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The social media revolution has brought tremendous change in business strategies for marketing and promoting the products and services. Online social networks have become prime choice to promote the products because of the large size of online communities. Identification of seed nodes or identifying the users who are able to maximize the spread of information over the network is the key challenge faced by organizations. It is proved as non-deterministic polynomial-time hard problem. The purpose of this paper is to design an efficient algorithm for optimal seed selection to cover the online social network as much as possible to maximize the influence. In this approach, agglomerative clustering is used to generate the initial population of seed nodes for GA.

In this paper agglomerative clustering based approach is proposed to generate the initial population of seed nodes for GA. This approach helps in creating the initial populations of Genetic algorithm from different parts of the network. Genetic algorithm evolves this population and aids in generating the best seed nodes in the network.

The performance of of proposed approach is assessed with respect to existing seed selection approaches like k-medoid, k-means, general greedy, random, discounted degree and high degree. The algorithms are compared over networks data sets with varying out-degree ratio. Experiments reveal that the proposed approach is able to improve the spread of influence by 35% as compared to contemporary techniques.

This paper is original contribution. The agglomerative clustering-based GA for optimal seed selection is developed to improve the spread of influence in online social networks. This paper is of immense importance for viral marketing and the organizations willing to promote product or services online via influential personalities.

This paper aims to investigate farmers' willingness to adopt new rice varieties and the factors influencing their seed‐selection behaviors.

The Logit model was used to analyze farmers' willingness and the determinants of the behaviors in selecting and using new varieties. A total of 402 farming households in the main rice‐producing areas of Sichuan Province were surveyed.

The results indicate that rice yield and sales, agro‐technicians' popularization, kith and kin's seed purchase behaviors have a positive impact on farmers'choice, while the present production income has a negative effect. But previous seed purchasing behaviors, soil characteristics, media publicity, seed companies' recommendation and age of farmer have either positive or negative impact on farmers' choice.

The paper is however unable to explain the general situation of China so it considers Sichuan province as a case. Increased sample quantities may be necessary in further research.

The paper proposes an hypothesis of farmers' willingness and determinants in choosing new rice varieties and verifies it by Logit model. It first analyzes the farmers'decision‐making behavior in choosing new rice varieties in Sichuan, and investigates the farmers' behavior more scientifically.

The exponential growth in computational capabilities and the increasing reliability of current simulation tools have fostered the use of computational fluid dynamics (CFD) in the design of pioneering aircraft engine architectures, such as the counter rotating open rotor (CROR) engine. Today, this design process is led by tight performance and noise constraints from a very early stage, thus requiring deep investigations of the aerodynamic and acoustic behaviour of the fluid flow. The purpose of this study is to track the trajectory of tip vortices, which is of critical importance to understand and prevent potential vortex–blade interactions with subsequent rows, as this condition strongly influences the aerodynamic and structural performance and acoustic footprints of the engine.

In this paper, a flow feature detection methodology is applied to a particular CROR test case with the goal of visualizing and tracking the development of these coherent structures from the tip of front rotating blades. The suitability and performance of four typical region-based methodologies and one line-based (LB) criteria are firstly evaluated. Then, two novel seeding methodologies are presented as an attempt to improve the performance of the LB algorithm previously investigated.

It was demonstrated that the new seeding algorithms increase the probability of the selected seeds to grow into a tip vortex line and reduce the user’s dependence upon the selection of candidate seeds, providing faster and more accurate answers during the design-to-noise iterative process.

Apart from the new vortex detection initialization methodologies, the paper also attempts to assist the user in the endeavour of extracting rotating structures from their own CFD simulations.

The purpose of this paper is to critically examine the multinational oil companies’ (MOCs) corporate social responsibility (CSR) initiatives in Nigeria. Its special focus is to investigate the impact of the global memorandum of understanding (GMoU) on development of enterprising rural women as custodians of seed, food and traditional knowledge for climate change resilience in the Niger Delta region of Nigeria.

