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Eco-innovation products, which means achieving more efficient and responsible use of resources and reducing the detrimental impact on the environment, can win a competitive advantage for the enterprises. But it is not easy to implement due to the high cost of eco-innovative technologies development, the uncertainty of market needs and return risk of investment. Many enterprises seek collaborations from their upstream suppliers to jointly carry out eco-innovation, such as Apple, IBM and Nike. A unique feature of collaboration is that efforts by one party enhance the marginal value of the other party's efforts. However, the collaboration will make the partner know the eco-innovation technology and prompt the partner to encroach the market to sell competitive products by herself. Motivated by this observation, this paper considers the optimal collaboration strategy on eco-innovation between upstream and downstream supply chain member and the optimal encroachment strategy of upstream supplier in a supply chain.

This paper models a supply chain wherein a supplier provides products or materials for her manufacturer and cooperates with her manufacturer in eco-innovation. Also, the supplier could encroach on the market to sell similar products by herself. Then this paper uses game theory and mathematical modeling to do relative analysis.

The analysis reveals several interesting insights. First, eco-innovation collaboration makes supplier encroachment no longer only rely on the encroachment cost. The delayed realized eco-innovation efficiency information also plays a vital role. Second, different from previous research, the authors find the manufacturer's preference for supplier encroachment depends on the uncertainty of eco-innovation efficiency and potential market demand. Third, both partial and full encroachment strategies of the supplier can effectively improve the eco-innovation level.

The paper is the first to take the interplay between collaboration and encroachment into account in a supply chain. The results caution enterprises and policymakers to take vertical collaboration and delayed realized information into account in the competitive supply chain before making any operational decisions. Furthermore, the authors propose that governmental intervention aimed at stimulating supplier encroachment in appropriate circumstances can contribute to the improved environmental performance of products.

This paper aims to propose and establish a set of new benchmark models to investigate and confidently validate the modeling and prediction of total stray-field loss inside magnetic and non-magnetic components under harmonics-direct current (HDC) hybrid excitations. As a new member-set (P21^e) of the testing electromagnetic analysis methods Problem 21 Family, the focus is on efficient analysis methods and accurate material property modeling under complex excitations.

This P21^e-based benchmarking covers the design of new benchmark models with magnetic flux compensation, the establishment of a new benchmark measurement system with HDC hybrid excitation, the formulation of the testing program (such as defined Cases I–V) and the measurement and prediction of material properties under HDC hybrid excitations, to test electromagnetic analysis methods and finite element (FE) computation models and investigate the electromagnetic behavior of typical magnetic and electromagnetic shields in electrical equipment.

The updated Problem 21 Family (V.2021) can now be used to investigate and validate the total power loss and the different shielding performance of magnetic and electromagnetic shields under various HDC hybrid excitations, including the different spatial distributions of the same excitation parameters. The new member-set (P21^e) with magnetic flux compensation can experimentally determine the total power loss inside the load-component, which helps to validate the numerical modeling and simulation with confidence. The additional iron loss inside the laminated sheets caused by the magnetic flux normal to the laminations must be correctly modeled and predicted during the design and analysis. It is also observed that the magnetic properties (B27R090) measured in the rolling and transverse directions with different direct current (DC) biasing magnetic field are quite different from each other.

The future benchmarking target is to study the effects of stronger HDC hybrid excitations on the internal loss behavior and the microstructure of magnetic load components.

This paper proposes a new extension of Problem 21 Family (1993–2021) with the upgraded excitation, involving multi-harmonics and DC bias. The alternating current (AC) and DC excitation can be applied at the two sides of the model’s load-component to avoid the adverse impact on the AC and DC power supply and investigate the effect of different AC and DC hybrid patterns on the total loss inside the load-component. The overall effectiveness of numerical modeling and simulation is highlighted and achieved via combining the efficient electromagnetic analysis methods and solvers, the reliable material property modeling and prediction under complex excitations and the precise FE computation model using partition processing. The outcome of this project will be beneficial to large-scale and high-performance numerical modeling.

Advances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).

First, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.

The results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.

The evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.

This paper aims to explore the characteristics of the co-occurrence network of online tags and propose new approaches of applying social network analysis by utilising social tagging in order to organise data.

The authors collected online resources labelled “tag” from 7 November 2004 to 31 October 2011 from the CiteULike website, comprising 684 papers and their URLs, titles and data on tagging (users, times, and tags). They examined the co-occurrence network of online tags by using the analyses of social networks, including the analysis of coherence, the analysis of centricity and core to periphery categorical analysis.

Some features of the co-occurrence of online tags are as follows: the internet is subject to the “small world” phenomenon, as well as being “scale-free”. The structure of the internet reflects stable areas of core knowledge. In addition to five possible applications of social network analysis, social tagging has the greatest significance in organising online resources.

