This research endeavors to investigate the causal relationship between climate change and pollution intensity, employing a county-level dataset encompassing meteorological data and air pollution measurements from 2008 to 2017 in China. We discern the immediate causal impact of “local temperature shocks” on pollution intensity, uncovering a positive association between climate change and pollution intensity. Notably, this effect manifests with greater significance in economically underdeveloped regions, the northern provinces, and cities not reliant on resource-based industries. Moreover, we disentangle the influence of extreme heat on air pollution into three distinct facets: scale, technological, and structural effects. Our findings reveal that extreme heat augments energy demand, diminishes energy efficiency, and amplifies coal consumption, consequently elevating pollution intensity. Drawing upon the SSP126 and SSP585 climate change scenarios for future Chinese cities in the short-term (2041–2060), mid-term (2061–2080), and long-term (2081–2100), our simulation outcomes demonstrate that the escalating frequency of hot days will perpetuate an adverse impact on pollution intensity across China's counties. This study presents a fresh perspective on the environmental repercussions of climate change and provides theoretical underpinning for local authorities in their endeavors to attain concurrent reductions in both pollution and carbon emissions.

To effectively address challenges that stem from e-commerce, it is crucial to harness diverse review data from e-commerce platforms. These data support consumers in making informed purchase decisions and aid manufacturers in optimizing product attributes. Incorporating sentiment data from heterogeneous reviews across different time periods into a decision-making framework is a pivotal consideration in purchase decisions and product design. The goal of the study is to establish an online product decision support method grounded in consumer irrational behavior and segmented reviews over time. It aims to offer users reliable and consistent outcomes when making personalized purchase decisions. The probabilistic linguistic term set is employed to represent consumer sentiments with varying degrees of granularity across different time periods. Subsequently, stochastic sampling is utilized to simulate the decision-making process of individual consumers. Regret theory is then applied to analyze consumers' irrational psychological behavior. Building upon heterogeneous data gathered from e-commerce platforms, including review ratings, likes, and follow-up reviews, a multiperiod group decision approach based on maximum similarity and review helpfulness is proposed. This decision-making method is advanced through a decomposition-aggregation process, safeguarding against information distortion and ensuring result reliability. This method provides consumers with product selection solutions across the temporal dimension and serves as a theoretical compass for manufacturers and sellers seeking product enhancement and sales optimization.

The q-rung orthopair uncertain linguistic set, which combines the q-rung orthopair fuzzy set and uncertain linguistic variable, can simultaneously represent the quantitative and qualitative information given by experts. In the procedure of addressing q-rung orthopair uncertain linguistic information, to eliminate extreme evaluation values for arguments and capture the multilayer heterogeneous relationship among the membership function and attributes, q-rung orthopair uncertain linguistic interaction power partitioned Maclaurin Symmetric mean operators are presented in this paper. Firstly, we introduce the interaction operational rules between q-rung orthopair uncertain linguistic sets. Then, we integrate the power average operator and partitioned Maclaurin Symmetric mean (PMSM) operator into a framework and present the power PMSM (PPMSM) operator. Furthermore, we embed the PPMSM operator into the q-rung orthopair uncertain linguistic sets based on the interaction operational laws and propose the q-rung orthopair uncertain linguistic interaction power partitioned Maclaurin Symmetric mean operator and its weighted form. Meanwhile, we analyze some properties and special cases of the proposed operators. Afterward, we report an algorithm that we developed for handling multi-attribute decision-making problems based on the proposed operator. Finally, we conduct case studies and comparison analysis over existing methods to demonstrate the effectiveness and superiority of the proposed algorithm.

This study proposes a novel multiple criteria decision making (MCDM) framework aimed at selecting refrigeration technologies that are both carbon- and energy -efficient, aligning with the UK's net -zero policies and the UN's Sustainable Development Goals (SDGs). Addressing the challenge of a limited number of competing technologies and the need to incorporate diverse stakeholders' perspectives, we design a hybrid DEA-TOPSIS approach utilizing the Feasible Super -Efficiency Slacks -Based Algorithm (FSESBA). FSESBA proves invaluable, especially in scenarios involving super -efficiency or efficiency trend measurement, where addressing undesirable factors may lead to the well-known infeasibility problem. While we establish the theoretical soundness of the DEA-TOPSIS model, we validate the efficacy of our proposed approach through comparative analysis with conventional methods. Subsequently, we evaluate the choices of present and upcoming refrigeration technologies at a leading UK supermarket. Our findings reveal a shift from prevalent HFO-based technologies in 2020 to CO2-based technologies by 2050, attributed to their lower energy usage and GHG emissions. In addition, maintaining current refrigeration systems could contribute to achieving international and national targets to decrease F -Gas refrigerant usage, although net -zero targets will remain out of reach. In summary, our research findings underscore the potential of the introduced model to reinforce the adoption of novel refrigeration system technology, offering valuable support for the UK SDGs taskforces and net -zero policy formulation.

