Search results

Drought is the primary disaster that negatively impacts agricultural and animal husbandry production. It can lead to crop reduction and even pose a threat to human survival in environmentally sensitive areas of China (ESAC). However, the phases and periodicity of drought changes in the ESAC remain largely unknown. Thus, this paper aims to identify the periodic characteristics of meteorological drought changes.

The potential evapotranspiration was calculated using the Penman–Monteith formula recommended by the Food and Agriculture Organization of the United Nations, whereas the standardized precipitation evaporation index (SPEI) of drought was simulated by coupling precipitation data. Subsequently, the Bernaola-Galvan segmentation algorithm was proposed to divide the periods of drought change and the newly developed extreme-point symmetric mode decomposition to analyze the periodic drought patterns.

The findings reveal a significant increase in SPEI in the ESAC, with the rate of decline in drought events higher in the ESAC than in China, indicating a more pronounced wetting trend in the study area. Spatially, the northeast region showed an evident drying trend, whereas the southwest region showed a wetting trend. Two abrupt changes in the drought pattern were observed during the study period, namely, in 1965 and 1983. The spatial instability of moderate or severe drought frequency and intensity on a seasonal scale was more consistent during 1966–1983 and 1984–2018, compared to 1961–1965. Drought variation was predominantly influenced by interannual oscillations, with the periods of the components of intrinsic mode functions 1 (IMF1) and 2 (IMF2) being 3.1 and 7.3 years, respectively. Their cumulative variance contribution rate reached 70.22%.

The trend decomposition and periods of droughts in the study area were analyzed, which may provide an important scientific reference for water resource management and agricultural production activities in the ESAC. However, several problems remain unaddressed. First, the SPEI considers only precipitation and evapotranspiration, making it extremely sensitive to temperature increases. It also ignores the nonstationary nature of the hydrometeorological water process; therefore, it is prone to bias in drought detection and may overestimate the intensity and duration of droughts. Therefore, further studies on the application and comparison of various drought indices should be conducted to develop a more effective meteorological drought index. Second, the local water budget is mainly affected by surface evapotranspiration and precipitation. Evapotranspiration is calculated by various methods that provide different results. Therefore, future studies need to explore both the advantages and disadvantages of various evapotranspiration calculation methods (e.g. Hargreaves, Thornthwaite and Penman–Monteith) and their application scenarios. Third, this study focused on the temporal and spatial evolution and periodic characteristics of droughts, without considering the driving mechanisms behind them and their impact on the ecosystem. In future, it will be necessary to focus on a sensitivity analysis of drought indices with regard to climate change. Finally, although this study calculated the SPEI using meteorological data provided by China’s high-density observatory network, deviations and uncertainties were inevitable in the point-to-grid spatialization process. This shortcoming may be avoided by using satellite remote sensing data with high spatiotemporal resolution in the future, which can allow pixel-scale monitoring and simulation of meteorological drought evolution.

Under the background of continuous global warming, the climate in arid and semiarid areas of China has shown a trend of warming and wetting. It means that the plant environment in this region is getting better. In the future, the project of afforestation and returning farmland to forest and grassland in this region can increase the planting proportion of water-loving tree species to obtain better ecological benefits. Meanwhile, this study found that in the relatively water-scarce regions of China, drought duration was dominated by interannual oscillations (3.1a and 7.3a). This suggests that governments and nongovernmental organizations in the region should pay attention to the short drought period in the ESAC when they carry out ecological restoration and protection projects such as the construction of forest reserves and high-quality farmland.

The findings enhance the understanding of the phasic and periodic characteristics of drought changes in the ESAC. Future studies on the stress effects of drought on crop yield may consider these effects to better reflect the agricultural response to meteorological drought and thus effectively improve the tolerance of agricultural activities to drought events.

This paper aims to examine the complex balance between enthusiasm and skepticism regarding artificial intelligence (AI) integration in educational practices. It advocates for a cautious, evidence-based approach while addressing both opportunities and challenges, aligning with the United Nations Sustainable Development Goal 4 (SDG4) for Quality Education.

Through critical analysis of current discourse surrounding AI in education, this paper synthesizes existing literature on both supportive and skeptical perspectives. The methodology involves systematic examination of past educational technology trends, current AI developments and their implications for teaching and learning. The paper develops its research agenda through careful consideration of existing empirical studies, theoretical frameworks and identifying gaps in current understanding.

