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This study aims to respond to the latest research calls to precisely revisit the nexus between corporate green innovation (CGI) and financial decisions through deeply investigating the mediating effect of corporate environmental performance measured by the effectiveness of emission reduction.

This study analyzes nonfinancial-listed firms on the Australian Securities Exchange from 2002 to 2019 using multiple regression analysis on a panel data set. Initially, different static panel data approaches were used. To account for the potential endogeneity issue and generate robust outcomes, the authors apply the one-step system generalized method of moment, two-stage least squares and lagged model approaches.

The results provide a clear indication that the practices of green innovation can favorably contribute to the level of environmental performance, which in turn affect the firm’s ability in opening the new financial doors and shape solid capital structure. In this context, the effective environmental performance fully mediates the nexus between CGI and capital structure of a firm. More importantly, the outcomes are robust and coherent across different estimation techniques.

The originality of this study lies in its utilization of mediation analysis to explore the relationship between CGI and a firm's financial structure. This approach distinguishes it from previous research by offering a thorough and nuanced understanding of how green innovation practices influence the financing decisions of a firm.

To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.

First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.

The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.

This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

Ascorbic acid, a water-soluble antioxidant, is an essential component of the human diet and is known for its potent antioxidant properties against several diseases. In recent years, there has been increasing interest in the development of nonenzymatic sensors due to their simplicity, efficiency and excellent selectivity. The aim of this study is to present a selective and sensitive method for the detection of ascorbic acid in aqueous system using a new electrochemical non-enzymatic sensor based on a gold nanoparticles Au-NPs-1,3-di(4-bromophényl)-5-tert-butyl-1,3,5-triazinane (DBTTA) composite.

Using the square wave voltammetry (SWV) technique, a series of Au-NPs-DBTTA composites were successfully developed and investigated. First, DBTTA was synthesized via the condensation of tert-butylamine and a4-bromoaniline. The structure obtained was identified by IR, 1H NMR and 13C NMR analysis. A glassy carbon electrode (GCE) was modified with 10–1 M DBTTA dissolved in an aqueous solution by cyclic voltammetry in the potential range of 1–1.4 V. Au-NPs were then deposited on the DBTTA/GCE by a chronoamperometric technique. SWV was used to study the electrochemical behavior of the modified electrode (DBTTA/Au-NPs/GCEs). To observe the effect of nanoparticles, ascorbic acid in a buffer solution was analyzed by SWV at the modified electrode with and without gold nanoparticles (Au-NPs).

The DBTTA/Au-NPs/GCE showed better electroanalytical results. The detection limit of 10–5 M was obtained and the electrode was proportional to the logarithm of the AA concentration in the range of 5 × 10⁻³ M to 1 × 10⁻¹ with very good correlation parameters.

It was also found that the elaborated sensor exhibited reproducibility and excellent selectivity against interfering molecules such as uric acid, aspartic acid and glucose. The proposed sensor was tested for the recognition of AA in orange, and satisfactory results were obtained.

The 21st century has brought the business model earth-shaking changes, especially since the Corona Virus Disease 2019 (COVID-19) epidemic at the end of 2019. Now, the epidemic normalization is slowing down China's rapid development. However, technological development, like artificial intelligence (AI), is unstoppable and is transforming China's economic growth modes from factor-driven to innovation-driven systems. Therefore, it is necessary to study further the new changes in labor entrepreneurship and innovation business models and their mechanism of action on economic growth.

This work studies how innovative human capital (IHC) uses AI and other scientific and technological (S&T) innovation technologies to promote China's innovation-driven economic growth model transformation from the labor entrepreneurship and innovation perspective.

The research shows that the entrepreneurial innovation ability of IHC can increase marginal return and output multiplier effect. It changes the traditional business model and promotes China's economic growth and innovation development. At the same time, this work analyzes China's inter-provincial panel data through the panel smooth transition regression (PSTR) model. It concludes that there is a nonlinear relationship between IHC and the output of innovative achievements. The main body presents three stages of nonlinear changes: first rising, then slightly declining, and rising so far.

The finding provides a direction for solving the problem of slow economic growth and accelerating the transformation of economic growth mode under epidemic normalization.

This paper aims to illuminate the nuanced dynamics of green supply chain management (GSCM), specifically focusing on the intersections of supplier relationships, supplier governance and organizational agility. Recognizing a gap in the understanding of how these elements confluence to promote green purchasing, the paper uses a quantitative study on data collected from the Australian food industry. Advanced analysis techniques provide empirical evidence underscoring the pivotal roles these elements play, expanding on current GSCM literature within a resource-based view.