The intelligent strategy of the new energy vehicle (NEV) industry has triggered the rapid prevalence of in-vehicle anthropomorphic artificial intelligence (AI) assistants. There is still a lack of clarity regarding NEV users' attitudes toward this cutting-edge technology and whether they receive a satisfactory intelligent service experience. To circumvent potential emerging technology resistance, in this article, we utilize text analysis techniques for the identification of AI interaction emotions, love and disgust (enablers and inhibitors) with significant influence on user satisfaction, and validates the improving role of multimodality on the effectiveness of anthropomorphic interaction. In addition, this study innovatively constructs a multidimensional corpus of modality × emotion, using structural topic modeling to uncover the constituent elements and real-time changes of love and disgust emotions in different modalities, from which development opportunities and improvement directions for AI anthropomorphic interaction technologies are identified. The findings provide new insights into the application of emotion analysis methods to improve users' intelligent service experience and provide a realistic reference for mitigating emerging technology resistance in the NEV industry.

Data resources, a fundamental component in the digital economy, play a vital role for businesses aiming to establish a lasting competitive edge. A company's data resources can uniquely influence the decisions surrounding products and services within the supply chain. The integrated dual-channel supply chain (DCSC) involves direct online channels utilized by manufacturing service providers alongside offline channels, such as physical retail stores, managed by sales service integrators. The DCSC is capable of performing second-stage data mining services in accordance with the sold products and customer services after the product sales are completed. Subsequently, data resource mining can be achieved through a cross-channel approach. As such, considering the exploration of cross-channel data resources, this study strives to formulate a structure for a dual-channel closed-loop supply chain. Furthermore, utilizing concepts from the Stackelberg and Nash equilibrium game theories, it delves into the analysis of pricing decisions and profit allocation. This examination encompasses distinct closed-loop supply chain configurations, viewed through the lenses of both centralized and decentralized decision-making approaches. In addition, the effects of cross-channel data mining and channel consumption preferences on supply chain decisions are analyzed, and the analysis is conducted in combination with numerical examples. As evidenced by the findings in this investigation, cross-channel data resource mining, consumer channel preference, and the data mining value conversion rate can prominently affect the formulation of pricing strategies and the distribution of profits in closed-loop supply chains. The potential value of data resources can lead to the generation of “external incentives” following the strategy of data resource mining. Furthermore, the data resource mining strategy is promising in stimulating the growth of the product and service markets. Finally, the overall profit of the supply chain is increased with the increase in the efficiency of data resource conversion. Enhancements in the efficacy of data resource conversion correspondingly lead to heightened overall profits within the supply chain.

In this paper, a comprehensive optimization problem is developed for a composite of an assembly line reconfiguration problem with multiple lines and a capacitated lot-sizing problem. Multiple products are considered, whose demand is uncertain and is dynamically forecasted. The production planner is assumed to be risk-averse, and decisions are made contingent upon the risk preference. To model the problem, a stochastic program with two stages is utilized. A solution approach is devised using a divide-and-conquer algorithm, which incorporates a set of valid inequalities. The effectiveness and efficiency of the proposed solution approach are assessed through a series of computational tests. Finally, a case study focusing on an engine production process is presented, leading to the derivation of several valuable insights.

The augmentation of transactional volume within carbon emissions trading systems (ETS) is widely acknowledged as an efficacious mechanism for ensuring the efficient distribution of resources and financial support to corporations, significantly influencing their financing limitations. Despite its relevance, this subject has garnered scant attention in scholarly discourse. This research utilizes a panel dataset comprising Chinese listed firms from 2009 to 2019, applying the difference-in-differences approach to examine the correlation between the magnitude of carbon emissions trading in regional ETS pilots and the evolution of financial constraints. Our analysis reveals that a 1% escalation in carbon transaction volume correlates with a 0.1885% reduction in the financial constraints encountered by companies. This phenomenon is particularly salient among smaller enterprises in the eastern provinces and second-tier urban centers, and those engaged in the primary and secondary sectors. Moreover, our principal results demonstrate resilience across various sensitivity analyses, encompassing common trend scrutiny and alternative methodologies like propensity score matching estimation. The research further delves into the underlying mechanisms by which carbon trading can mitigate a firm’s fiscal pressures. Our examination identifies investment in intangible assets, improved carbon performance, and liquidity enhancement as key conduits. These findings carry substantial policy implications, advocating for governmental initiatives to bolster corporate engagement in ETS, thereby easing financial burdens while concurrently advancing environmental regulation and low-carbon transformation objectives.