The analysis reveals that while AI offers promising potential for enhancing learning outcomes and educational accessibility, its integration presents significant challenges that require careful consideration. The paper identifies critical tensions between technological innovation and pedagogical values, highlighting areas where enthusiasm for AI adoption must be tempered with empirical evidence and critical evaluation. Current evidence suggests that successful AI integration requires balanced consideration of both opportunities and limitations, with particular attention to maintaining human-centered educational practices.

This viewpoint provides a comprehensive framework for understanding the dialectic between AI’s educational potential and its limitations. By synthesizing both supportive and critical perspectives, it offers a nuanced approach to AI integration that acknowledges both opportunities and challenges. The article’s value lies in its systematic identification of key research priorities and its emphasis on evidence-based implementation strategies that serve educational goals while mitigating potential risks.

The research objective is to establish a smart system for building operation and maintenance using self-organizing map-based cluster merging (SOMCM) algorithm.

The process begins with a thorough literature review to establish the interface framework, followed by its design. An empirical study in Taoyuan City’s industrial park, involving 46 buildings and 3,526 maintenance records, informed development. By integrating the “Shared Facility Management System Equipment Repair Module” and the “Maintenance Management System for Existing Facilities,” 21 enhanced interface components were created. All work orders are stored in a database for aggregation, statistical analysis and clustering using the algorithm SOMCM, aiding repair decision-making.

The outcomes stemming from the proposed methodology culminate in the identification of seven patterns that can significantly enhance the efficiency of maintenance operations: (1) simplify current self-repair to outsourcing; (2) modify the current traditional contract type to open contract type; (3) adopt massive procurement for major facilities (e.g. air conditioning); (4) schedule power supply systems in a systematic and efficient way; (5) establish maintenance patterns as suggested to eliminate warehouse for spares; (6) reallocate maintenance resources in a seasonal cycle; (7) set up a standby team to resolve emergency repairs. The findings can reduce a significant amount of time and cost for the investigated industrial park.

Maintenance work has faced delays, aging equipment has caused component damage, and park structures no longer meet operational needs. Addressing these challenges, the study introduces a novel SOMCM approach for smart building operation and maintenance. This approach emphasizes creating a user-friendly, practical system pivotal to platform success. By integrating demand-driven strategies, it enhances traditional maintenance processes and offers innovative solutions to operational and management issues, ensuring alignment with modern requirements and improved efficiency.

Introduction: In this study, approaches based on right-tail unit root tests are used to analyze high-frequency time series. Although these approaches successfully capture unusually extreme price movements (bubbles) in financial markets, they can be biased in policymaking and forecasting. Testing the parameter stability enables the detection of both unusual price behavior and possible change points within the framework of the volatility approach. The break dates that cause the parameter change on the return series can be obtained, and the differentiation in the period can be seen.

Purpose: In this study, the analysis of periods that differ from the “changing parameter values” of the volatility process that emerged after November 2018 in the Borsa Istanbul (BIST) is made by using a new econometric approach in terms of change dates, parameter stability, and explosiveness characteristics. In this way, starting from determining periods with stable parameter values, the volatility process is tested to decide whether or not it shows an explosive feature.

Methodology: This study’s mainstay was published in February 2023. The findings reached within the framework of the knowledge provided by the technique in question will be the first in the applied literature. We used a uniform test for a mildly explosive GARCH process with double supreme statistics for BIST.

Findings: BIST is significantly affected by social and political events. This result implies that the “semi-efficient” market hypothesis for BIST needs to be re-examined in this context.

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

As an ancient art form, embroidery has strong practicality and artistic value. However, current embroidery style migration models produce images with unclear textures and a lack of stitch detail. So, in this paper, we propose a cyclic consistent embroidery style migration network with texture constraints, which is called Texture Cycle GAN (TCGAN).

The model is based on the existing Cycle GAN network with an additional texture module. This texture module is implemented using a pre-trained Markovian adversarial network to synthesize embroidery texture features. The overall algorithm consists of two generative adversarial networks (for style migration) and the Markovian adversarial network (for texture synthesis).

Qualitative and quantitative experiments show that, compared with the existing convolutional neural network style transfer algorithm, the introduction of the texture-constrained embroidery style transfer model TCGAN can effectively learn the characteristics of style images, generate digital embroidery works with clear texture and natural stitches and achieve more realistic embroidery simulation effects.

By improving the algorithm for image style migration and designing a reasonable loss function, the generated embroidery patterns are made more detailed, which shows that the model can improve the realism of embroidery style simulation and help to improve the standard of embroidery craftsmanship, thus promoting the development of the embroidery industry.

