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The objective of this paper is to establish a solution strategy for obtaining dual solutions, namely trivial (conventional) and nontrivial (unconventional) solutions, of coupled pore-fluid flow and chemical dissolution problems in heterogeneous porous media.

Through applying a perturbation to the pore-fluid velocity, original governing partial differential equations of a coupled pore-fluid flow and chemical dissolution problem in heterogeneous porous media are transformed into perturbed ones, which are then solved by using the semi-analytical finite element method. Through switching off and on the applied perturbation terms in the resulting perturbed governing partial differential equations, both the trivial and nontrivial solutions can be obtained for the original governing partial differential equations of the coupled pore-fluid flow and chemical dissolution problem in fluid-saturated heterogeneous porous media.

When a coupled pore-fluid flow and chemical dissolution system is in a stable state, the trivial and nontrivial solutions of the system are identical. However, if a coupled pore-fluid flow and chemical dissolution system is in an unstable state, then the trivial and nontrivial solutions of the system are totally different. This recognition can be equally used to judge whether a coupled pore-fluid flow and chemical dissolution system involving heterogeneous porous media is in a stable state or in an unstable state. The proposed solution strategy can produce dual solutions for simulating coupled pore-fluid flow and chemical dissolution problems in fluid-saturated heterogeneous porous media.

A solution strategy is proposed to obtain the nontrivial solution, which is often overlooked in the computational simulation of coupled pore-fluid flow and chemical dissolution problems in fluid-saturated heterogeneous porous media. The proposed solution strategy provides a useful way for understanding the underlying dynamic mechanisms of the chemical damage effect associated with the stability of structures that are built on soil foundations.

The purpose of this study is to determine whether the fine wine market is efficient between homogeneous lots and heterogeneous lots.

Auction price data for homogeneous (or solid) lots of fine wines was analyzed to create price prediction models. Those models were used to predict the expected auction price for the bottles within heterogeneous lots. Lastly, models were created to explain and predict the differences between expected and realized prices for heterogenous wine lots.

The results show that large inefficiencies exist. The more complex and expensive the heterogeneous lot, the greater the discount relative to what would have been realized if the bottles had been sold individually. This discount can exceed 50% of the expected auction price.

Heterogeneous lots may arise as a practical requirement from the auction house. Restaurant buyers probably have little interest in such lots because of the inclusion of wines the restaurant will be unable to sell. Collectors may be uniquely positioned to benefit from this price discount.

These results are unique in the literature, because the price dynamics of heterogeneous (or mixed) lots of fine wines have not previously been studied.

The scaling of social initiatives is of immense importance for public innovation. A key factor in the scaling process is the institutional environment, which plays a key role in supporting and coordinating the scaling process. This environment can be either homogeneous or heterogeneous, i.e. dominated by a few or a variety of public organizations, norms and practices. Both environments have advantages and challenges. However, the academic understanding of the influence of the institutional environment is limited, and a systematic empirical assessment of the relationship between the type of environment and scaling is lacking. The purpose of this paper is to explore the influence of homogeneous and heterogeneous environments on the scaling process of social initiatives.

A comparative qualitative study was conducted in the Dutch social sector, including 48 in-depth interviews with initiators and organizations operating in three different domains – a homogeneous, heterogeneous or mixed environment – in the public sector.

Homogeneous environments are less supportive of scaling social initiatives than heterogeneous environments, as sharing best practices and collaborating with less-familiar organizations is not common. Heterogeneous environments, however, do not provide coordination of the scaling process, with the risk that public funds are used less efficiently and effectively. Institutional environments with a balance of homogeneity and heterogeneity are best suited for scaling social initiatives.

By combining literature from different research areas and conducting extensive empirical research in different domains of the social sector, the authors' study provides a broad and nuanced picture and brings precision to the authors' understanding of the relationships between the institutional environment and scaling in the public sector.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

With technological innovation elements and the competitive market environment becoming increasingly complex, numerous firms utilize network embeddedness to achieve and sustain innovation. However, empirical research has not conclusively established which form of network embeddedness more effectively facilitates corporate innovation. Drawing on the heterogeneous network resources perspective, this study explores the impact of market network embeddedness, technology network embeddedness and their synergy on the green innovation performance of manufacturing small and medium-sized enterprises (SMEs). Furthermore, it investigates the moderating role of resource orchestration capability in these relationships.

Through an online questionnaire survey of Chinese manufacturing SMEs, 293 sample data were collected, and the hierarchical regression analysis was conducted to test the hypothesis.

The results indicate that market and technology network embeddedness significantly enhance green innovation performance, with the former exerting a more significant impact. Furthermore, the synergy between market and technology network embeddedness positively influences green innovation performance. Additionally, resource orchestration capability strengthens the positive effects of both market and technology network embeddedness on green innovation performance, while the moderating effect of resource orchestration capability on the relationship between the synergy of the two and green innovation performance was insignificant.