This research finds that co-occurrence of tags online is an effective way to organise and index data. Some suggestions are provided on the organisation of online resources.

This paper aims to manage the word gap in information retrieval (IR) especially for long documents belonging to specific domains. In fact, with the continuous growth of text data that modern IR systems have to manage, existing solutions are needed to efficiently find the best set of documents for a given request. The words used to describe a query can differ from those used in related documents. Despite meaning closeness, nonoverlapping words are challenging for IR systems. This word gap becomes significant for long documents from specific domains.

To generate new words for a document, a deep learning (DL) masked language model is used to infer related words. Used DL models are pretrained on massive text data and carry common or specific domain knowledge to propose a better document representation.

The authors evaluate the approach of this study on specific IR domains with long documents to show the genericity of the proposed model and achieve encouraging results.

In this paper, to the best of the authors’ knowledge, an original unsupervised and modular IR system based on recent DL methods is introduced.

The purpose of this paper is to investigate the impact of temperature on residential electricity demand in the city of Greater Accra, Ghana. It is believed that the increasing trend of temperatures may significantly affect people’s lives and demand for electricity from the national grid. Given the recurrent electricity crisis in Ghana, this study will investigate both the current and future residential energy demands in the light of temperature fluctuations. This will inform future power generation using renewable energy resources mix to find a sustainable solution to the recurrent energy demand challenges in Ghana. This study will help the Government of Ghana to better understand the temperature dependence of residential energy demand, which in turn will help in developing behavioral modification programs aimed at reducing energy consumption. Monthly data for the temperature and residential electricity consumption for Greater Accra Region from January 2007 to December 2018 obtained from the Ghana Meteorological Service (GMS) and Ghana Grid Company (Gridco), respectively, are used for the analysis.

This study used monthly time series data from 2007 to 2018. Data on monthly electricity demand and temperature are obtained from the Ghana Grid Company and GMS. The theoretical framework for residential electricity consumption, the log-linear demand equation and time series regression approaches was used for this study. To demonstrate certain desirable properties and to produce good estimators in this study, an analysis technique of ordinary least squares measurement was also applied.

This study showed an impact on residential electricity requirements in the selected regions of Greater Accra owing to temperature change. The analysis suggests a substantial positive response to an increase in temperature demand for residential electricity and thus indicates a growth of the region’s demand for electricity in the future because of temperature changes. As this analysis projects, the growth in the electricity demand seems too small for concern, perhaps because of the incoherence of the mechanisms used to regulate the temperature by the residents. However, two points should be considered when drawing any conclusions even in the case of Greater Accra alone. First, the growth in the demand for electricity shown in the present study is the growth of demand due only to increasing temperatures that do not consider changes in all the other factors driving the growth of demand. The electricity demand will in the future increase beyond what is induced by temperature, due to increasing demand, population and mechanization and other socioeconomic factors. Second, power consumption understated genuine electricity demand, owing to the massive shedding of loads (Dumsor) which occurred in Ghana from 2012 to 2015 in the analysis period that also applies in the Greater Accra region. Given both of these factors, the growth in demand for electricity is set to increase in response to climate change, which draws on the authorities to prepare more critically on capacity building which loads balancing. The results also revealed that monthly total residential electricity consumption, particularly the monthly peak electricity consumption in the city of Accra is highly sensitive to temperature. Therefore, the rise in temperature under different climate change scenarios would have a high impact on residential electricity consumption. This study reveals that the monthly total residential electricity demand in Greater Accra will increase by up to 3.1%.

The research data was largely restricted to only one region in Ghana because of the inconsistencies in the data from the other regions. The only climate variable use was temperature because it was proven in the literature that it was the most dominant variable that affects electricity demand, so it was not out of place to use only this variable. The research, however, can be extended to capture the entire regions of the country if sponsorship and accurate data can be obtained.

The government as the policy and law-making authority has to play the most influential role to ensure adaptation at all levels toward the impact of climate change for residential consumers. It is the main responsibility of the government to arrange enough supports to help residential consumers adapt to climate change and try to make consumers self-sufficient by modification of certain behaviors rather than supply dependent. Government bodies need to carefully define their climate adaptation supports and incentive programs to influence residential-level consumption practices and demand management. Here, energy policies and investments need to be more strategic. The most critical problem is to identify the appropriate adaptation policies that favor the most vulnerable sectors such as the residential sector.

To evaluate both mitigation and adaptation policies, it is important to estimate the effect of climate change on energy usage around the world. Existing empirical figures, however, are concentrated in Western nations, especially the USA. To predict how electricity usage will shift in the city of Greater Accra, Ghana, the authors used regular household electricity consumption data.

