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Despite the support of digital technology, there is a high degree of ambiguity and fluidity in the boundaries of digital products. This is because the addition of distributed innovation entities has an impact on the scope and scale of digital product innovation. Building upon the knowledge orchestration perspective, this study aims to construct a theoretical model, comprising distributed innovation, knowledge reorchestration and digital product innovation performance, and discuss the moderating roles of intellectual property protection and knowledge exchange activities.

Using a sample of 362 Chinese science and technology enterprises, the scholarship’s framework and hypotheses were tested using regression and bootstrap analysis.

The results confirm that distributed innovation positively enhances enterprises’ digital product innovation performance; knowledge reorchestration plays a partial mediating role in the linkage amongst distributed innovation and digital product innovation performance; and intellectual property protection and knowledge exchange activities negatively and positively moderate the mediating role of knowledge reorchestration amongst distributed innovation and digital product innovation performance, respectively.

This empirical scholarship explores the effect mechanism of intellectual property protection, knowledge exchange activities and knowledge reorchestration on the linkage amongst distributed innovation and digital product innovation performance. This paper expands the theoretical application of distributed innovation, knowledge orchestration and other related theories in the context of the digital economy and further provides a policymaking reference for the improvement of enterprises’ digital product innovations.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The purpose of this study is to explore the effects of aerospike and hemispherical aerodisks on flow characteristics and drag reduction in supersonic flow over a blunt body. Specifically, the study aims to analyze the impact of varying the length of the cylindrical rod in the aerospike (ranging from 0.5 to 2.0 times the diameter of the blunt body) and the diameter of the hemispherical disk (ranging from 0.25 to 0.75 times the blunt body diameter). CFD simulations were conducted at a supersonic Mach number of 2 and a Reynolds number of 2.79 × 10⁶.

ICEM CFD and ANSYS CFX solver were used to generate the three-dimensional flow along with its structures. The flow structure and drag coefficient were computed using Reynolds-averaged Navier–Stokes equation model. The drag reduction mechanism was also explained using the idea of dividing streamline and density contour. The performance of the aero spike length and the effect of aero disk size on the drag are investigated.

The separating shock is located in front of the blunt body, forming an effective conical shape that reduces the pressure drag acting on the blunt body. It was observed that extending the length of the spike beyond a specific critical point did not impact the flow field characteristics and had no further influence on the enhanced performance. The optimal combination of disk and spike length was determined, resulting in a substantial reduction in drag through the introduction of the aerospike and disk.

To predict the accurate results of drag and to reduce the simulation time, a hexa grid with finer mesh structure was adopted in the simulation.

The blunt nose structures are primarily employed in the design of rockets, missiles, and re-entry capsules to withstand higher aerodynamic loads and aerodynamic heating.

For the optimized size of the aero spike, aero disk is also optimized to use the benefits of both.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

This study aims to examine the blockchain landscape in supply chain management by drawing insights from academic and industry literature. It identifies the key drivers, categorizes the products involved and highlights the business values achieved by early adopters of blockchain technology within the supply chain domain. Additionally, it explores fingerprinting techniques to establish a robust connection between physical products and the blockchain ledger.

The authors combined the interpretive sensemaking systematic literature review to offer insights into how organizations interpreted their business challenges and adopted blockchain technology in their specific supply chain context; content analysis (using Leximancer automated text mining software) for concept mapping visualization, facilitating the identification of key themes, trends and relationships, and qualitative thematic analysis (NVivo) for data organization, coding and enhancing the depth and efficiency of analysis.

The findings highlight the transformative potential of blockchain technology and offer valuable insights into its implementation in optimizing supply chain operations. Furthermore, it emphasizes the importance of product provenance information to consumers, with blockchain technology offering certainty and increasing customer loyalty toward brands that prioritize transparency.