The study faced many limitations, such as collecting primary data, which relied on a questionnaire only, using cross-sectional data and examining only manufacturing SMEs.

Based on the heterogeneous network resources perspective and integrating social network theory and resource orchestration theory, this study explores the impact of network embeddedness on the green innovation performance of manufacturing SMEs, which sheds new light on the network embeddedness research framework and also enriches the antecedents of green innovation. In addition, this study provides implications on how manufacturing SMEs effectively utilize network embeddedness and resource orchestration capability to enhance green innovation performance.

This paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base.

This paper uses 1,801 patent data in China’s biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base.

The empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant.

This research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base.

This research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

Human-induced marine environmental noise, such as commercial shipping and seismic exploration, is concentrated in the low-frequency range. Meanwhile, low-frequency sound signals can achieve long-distance propagation in water. To meet the requirements of long-distance underwater detection and communication, this paper aims to propose an micro-electro-mechanical system (MEMS) flexible conformal hydrophone for low-frequency underwater acoustic signals. The substrate of the proposed hydrophone is polyimide, with silicon as the piezoresistive unit.

This paper proposes a MEMS heterojunction integration process for preparing flexible conformal hydrophones. In addition, sensors prepared based on this process are non-contact flexible sensors that can detect weak signals or small deformations.

The experimental results indicate that making devices with this process cannot only achieve heterogeneous integration of silicon film, metal wire and polyimide, but also allow for customized positions of the silicon film as needed. The success rate of silicon film transfer printing is over 95%. When a stress of 1 Pa is applied on the x-axis or y-axis, the maximum stress on Si as a pie-zoresistive material is above, and the average stress on the Si film is around.

The flexible conformal vector hydrophone prepared by heterogeneous integration technology provides ideas for underwater acoustic communication and signal acquisition of biomimetic flexible robotic fish.

This paper aims to analyze the heterogeneous effect of prudential regulation on the stability of banks in the West African Economic and Monetary Union (WAEMU).

The author uses in this study individual bank data from balance sheets, income statements of banks in the WAEMU space and annual reports of the banking commission formed into a three-year panel from the period 2017 to 2019. First, this study uses hierarchical clustering based on specific banking characteristics to determine whether the WAEMU region’s banking markets are heterogeneous or not. Second, this study uses quantile regression approach with fixed effects to explore how that prudential regulation affects the conditional distribution of WAEMU bank stability.

The analysis reveals heterogeneity resulting in two distinct groups. Using the quantile regression approach, this study demonstrates that prudential regulation has a significantly more substantial and positive effect on the upper quantiles than on the lower quantiles of the conditional distribution of WAEMU bank stability. Furthermore, the effect of banking regulation also varies among pan-African cross-border banks, national banks and foreign banks. Among these types of banks, pan-African cross-border banks remain the most stable by adopting prudential regulation. The results remain robust and vary across different WAEMU countries.

The contribution of this study to the literature is multifaceted. First, this study uses individual bank-level constituted in panel data from the WAEMU region to assess the effect of prudential regulation on the stability of the WAEMU’s banking sector. This approach allows for a more granular analysis as this study considers individual regional banks’ specific characteristics and behaviors. Second, this study considers the heterogeneous effect of regulation on the stability of banks within the WAEMU space. This means that this study acknowledges that not all banks are affected similarly by prudential regulations, and this research aims to identify and quantify these differences.

Online collaboration in today's world is a topic of genuine interest to Internet researchers. The purpose of this paper is to explore the role of group knowledge heterogeneity (GKH) in open collaboration performance using the mediating mechanisms of group cognition (GC) and interaction to understand the determinants of the success of online open collaboration platforms.

Study findings are based on partial least squares structural equation modeling (PLS-SEM), the formal mediation test and moderating effect analysis from Wikipedia's 160 online open collaborative groups.

For online knowledge heterogeneous groups, open collaboration performance is mediated by both GC and collaborative interaction (COL). The mediating role of GC is weak, while the mediating role of COL is strengthened when knowledge complexity (KC) is higher. By dividing group interaction into COL and communicative interaction (COM), the authors also observed that COL is effective for online open collaboration, whereas COM is limited.

These findings suggest that for more heterogeneous large groups, group interaction would explain more variance in performance than GC, offering an in-depth understanding of the relationship between group heterogeneity and open collaboration performance, answering what determines the success of online open collaboration platforms as well as explaining the inconsistency in prior findings. In addition, this study expands the application of Interactive Team Cognition (ITC) theory to the online open collaboration context.