve the estimation of the coefficients with time-invariant regressors, which are computationally convenient and simple to implement. Also, the paper shows consistency and asymptotic normality of the two proposed estimator for sequential and simultaneous N, T asymptotics.

Smoothed quantile regression for partially functional linear models in high dimensions.

Practitioners of current data analysis are regularly confronted with the situation where the heavy-tailed skewed response is related to both multiple functional predictors and high-dimensional scalar covariates. We propose a new class of partially functional penalized convolution-type smoothed quantile regression to characterize the conditional quantile level between a scalar response and predictors of both functional and scalar types. The new approach overcomes the lack of smoothness and severe convexity of the standard quantile empirical loss, considerably improving the computing efficiency of partially functional quantile regression.