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This paper examines the determinants of success in seeking local government earmarked funding. We compile data of the aggregate amounts of the New York City Council discretionary expense grants received or requested by each council district every year during 2011-2013. The statistical results show that the allocation of the expense grants are politically motivated with more earmark funds flowing to the districts council leaders and key committee chairpersons represent. Furthermore, constituents of key committee chairpersons are more successful in the earmarking process. Districts with larger African American population have lower success ratios possibly because they request significantly more earmarks. These empirical findings are consistent with anecdotal perceptions that earmarking is not substantially effective in meeting community need.

The Chief Financial Officers Act and subsequent legislation require federal agencies to produce corporate-style financial statements. Arguments for financial statements drew on private sector analogies and suggested policy makers and managers would use the information to make better public policy and management decisions and improve accountability for financial management and program performance. Nearly all major government agencies have unqualified audit opinions and improvements in financial management are claimed. But benefits for policy making and management are not yet well understood. This paper examines the question by comparison with the private sector and by examining what agencies say about the uses and users of financial statement information. The emerging challenge in the evolution of federal financial reporting is to develop better government-specific analytical tools and other financial information for policy makers and managers.

The innovation process demands an interaction between environment agents, knowledge generators and policies of incentive for innovation and not only development by companies. Universities have gradually become the core of the knowledge production system and, therefore, their role regarding innovation has become more important and diversified. This study is aimed at identifying the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

This study is aimed at identifying, based on a systematic literature review, the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

The analysis of the 72 selected articles enabled identifying 15 mechanisms of U–I collaboration, proposing a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process.

In this paper, the authors screened nearly 1,500 papers and analyzed in detail 86 papers addressing U–I collaboration, mechanisms of U–I collaboration and operationalization steps of the U–I collaboration process. This paper provides a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process. This research contributes to both theory and practice by highlighting managerial aspects and stimulating academic research on such timely topic.

Concerning that limited explanation exists examining the function of corporate governance in trust processing within entrepreneurial network development, the purpose of this paper is to explore trust evolution and the role of corporate governance in an entrepreneurial network.

This paper makes an innovative exploration based on the case study of NVC Lighting Holding Limited.

It proposes that in the initial period of network relationship which is based on entrepreneur’s individual social network and embodies sole social network embeddness, entrepreneurial network relies more on affective trust than contractual trust. When stepping into extending period of network relationship which reflects separate embeddedness of social and market network, however, entrepreneurial network has an equal reliance on both affective trust and contractual trust. With further development, when ushering in the phase of maturity which undergoes superimposing embeddedness of both social and market network, entrepreneur network inclines to rely more heavily on affective trust than contractual trust. During the whole process, it can be found that the reliance of entrepreneurial network on trust has the tendency to transfer from affective trust to contractual trust. Furthermore, decreasing of equity ratio of founders and strengthening of controlling right heterogeneity in the corporate governance have facilitated the transfer process and the entrepreneurs’ authority has restraining effect on the evolution of the process.

Through case study, this paper presents the trust evolution process in different stages of entrepreneurial network. Another important theoretic contribution of this paper is that it reveals the function of corporate governance in trust processing within entrepreneurial network development.

Auxetics metamaterials show high performance in their specific characteristics, while the absolute stiffness and strength are much weaker due to substantial porosity. This paper aims to propose a novel auxetic honeycomb structure manufactured using selective laser melting and study the enhanced mechanical performance when subjected to in-plane compression loading.

A novel composite structure was designed and fabricated on the basis of an arrowhead auxetic honeycomb and filled with polyurethane foam. The deformation mechanism and mechanical responses of the structure with different structural parameters were investigated experimentally and numerically. With the verified simulation models, the effects of parameters on compression strength and energy absorption characteristics were further discussed through parametric analysis.

A good agreement was achieved between the experimental and simulation results, showing an evidently enhanced compression strength and energy absorption capacity. The interaction between the auxetic honeycomb and foam reveals to exploit a reinforcement effect on the compression performance. The parametric analysis indicates that the composite with smaller included angel and higher foam density exhibits higher plateau stress and better specific energy absorption, while increasing strut thickness is undesirable for high energy absorption efficiency.

The results of this study served to demonstrate an enhanced mechanical performance for the foam filled auxetic honeycomb, which is expected to be exploited with applications in aerospace, automobile, civil engineering and protective devices. The findings of this study can provide numerical and experimental references for the design of structural parameters.

With the massive use of fossil energy polluting the natural environment, clean energy has gradually become the focus of future energy development. The purpose of this article is to propose a new hybrid forecasting model to forecast the production and consumption of clean energy.

Firstly, the memory characteristics of the production and consumption of clean energy were analyzed by the rescaled range analysis (R/S) method. Secondly, the original series was decomposed into several components and residuals with different characteristics by the ensemble empirical mode decomposition (EEMD) algorithm, and the residuals were predicted by the fractional derivative grey Bernoulli model [FDGBM (p, 1)]. The other components were predicted using artificial intelligence (AI) models (least square support vector regression [LSSVR] and artificial neural network [ANN]). Finally, the fitting values of each part were added to get the predicted value of the original series.