This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 768 rural women respondents were sampled across the rural areas of the Niger Delta region in Nigeria.

The results from the use of a combined propensity score matching and logit model indicated that the meagre interventions of MOCs’ CSR targeted at the empowerment of rural women as custodians of seed, food and traditional knowledge for climate change resilience recorded significant success in improving the role of women in agricultural production, especially in women’s involvement across value chains.

This suggests that any increase in the MOCs’ CSR targeted at increasing rural women’s access to seed preservation facilities, food processing facilities and extension systems that impact a strong body of knowledge and expertise that can be used in climate change mitigation, disaster reduction and adaptation strategies will enhance women’s responsibilities in households and communities as stewards of natural and household resources and will position them well to contribute to livelihood strategies adapted to changing environmental realities.

This implies that MOCs’ GMoUs’ policies and practices should enhance women’s participation, value and recognize women’s knowledge and enable women as well as men farmers to participate in the decision-making process in agriculture, food production and land governance, as women need to be acknowledged and supported as the primary producers of food in the region, able to both cultivate healthy food and climate change resilience through small-scale agro-ecological farming system.

This research contributes to gender debate in agriculture from a CSR perspective in developing countries and explains the rational for demands for social projects by host communities. It concludes that business has an obligation to help solve problems of public concern.

Small farmers, being the primary producers of crops, are the key players in the food supply chain. Yet, they remain the most marginalized in the value chain. The marginalization of small farmers can affect food sustainability. The purpose of this paper is to identify opportunities for bringing more value to small farmers in an agricultural value chain.

This paper makes use of action research, studying the potato value chain, in a developing agricultural country Pakistan. The authors conducted an in-depth study of 37 farmers in four regions, each being a large potato growing ecosystem. The study examined the end-to-end decision-making processes, sources of input (both physical and information), cultivation and sales practices, cost structure, productivity and profitability of the farmers in potato farming.

Large variations exist in the crop yield, cost structure and profitability of farmers within each of and among the four regions due to differences in cultivation practices and approach to sales. There is a significant potential to lower costs, increase yield and enhance overall profitability by using the existing better processes. By addressing the issues faced by small farmers their profits can be potentially doubled. The paper also discusses potential means of recrafting and streamlining the value chain to bring more value to small farmers.

The paper provides a detailed account of how different interventions can increase the value for small farmers. Since the current food supply chain and sustainability are under stress, worldwide, the findings of this study have implications for farmers as well as policy makers.

The literature on streamlining the agricultural value chain and enhancing the share of small farmers is scarce. Improving the value chain and reducing the marginalization of small farmers is an essential step toward increasing food sustainability.

The purpose of this paper is to identify and analyse the news coverage and sentiment of real estate-related trends in Germany. Trends are considered as being stable and long-term. If the news coverage and sentiment of trends underlie cyclicity, this could impact investors’ behaviour. For instance, in the case of increased reporting on sustainability issues, investors may be inclined to invest more in sustainable buildings, assuming that this is of growing importance to their clients. Hence, investors could expect higher returns when a trend topic goes viral.

With the help of topic modelling, incorporating seed words partially generated via word embeddings, almost 170,000 newspaper articles published between 1999 and 2019 by a major German real estate news provider are analysed and assigned to real estate-related trends. Through applying a dictionary-based approach, this dataset is then analysed based on whether the tone of the news coverage of a specific trend is subject to change.

The articles concerning urbanisation and globalisation account for the largest shares of reporting. However, the shares are subject to change over time, both in terms of news coverage and sentiment. In particular, the topic of sustainability illustrates a clearly increasing trend with cyclical movements throughout the examined period. Overall, the digitalisation trend has a highly positive connotation within the analysed articles, while regulation displays the most negative sentiment.