It is widely believed that alternative low carbon fuels (ALCF) can be instrumental in achieving the transportation sector’s decarbonization goal. Unlike conventional fossil-based fuels, ALCF can be produced through a combination of different chemical processes and feedstocks. The inherent complexity of the problem justifies the multi-criteria decision-making (MCDM) approach to support decision-making in the presence of multiple criteria and data uncertainty. In this paper, we propose a novel stakeholder participation-based MCDM framework integrating experts' perspectives on ALCF production pathways using the analytics hierarchy process (AHP) and the q-rung orthopair linguistic partition Bonferroni mean (q-ROLPBM) operator. The key merit of our approach lies in treating criteria of different dimensions as heterogeneous indicators while considering the mutual influence between criteria within the same dimension. The proposed framework is applied to evaluate four ALCF production pathways against 13 criteria categorised under economic, environmental, technical, and social dimensions for the case of the United Kingdom (UK). Our analysis revealed the environmental and the economic dimensions to be the most important, followed by the social and technical evaluation dimensions. The e-fuel followed by the e-biofuel are found to be the two top-ranked production pathways that utilise the electrochemical reduction process and its combination with anaerobic digestion. These findings, along with our recommendations, provide decision-makers with guidelines on ALCF production pathway selection and formulate effective policies for investment.

The competitive landscape of multiple e-commerce platforms and the vast amount of product reviews associated with these platforms have supported both consumers’ online shopping decision making and also served as a reference for product attribute performance improvement. This article proposes a sentiment-driven fuzzy cloud multicriteria model for online product ranking and performance to provide purchase recommendations. In this novel model, bidirectional long short-term memory network-conditional random fields, sentiment analysis, and K-means clustering are first integrated to mine product attributes and compute sentiment values based on the reviews from various platforms. Next, considering the confidence of the sentiment value, the cloud model is combined with q-rung orthopair fuzzy sets to define the new concept of the q-rung orthopair fuzzy cloud (q-ROFC) and the interaction operational laws between q-ROFCs are given. The sentiment values of each product attribute from different platforms are cross combined and transformed into a type of q-ROFC, while multiple interactive information matrices are established. To investigate the correlation among homogeneous attributes, the q-ROFC interaction weighted partitioned Maclaurin symmetric mean operator is proposed. Finally, we provide real-world examples of online mobile phone ranking and attribute performance evaluation. The results show that our proposed method offers significant advantages in dealing with customer purchase decisions for online products and problems with performance direction identification. Managerial implications are discussed.

The mechanism of the impact of inter-firm social networks on innovation capabilities has attracted much research from both theoretical and empirical perspectives. However, as a special emerged and developing complex production system, how the scenario factors affect the relationship between these variables has not yet been analyzed. This study identified several scenario factors which can affect the firm's technological innovation capabilities. Take the manufacturing scenario in China as an example, combined with the need for firms' ambidexterity innovation and green innovation capability, a multi-objective simulation model is constructed. Past empirical analysis results on the relationship between inter-firm social network factors and innovation capabilities are used in the model. In addition, a numerical analysis was conducted using data from the Chinese auto manufacturing industry. The results of the simulation model led to several optimization strategies for firms that are in a dilemma of development in the manufacturing scenario.

鉴于新加坡高等教育近些年来在全球范围内取得的显著成绩,以南洋理工大学为例,对其历史和发展现状进行分析,总结出值得我国高校借鉴学习的几点内容,分别从人才引进、自主化办学、国际同行评审、国际化战略和排名提升策略几个方面分析其对于南洋理工大学成功所发挥的作用,并基于此对我国高等教育的发展提出建议,以期全力推进"双一流"高质量建设.

Developing a modern low-carbon economy while protecting health is not only a current trend but also an urgent problem that needs to be solved. The growth of the national low-carbon economy is closely related to various sectors; however, it remains unclear how the development of low-carbon economies in these sectors impacts the national economy and the health of residents. Using panel data on carbon emissions and resident health in 28 province-level regions in China, this study employs unit root tests, co-integration tests, and regression analysis to empirically examine the relationship between carbon emissions, low-carbon economic development, health, and GDP in industry, construction, and transportation. The results show that: First, China's carbon emissions can promote economic development. Second, low-carbon economic development can enhance resident health while improving GDP. Third, low-carbon economic development has a significant positive effect on GDP and resident health in the industrial and transportation sector, but not in the construction sector, and the level of industrial development and carbon emission sources are significant factors contributing to the inconsistency. Our findings complement existing insights into the coupling effect of carbon emissions and economic development across sectors. They can assist policymakers in tailoring low-carbon policies to specific sectors, formulating strategies to optimize energy consumption structures, improving green technology levels, and aiding enterprises in gradually reducing carbon emissions without sacrificing economic benefits, thus achieving low-carbon economic development.