The motivation for this paper and in particular the empirical analysis for Ghana is originality for the literature. This paper demonstrates an adequate understanding of the relevant literature in modern times.

The risks associated with digital innovation increasingly challenge value co-creation among stakeholders within the innovation ecosystem. Stakeholder collaboration is helpful in preventing risk occurrence. This study intends to explore the effects of different stakeholder collaboration strategies on risk prevention performance in a digital innovation ecosystem context.

A systematic literature analysis was first conducted to identify risk factors of digital innovation based on the technology–organization–environment (TOE) framework. Then, a bidimensional network model was constructed to visualize the collaborative relationships among stakeholders and the identified risks by focusing on a digital innovation case. The social network analysis method was applied to design stakeholder collaboration strategies from the ego and global network perspectives, and a simulation approach was conducted to evaluate the effects of the strategies on risk prevention performance.

The results validate the positive effect of stakeholder collaboration on risk prevention performance and reveal the important role of network reachability in formulating collaboration strategies. The strategy of strong–strong collaboration strategy can best enhance risk prevention performance like a “Matthew effect” in the digital innovation ecosystem.

First, risk identification based on the TOE framework provides a systematic list of risk factors for future digital innovation risk management research. Second, this study designs stakeholder collaboration strategies from a network perspective to enhance the understanding of the network status of each stakeholder and the network structure of the digital innovation ecosystem. Third, the simulation results reveal the effects of different collaboration strategies on risk prevention performance.

This study aims to evaluate the color accuracy of HP Jet Fusion 580 3D printing, comparing 3D-printed outcomes against original digital input colors.

A custom cyan, magenta, yellow and black (CMYK) and red, green, blue (RGB) color chart was applied to the top, bottom and side surfaces of a 3D model. Four of each model were 3D-printed on a HP Jet Fusion 580, and half the samples were finished with a cyanoacrylate gloss surface finish, while half were left in raw form. A spectrophotometer was used to document CIELAB (L*a*b*) data, and comparisons made to the original input colors, including calculation of ΔE.

The CMYK samples were significantly more accurate than RGB samples, and grayscale samples in both color spaces were the most accurate of all. Typically, CMYK swatches were darker than the input values, and gloss samples were consistently darker than raw samples. The chromaticity (a*b*) range was found to be significantly smaller than what can be achieved digitally, with highly saturated colors unable to be produced by the printer.

This is the first study, to the best of the authors’ knowledge, to characterize the full color spectrum possible with the HP Jet Fusion 580, recommending that designers use the CMYK color space when applying colors and textures to 3D models. A quick-reference color chart has been provided; however, it is recommended that future research focus on developing a color management profile to better map digital colors to the capabilities of the printer.

As an increasingly popular tool for product exploration, online communities have an important impact on consumers’ purchasing decisions. The purpose of this study is to gain a deeper understanding of how visiting behaviors in online communities affect consumers’ offline purchasing behavior. The moderating role of two dimensions of consumer visiting behaviors (visiting depth and visiting breadth) also receives attention. Moreover, the impact of consumer visiting behaviors on offline sales for different types of online communities is also the focus of this paper.

To test the empirical model, the authors collected data on consumers’ visits to an online real estate platform with local housing sales data. In addition to the baseline regression analysis of the data with the help of Stata 17.0, the study also analyzes the robustness of the results through several methods.

The authors focus on an online community for newly-built housing and find that consumer visits to the focal online community have a positive impact on offline sales. Visiting breadth has a negative moderating effect on this relationship, and no statistically significant moderating effect is found for visiting depth. Further, our empirical exploration finds that consumer visits to competitive online communities have a positive impact on offline focal product sales, but visits to complementary online communities have no statistically significant effect on offline sales.

Our findings contribute to the understanding of consumers’ cross-channel purchasing behavior and provide new insights into how visiting behaviors in online communities affect consumers’ purchasing decisions.

Concrete-filled steel tube structures are widely used for their high bearing capacity, good plasticity, good fire resistance and optimal seismic performance. In order to give full play to the advantages of concrete-filled steel tube, this paper proposes a prefabricated concrete-filled steel tube frame joint.

The concrete-filled steel tube column and beam are connected by high-strength bolted end-plate, and the steel bars in the concrete beam are welded vertically with the end-plates through the enlarged pier head. In addition, the finite element software ABAQUS is used numerically to study the seismic performance of the structure.

The ductility coefficient of the joint is in 1.72–6.82, and greater than 2.26 as a whole. The equivalent viscous damping coefficient of the joint is 0.13–3.03, indicating that the structure has good energy dissipation capacity.

The structure is convenient for construction and overcomes the shortcomings of the previous on-site welding and on-site concrete pouring. The high-strength bolted end-plate connection can effectively transfer the load, and each component can give play to its material characteristics.