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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.

Implementing green innovation is crucial for firms to build or sustain competitive advantages within the context of the sustainable development goals. Academic research has broadly explored how firms can induce green innovation behavior (GIB), examining external factors, but few studies in the current literature have deeply investigated unabsorbed slack as an internal antecedent of GIB. Drawing upon the behavioral theory of the firm and integrating it with dynamic capabilities theory, this study aims to address this deficiency by investigating the impact of unabsorbed slack on GIB and the mediating roles of two dimensions of capability reconfiguration: capability evolution and capability substitution in the relationship between unabsorbed slack and GIB. Furthermore, this study also discusses the moderating effects of consumer green pressure on the relationship between unabsorbed slack and capability evolution/substitution.

Survey data were collected from 286 Chinese technology-intensive manufacturing firms to empirically test the relationships among the variables.

The results reveal that unabsorbed slack has a positive influence on GIB. Furthermore, capability evolution and substitution both play mediating roles in the relationship between unabsorbed slack and GIB. Comparative analysis showed that the mediating effect of capability substitution is stronger than that of capability evolution. Moreover, consumer green pressure strengthens the positive relationship between unabsorbed slack and capability evolution/substitution.

This study enriches the research on the driving forces of GIB and contributes to providing managerial implications for firms to launch green innovation activities.

Chatbots are increasingly embodied in business and IS contexts to enhance customer and user experience. Despite wide interest in chatbots among business and IS academics, surprisingly, there are no current comprehensive reviews to reveal the knowledge structure of chatbot research in such areas.

This study employed a mixed-method approach that combines systematic review and bibliometric analysis to provide a comprehensive synthesis of chatbot research. The sample was obtained in December 2023 after searching across six databases: EBSCOhost, PsycINFO, Web of Science, Scopus, ACM Digital Library and IEEE Computer Society Digital Library.

This study reveals the major trend in publication trends, countries, article performance and cluster distribution of chatbot research. We also identify the key themes of chatbot research, which mainly focus on how users interact with chatbots and their consequences, such as users’ cognition and behavior. Moreover, several important research agendas have been discussed to address some limitations in the current chatbot research in business and IS fields.

The present review is one of the first attempts to systematically reveal the ongoing knowledge map of chatbots in business and IS fields, which makes important contributions and provides useful resources for future chatbot research and practice.

To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.

The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.

The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.

(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.

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.

Professional social networking sites (SNS) are widely employed by business individuals to build formal relationships, career opportunities, and professional development. While the characteristics of professional SNS are generally different from other SNS, there is limited understanding of the determinants of users’ continued usage on this platform. The study addresses this research gap by developing a conceptual framework that relates perceived values perspective (utilitarian value, hedonic value) and sociability dimensions (social presence, social benefit, social support, and self-presentation) to continuance intention to use professional SNS. Data were gathered from a questionnaire distributed on LinkedIn and analyzed using PLS-SEM. The findings contribute to the emerging literature on the IS continuance domain, particularly in the area of professional SNS. Furthermore, the study can help professional SNS providers properly manage to retain existing users for sustainable business performance.