Research on knowledge hiding has largely focused on its antecedents while overlooking its consequences. Drawing on moral cleansing theory, we adopt a “perpetrator-centric view” and posit that employees who engage in playing dumb and evasive hiding–two specific knowledge hiding behaviors that involve deception–will subsequently perform more organizational citizenship behavior directed toward individuals (OCB-I) because they perceive a loss of moral credits following their moral transgression. Further, we propose that the indirect effects are contingent on perpetrators’ moral identity internalization. We tested our hypotheses using a time-lagged research design with a sample of 362 respondents from a large pharmaceutical group company. Consistent with our hypotheses, we found that employees who engaged in playing dumb and evasive hiding subsequently exhibited more OCB-I as they perceived a loss of moral credits, whereas employees who engaged in rationalized hiding did not. In addition, the positive relationships between playing dumb and evasive hiding with perceived loss of moral credits were stronger when perpetrators had high moral identity internalization, as were the indirect effects of playing dumb and evasive hiding on OCB-I via perceived loss of moral credits. Our research contributes to the understanding of when and how engaging in knowledge hiding affects perpetrators and their compensatory behaviors toward coworkers.

The mutual influence and complementarity of technologies between different industries are becoming increasingly prominent. Revealing the topic evolution of technology linkages between industries is the foundation for understanding the technological development trend of the industry. Although numerous works have focused on technology topic mining and its evolution characteristics, these works have not accurately represented the interindustry technology linkage, analyze the related topics and even ignored the technological development characteristics hidden in the topic evolution pathway. Since the Lingo algorithm fully considers the time-series characteristics of the topics, and the knowledge evolution theory can reveal three inherent characteristics in the evolution of knowledge topics, namely, "stability, heredity, and variability," this article aims to combine the Lingo algorithm and the knowledge evolution theory to analyze the topic evolution of interindustry technology linkages. Additionally, because three-dimensional (3-D) printing technology has significant interdisciplinary and cross-industry characteristics, a wide range of application fields, and various interindustry technology linkages, 3-D printing technology is used for empirical analysis. The empirical results show that the key topics of interindustry technology linkages in 3-D printing include model design, manufacturing methods, manufacturing equipment, manufacturing material, and application. In addition, all these topics have the development feature of heredity. However, the topic of manufacturing materials presents significant variability, the topic of manufacturing methods has the strongest stability, and multiple subtopics of the five topics show variability and genetic intersection.

The digital transformation of enterprises has become an inevitable development trend and one of the key driving forces that promotes the sustainable development of enterprises. However, due to the many obstacles of financial burdens, technical thresholds, and talent shortages, digital transformation has become a challenging task for entrepreneurial Small and Medium-Sized Enterprises (SMEs). Additionally, many competitive digital transformation solutions on the market cause confusion when enterprises must choose one. This study drew a new information error-driven T-spherical fuzzy cloud algorithm to evaluate digital transformation solutions of entrepreneurial SMEs and support its selection. First, an evaluation index system for the digital transformation solution of entrepreneurial SMEs was established from four aspects. Then, a new concept of a T-spherical fuzzy cloud was defined to represent the evaluation information of the indicators. Additionally, a T-spherical Fuzzy Cloud Weighted Heronian Mean (T-SFCWHM) operator was used to aggregate the evaluation information. Afterward, an evaluation and selection decision framework for the digital transformation solution of entrepreneurial SMEs based on the T-SFCWHM operator was developed. Further, a practical example was given to illustrate the effectiveness of the proposed method. Finally, a discussion of the findings in our study was conducted, and the conclusions were summarized.

In this paper, a comprehensive production planning problem under uncertain demand is investigated. The problem intertwines two NP-hard optimization problems: an assembly line balancing problem and a capacitated lot-sizing problem. The problem is modelled as a two-stage stochastic program assuming a risk-averse decision maker. Efficient solution procedures are proposed for tackling the problem. A case study related to mask production is presented. Several insights are provided stemming from the COVID-19 pandemic. Finally, the results of a series of computational tests are reported.