This study found that clean energy had memory characteristics. The hybrid models EEMD–FDGBM (p, 1)–LSSVR and EEMD–FDGBM (p, 1)–ANN were significantly higher than other models in the prediction of clean energy production and consumption.

Consider that clean energy has complex nonlinear and memory characteristics. In this paper, the EEMD method combined the FDGBM (P, 1) and AI models to establish hybrid models to predict the consumption and output of clean energy.

The purpose of this paper is to find the influence of surface bump texture combination characteristics on friction-wear properties so as to provide a basis for the selection of the bump texture combination scheme on the surface of the roll.

In this paper, six groups of different bump texture combination characteristics and their processing methods are introduced, of which three groups are regular distribution with different spacing and three groups are random distribution with different spacing. Then the effect of bump textures with different spacing, regular and random distribution on friction-wear properties was studied by ring block friction-wear experiments.

The results show that the friction coefficient of random distribution texture surface is lower than that of regular texture surface under the same spacing condition. In the regular distribution, the friction coefficient decreases with the increase of texture spacing. In the random distribution, the friction coefficient increases at first and then decreases with the increase of texture spacing. In addition, the wear resistance of textured surface is significantly higher than that of smooth surface because of the higher microhardness of the textured area. The attenuation ratio of textured surface roughness decreases with the increase of the distance between adjacent textures.

At present, the research on roller surface friction-wear is mainly based on the change of the overall surface roughness. However, there are few reports on the influence of the combination characteristics of laser bump texture on friction-wear from the microscopic scale.

The Chinese Government relies heavily on increasing farming scale to address the new challenges facing China’s agricultural development. A basket of polices targeted at increasing farming scale has been issued in recent years thereby resulting to debates over large-scale farming in China. The purpose of this paper is to summarize and highlight the debate over China’s on-going large-scale farming.

This paper contributes to the policy debates over large-scale farming in China by reviewing the existing literature on the positive and negative impacts of large-scale farming on agricultural development, land productivity, and national food security. This paper overviews the different ideas and findings of studies associated with the topic.

The mainstream literature supports the idea that large-scale farming will address the challenges facing China’s agricultural development, while others argue that it crowds out the benefits of small farmers, and raises concerns over inequalities of resources allocation and income distribution, and undermines social stability. While proponents argue that large-scale farming may lead to rise in land productivity, opponents argue that it may result into decrease in land productivity and national grain output.

This paper is a policy review based on existing works. It organizes the different debates over large-scale farming in China, and highlights the key arguments put forward.

This paper aims to develop a novel grey Bernoulli model with memory characteristics, which is designed to dynamically choose the optimal memory kernel function and the length of memory dependence period, ultimately enhancing the model's predictive accuracy.

This paper enhances the traditional grey Bernoulli model by introducing memory-dependent derivatives, resulting in a novel memory-dependent derivative grey model. Additionally, fractional-order accumulation is employed for preprocessing the original data. The length of the memory dependence period for memory-dependent derivatives is determined through grey correlation analysis. Furthermore, the whale optimization algorithm is utilized to optimize the cumulative order, power index and memory kernel function index of the model, enabling adaptability to diverse scenarios.

The selection of appropriate memory kernel functions and memory dependency lengths will improve model prediction performance. The model can adaptively select the memory kernel function and memory dependence length, and the performance of the model is better than other comparison models.

The model presented in this article has some limitations. The grey model is itself suitable for small sample data, and memory-dependent derivatives mainly consider the memory effect on a fixed length. Therefore, this model is mainly applicable to data prediction with short-term memory effect and has certain limitations on time series of long-term memory.

In practical systems, memory effects typically exhibit a decaying pattern, which is effectively characterized by the memory kernel function. The model in this study skillfully determines the appropriate kernel functions and memory dependency lengths to capture these memory effects, enhancing its alignment with real-world scenarios.

Based on the memory-dependent derivative method, a memory-dependent derivative grey Bernoulli model that more accurately reflects the actual memory effect is constructed and applied to power generation forecasting in China, South Korea and India.

The purpose of this paper is to propose a novel hybrid excitation permanent magnet synchronous generator (HEPMSG) utilizing tooth harmonic for excitation, the structural features and operation principle of which are also described.

To obtain the operation performance quickly, this paper derives the mathematical model of the machine system represented by circuit, and analyzes the operation mode of rectifier circuit in the tooth harmonic excitation system, then the standard state equations for each operation mode are obtained. Combining the inductance parameter of this machine with the load resistance and inductance, the armature current waveform, the field current waveform and tooth harmonic winding current waveform are obtained by using the numerical method to solve the standard state equation.

Comparing with the experimental results, the availability of the principle and the validity of the model of the machine system are verified.

This HEPMSG is a new brushless self-excited and self-regulated generator, which is suitable for an independent power source.

Unlike the existing hybrid excitation permanent magnet machine, this HEPMSG utilized the inherent tooth harmonic EMF of the rotor to adjust the air-gap magnetic field of the permanent magnet machine.