This paper aims to address the gaps in current research by exploring how blockchain technology influences corporate green innovation.

This study investigates the potential of blockchain technology to stimulate the green innovation of companies using the difference-in-difference model with a panel data set of 1,803 Chinese listed companies from 2012 to 2019.

The application of blockchain significantly increases the number of green invention patents obtained by companies but has no significant impact on green utility model patents, that is, blockchain applications improve the quality rather than the quantity of green innovation. The role of blockchain in promoting green innovation is particularly pronounced in state-owned enterprises, non-heavily polluting industries and older companies. The use of blockchain technology helps reduce sales costs and boosts research and development investments, thereby encouraging green innovation. Additionally, a company’s internal control quality plays a moderating effect.

Firstly, previous research on blockchain has primarily centered on its relationship with supply chain management. This article empirically tests the impact of blockchain applications on the green innovation of companies using the DID method. Secondly, current studies mainly explore the influencing factors on green invention patents. This article examines the impact of blockchain applications on both green invention patents and green utility model patents and identifies distinct influencing effects. Finally, this article introduces the internal control mechanism of enterprises into the DID model and explores the potential impact of the quality of internal control on the relationship between blockchain and green innovation.

In view of the unknown environmental parameters and uncertain interference during gripping by the manipulator, it is difficult to obtain an effective gripping force with the traditional impedance control method. To avoid this dilemma, the purpose of this study is to propose an adaptive control strategy based on an adaptive neural network and a PID search optimization algorithm for unknown environments.

The method is based on a variable impedance model, and a new impedance model is established using a radial basis function (RBF) neural network to estimate unknown parameters of the impedance model. The approximation errors of the adaptive neural network and the uncertain disturbance are effectively suppressed by designing the adaptive rate. In the meantime, auxiliary variables are constructed for Lyapunov stability analysis and adaptive controller design, and PSA is used to ensure the stability of the adaptive impedance control system. Based on the Lyapunov stability criterion, the adaptive im-pedance control system is proved to have progressive tracking convergence property.

Through comparative simulations and experiments, the superiority of the proposed adaptive control strategy in position and force tracking has been verified. For objects with low flexibility and light-weight (such as a coke, a banana and a nectarine), this control method demonstrates errors of less than 10%.

This paper uses RBF neural networks to estimate unknown parameters of the impedance model in real-time, enhancing system adaptability. Neural network weights are updated online to suppress errors and disturbances. Auxiliary variables are designed for Lyapunov stability analysis. The PSA algorithm is used to adjust controller parameters in real-time. Additionally, comparative simulations and experi-ments are designed to analyze and validate the performance of controller.

Environmental emissions are increasing in the urban areas. Much of the emissions arise from public procurement activities given that public sector firms are major customers to many supplying firms. Given the tremendous contribution, this study aims to examine the adoption of environmentally friendly urban freight logistics practices among public sector firms through assessing the impact of urban environmental governance, government environmental communication and organizational environmental governance.

Data for the study were collected in a single time period from central procuring and disposing entities (public sector firms) in the urban areas. A sample of 105 public sector firms in were used. One procurement officer and one member of the contracts committee were the key informants in the study. AMOS SPSS version 26 was used to obtain the results for the structural model and measurement model, respectively.

The findings indicate that the adoption of environmentally friendly urban freight logistics practices among public sector firms is significantly influenced by government environmental communication, organizational environmental governance and urban environmental governance. Urban environmental governance significantly influences organizational environmental governance. Urban environmental governance fully mediates the relationship between government environmental communication and public sector firms’ adoption of environmentally friendly urban freight logistics practices. Also, urban environmental governance and organizational environmental governance mediate the relationship between government environmental communication and adoption of environmentally friendly urban freight logistics practices.

This study examined the adoption of environmentally friendly urban freight logistics practices among public sector firms. However, the study was conducted in a public procurement setting rather than a private sector procurement setting. Also, the study examined the impact of government environmental communication on public sector firms’ adoption of environmentally friendly urban freight logistics practices ignoring the impact of internal communications made within the public sector firms on environmental issues.

This study examined the adoption of environmentally friendly urban freight logistics practices among public sector firms. Freight logistics in public sector procurement has not been given significant attention in earlier research. Emphasis is placed on sustainable public sector procurement ignoring other aspects that would help curb environmental emissions that may arise during and after the delivery of public procurement requirements.