This study, based on a questionnaire sent to Australian food professionals, used higher-order reflective constructs to assess supplier relationships and governance. Data was analyzed using partial least squares structural equation modeling and Hayes PROCESS, considering factors like firm revenue and manager experience. Both the reliability of measures and mediation hypotheses were stringently validated using established guidelines.

The comprehensive study validated supplier governance's key influence on green purchasing and supplier relationships. Notably, organizational agility emerged as a crucial mediator, underscoring the interplay of these constructs. Concurrently, the reflective measurement model exhibited robust validity and reliability. Interestingly, demographic factors such as company size, revenue and managerial experience showed no discernible impact on green purchasing practices.

In the Australian food sector, supplier governance and relationships are pivotal for advancing green purchasing. This study emphasizes the value of organizational agility in amplifying these practices. Managers, when aligning with supplier relationships enhanced by communication and mutual aid, can foster robust green initiatives. Embracing these insights and the critical importance of supplier governance, managers can drive more sustainable, informed supply chain decisions in the industry.

In pursuit of understanding the relationship between supplier governance, supplier relationships and green purchasing, this research uniquely situates itself within the resource-based view (RBV) to reveal critical theoretical and practical implications. By focusing on the Australian food industry, the study spotlights the often-overlooked mediating role of organizational agility in linking supplier relationships with green purchasing efforts. In doing so, this research not only strengthens the argument for fortified supplier relationships – as a catalyst for enhancing agility and thereby green practices – but also re-contextualizes the RBV in a fresh light. This new perspective provides managers with an enriched model, emphasizing the imperative of solid supplier governance for sustainable, agile and green supply chain operations in the food domain.

The purpose of this study is to establish a novel airfoil icing prediction model using deep learning with geometrical constraints, called geometrical constraints enhancement neural networks, to improve the prediction accuracy compared to the non-geometrical constraints model.

The model is developed with flight velocity, ambient temperature, liquid water content, median volumetric diameter and icing time taken as inputs and icing thickness given as outputs. To enhance the icing prediction accuracy, the model involves geometrical constraints into the loss function. Then the model is trained according to icing samples of 2D NACA0012 airfoil acquired by numerical simulation.

The results show that the involvement of geometrical constraints effectively enhances the prediction accuracy of ice shape, by weakening the appearance of fluctuation features. After training, the airfoil icing prediction model can be used for quickly predicting airfoil icing.

This work involves geometrical constraints in airfoil icing prediction model. The proposed model has reasonable capability in the fast assessment of aircraft icing.

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

The nickel-based alloys Inconel 625 and Inconel 718 stand out due to their high strength and corrosion resistance in important industries like aerospace, aviation and automotive. Even though they are widely used, current techniques of producing materials that are difficult to cut pose several problems from a financial, ecological and even health perspective. To handle these problems and acquire improved mechanical and structural qualities, laser powder bed fusion (LPBF) has been widely used as one of the most essential additive manufacturing techniques. The purpose of this article is to focus on the state of the art on LPBF parts of Inconel 625 and Inconel 718 for microstructure, mechanical behavior and postprocessing.

The mechanical behavior of LPBF-fabricated Inconel is described, including hardness, surface morphology and wear, as well as the influence of fabrication orientation on surface quality, biocompatibility and resultant mechanical properties, particularly tensile strength, fatigue performance and tribological behaviors.

The postprocessing techniques such as thermal treatments, polishing techniques for surface enhancement, mechanical and laser-induced peening and physical operations are summarized.

The highlighted topic presents the critical aspects of the advantages and challenges of the LPBF parts produced by Inconel 718 and 625, which can be a guideline for manufacturers and academia in practical applications.

This paper aims to deepen the understanding of the service design concept by critically analyzing the existing servitization literature. The paper’s main purpose is to structure service design and offer a clear understanding of how it should be applied.

A systematic literature review was conducted within servitization literature to understand the evolution of the service design concept. The authors use service design pillars (i.e. user-centered, co-creative, sequencing, evidencing and holistic) as a theoretical framework to explain how service design should be effectively incorporated into the servitization journey.

The findings expose a discordant interpretation of the pillars underpinning service design, revealing a paradoxical comprehension that jeopardizes its practical advancement within the servitization literature. The authors propose that service design should first be seen holistically, then target user-centered practices for sequencing service development steps, and finally, co-creating with partners to make the service evident to users. Furthermore, the authors contextualize service design within contemporary and traditional service-related issues such as servitization innovation, customer experience, service-dominant logic, service ecosystems and digital transformation.

This research pinpoints the service design concept’s shortcomings in the servitization literature. The study promotes a critical reflection on the service design concept and its current application, providing avenues for future research.