This research has several limitations that should be acknowledged. First, there is a possibility that some relevant investigations may have been missed or omitted, which could impact the findings. In addition, the limited availability of literature on blockchain adoption in supply chains may restrict the scope of the conclusions. The evolving nature of blockchain adoption in supply chains also poses a limitation. As the technology is in its infancy, the authors expect that a rapidly emerging body of literature will provide more extensive evidence-based general conclusions in the future. Another limitation is the lack of information contrasting academic and industry research, which could have provided more balanced insights into the technology’s advancement. The authors attributed this limitation to the narrow collaborations between academia and industry in the field of blockchain for supply chain management.

Practitioners recognize the potential of blockchain in addressing industry-specific challenges, such as ensuring transparency and data provenance. Understanding the benefits achieved by early adopters can serve as a starting point for companies considering blockchain adoption. Blockchain technology can verify product origin, enable truthful certifications and comply with established standards, reinforcing trust among stakeholders and customers. Thus, implementing blockchain solutions can enhance brand reputation and consumer confidence by ensuring product authenticity and quality. Based on the results, companies can align their strategies and initiatives with their needs and expectations.

In essence, the integration of blockchain technology within supply chain provenance initiatives not only influences economic aspects but also brings substantial social impacts by reinforcing consumer trust, encouraging sustainable and ethical practices, combating product counterfeiting, empowering stakeholders and contributing to a more responsible, transparent and progressive socioeconomic environment.

This study consolidates current knowledge on blockchain’s capacity and identifies the specific drivers and business values associated with early blockchain adoption in supply chain provenance. Furthermore, it underscores the critical role of product fingerprinting techniques in supporting blockchain for supply chain provenance, facilitating more robust and efficient supply chain operations.

In the process of conveying the solid–liquid two-phase medium of the centrifugal slurry pump, the wear of the flow-passing parts is an important problem affecting its life and safe operation. Therefore, a numerical investigation on the wear characteristics of the centrifugal slurry pump under different particle conditions was conducted.

A solid-liquid two-phase model based on CFD-DEM coupling is established and used to analyze the flow field and the wear characteristics of the flow-passing parts with different particle densities, volume fractions and sizes.

Particle conditions will affect the pump flow field. To analyze the pump wear characteristics, the wear distribution, wear value and cumulative force laws of flow-passing parts under different particle conditions are obtained. In each flow-passing part, with the increase of particle density, volume fraction and size, the wear area is concentrated and the wear depth increases. Under different particle conditions, the wear is mainly on the volute chamber and the blade pressure surface, and the tangential cumulative force of flow-passing parts is much larger than the normal cumulative force.

An accurate model and a coupled simulation method for predicting the wear of the slurry pump are obtained, and the wear characteristic law can provide a reference for the design of the slurry pump to reduce friction.

Hydro-viscous drive (HVD) clutches are widely used in equipment requiring soft start, such as fans and pumps, to transmit torque and adjust speed by changing the gap distance between friction pairs. This paper aims to propose a novel two-parameter evaluation method for HVD during the mixed lubrication stage. The objective is to develop an effective model that establishes the relationship between these parameters and the actual surface topography.

In the presented methods, the fractal features of the real manufacturing surface are calculated based on the power spectrum function by the ultra-depth three-dimensional microscope. After that, the hybrid friction model of the friction plate is established based on mixed elasto-hydrodynamic lubrication theory, boundary friction model and fractal theory. Then the torque and load bearing characteristics of the clutch are obtained, and the influences of the surface fractal features are investigated and discussed. Finally, the Weierstrass–Mandelbrot function is adopted for the surface topography characterization and evaluation.

The results indicate that the proposed method exhibits good accuracy, while the speed difference between the friction pair exceeds 2,500 rpm. It is concluded that this paper proposed a way to evaluate the torque and loading capacity of HVD considering the real manufacturing surface topography and is helpful for surface optimization.

The originality and value of this study lie in its development of a novel torque and load bearing capacity evaluation method for HVD in mixed lubrication stage, considering manufacturing surface topography and describing the real manufacturing surface.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.