The maturity of Industry 4.0 technologies such as the Internet of Things and cloud computing has accelerated the development of various platforms. In new energy vehicle (NEV) recommendation platforms, customer reviews have been well recognized for their ability to provide value-added information to customers interested in purchasing NEVs. However, the countless NEV reviews on recommendation platforms make it difficult for consumers to select their preferred NEV. The existing NEV recommendation platforms also do not automatically perform fine-grained sentiment analysis of the product attributes contained in reviews. Consequently, they cannot provide personalized purchase recommendations for consumers. To this end, this study aims to propose a product purchase decision support method based on sentiment analysis and multi-attribute decision-making to improve the accuracy of personalized NEV recommendation platforms. Sentiment analysis was conducted on the attribute reviews of NEVs on a product recommendation platform. Subsequently, the positive, negative, and neutral sentiment ratios obtained based on sentiment analysis were regarded as q-rung orthopair fuzzy numbers. The ratios were then recognized as cumulative prospect theory (CPT) inputs. The prospect values of each NEV under each attribute were calculated and further aggregated into a Muirhead mean operator to finally obtain the product rankings. This method was used to portray the consumers' decision-making process considering various situations and irrational psychological factors (e.g., risk-preference attitude). The results show that our proposal can recommend NEVs that are more consistent with consumers' personalized requirements. To conclude, our study can enhance the decision-making support capacity of product recommendation platforms by providing sentiment analysis and capturing customers' preferences for product attributes. Additionally, it can recommend more suitable NEVs to meet personalized customer requirements.

In the era of big data, the data in many business scenarios are characterized by a small number of labelled samples and a large number of unlabelled samples. It is quite difficult to classify and identify such data and provide effective decision support for a business. A commonly employed processing method in this kind of data scenario is the disagreement-based semisupervised learning method, i.e., exchanging high-confidence samples among multiple models as pseudolabel samples to improve each model’s classification performance. As such pseudolabel samples inevitably contain label noise, they may interfere with the subsequent model learning and damage the robustness of the ensemble model. To solve this problem, a semisupervised classification algorithm based on noise learning theory and a disagreement cotraining framework is proposed. In this model, first, the probably approximately correct (PAC) estimation theory under label noise conditions is applied, the relationship between the label noise level and model robust estimation in the process of multiround cotraining is discussed, and a disagreement elimination algorithm framework based on multiple-model (feature argument and select (FANS) algorithm and L1 penalized logistics regression (PLR) algorithm) cotraining is constructed based on this theoretical relationship. The experimental results show that the algorithm proposed in this paper gives not only a high-confidence sample set that meets the upper bound constraint of the label noise level but also a robust ensemble model capable of resisting sampling bias. The work performed in this paper provides a new research perspective for semisupervised learning theory based on disagreement.

Human activity recognition (HAR) is an emerging field that identifies human actions in different settings. This activity is recognized by sensors placed in the room or residence where we wish to observe human action. Real-world applications and automation employ activity recognition to detect anomalous behavior. For example, the anomalous behavior of patients such as walking while advised to rest in bed and falling elderly people need to be monitored carefully in hospitals as well as in home-based monitoring systems. Security, healthcare, human interaction, and computer vision use it. The activity is monitored through sensors and cameras. There is no general, explicit approach for inferring human activities from sensor data. Sensor data and heuristics present technological challenges. Several elements must be evaluated to build a reliable activity recognition system. Factors such as storage, connectivity, processing, energy efficiency, and system adaptability are important. Deep learning systems can better recognize human activities from earlier datasets. In this study, the hybrid One Dimensional Convolution Neural Network with Long Short Term Memory (LSTM) classifier is employed to improve the performance of HAR. It offers a method for automatically and data-adaptively removing reliable characteristics from raw data. This model proposes a two-way classification for abstract and individual activity monitoring. Human activities such as walking, sitting, walking downstairs, walking upstairs, laying, and standing along with mobile phone usage are considered in this study. We also compare state-of-the-art algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Long Short Term Memory (LSTM), and Convolutional Neural Network (CNN). The UCI-HAR dataset is used for recognizing human activity in the proposed work. Features such as mean, median, and autoregressive coefficients are derived from the raw data and processed with principal component analysis to make them more reliable. The LSTM model accepts a series of activities, whereas the CNN accepts a single input. The CNN takes the single input data and each of the outputs is forwarded to the LSTM model, which classifies the activity. The Hybrid model achieves 97.89% accuracy with the new feature selection methods, whereas the CNN and LSTM individually produce 92.77% and 92.80% accuracy.