To the best of the authors’ knowledge, this is the first application to explore German real estate newspaper articles regarding the methodologies of word representation and seeded topic modelling. The integration of topic modelling into real estate analysis provides a means through which to extract information in a standardised and replicable way. The methodology can be applied to several further fields like analysing market reports, company statements or social media comments on real estate topics. Finally, this is also the first study to measure the cyclicity of real estate-related trends by means of textual analysis.

The purpose of this paper is to propose a novel method to enhance the spread of messages in social networks by “Spreading Groups.” These sub-structures of highly connected accounts intentionally echo messages between the members of the subgroup at the early stages of a spread. This echoing further boosts the spread to regions substantially larger than the initial region. These spreading accounts can be actual humans or social bots.

The paper reveals an interesting anomaly in information cascades in Twitter and proposes the spreading group model that explains this anomaly. The model was tested using an agent-based simulation, real Twitter data and questionnaires.

The messages of few anonymous Twitter accounts spread on average more than well-known global financial media groups, such as The Wall Street Journal or Bloomberg. The spreading groups (also sometimes called BotNets) model provides an effective mechanism that can explain these findings.

Spreading groups are only one possible mechanism that can explain the effectiveness of spread of tweets from lesser known accounts. The implication of this work is in showing how spreading groups can be used as a mechanism to spread messages in social networks. The construction of spreading groups is rather technical and does not require using opinion leaders. Similar to the case of “Fake News,” we expect the topic of spreading groups and their aim to manipulate information to receive growing attention in public discussion.

While harnessing opinion leaders to spread messages is costly, constructing spreading groups is more technical and replicable. Spreading groups are an efficient method to amplify the spread of message in social networks.

With the blossoming of fake news, one might tend to assess the reliability of news by the number of users involved in its spread. This heuristic might be easily fooled by spreading groups. Furthermore, spreading groups consisting of a blend of human and computerized bots might be hard to detect. They can be used to manipulate financial markets or political campaigns.

The paper demonstrates an anomaly in Twitter that was not studied before. It proposes a novel approach to spreading messages in social networks. The methods presented in the paper are valuable for anyone interested in spreading messages or an agenda such as political actors or other agenda enthusiasts. While social bots have been widely studied, their synchronization to increase the spread is novel.

Application environments of wireless sensor networks (WSNs) include heterogeneous nodes with different packet sizes, transmission abilities and tolerable delay times. This study aims to design a reasonable network topology and transmission timing for these heterogeneous nodes to improve the quality of service (QoS) of networks.

In this paper, the authors treat node urgency and data packets as the basis of network clustering and to extend the network lifetime. The flow, energy consumption and residual energy of a node are included in the cluster head election. We also propose a delay evaluation function.

All the nodes in the network are guaranteed to transmit to the sink nodes efficiently by planning the transmission order in each cluster.

The simulation results show that the proposed method can balance node urgency and data packets path planning, which not only extends the lifetime of the network but also decreases network delay and improves the overall efficiency.

This paper reports on the work done to decompose a large sized solid model into smaller solid components for rapid prototyping technology. The target geometric domain of the solid model includes quadrics and free form surfaces.

The decomposition criteria are based on the manufacturability of the model against a user‐defined manufacturing chamber size and the maintenance of geometrical information of the model. In the proposed algorithm, two types of manufacturing chamber are considered: cylindrical shape and rectangular shape. These two types of chamber shape are commonly implemented in rapid prototyping machines.

The proposed method uses a combination of the regular decomposition (RD)‐method and irregular decomposition (ID)‐method to split a non‐producible solid model into smaller producible subparts. In the ID‐method, the producible feature group decomposition (PFGD)‐method focuses on the decomposition by recognising producible feature groups. In the decomposition process, less additional geometrical and topological information are created. The RD‐method focuses on the splitting of non‐producible sub‐parts, which cannot be further decomposed by the PFGD‐method. Different types of regular split tool surface are studied.

Combination of the RD‐method and the ID‐method makes up the proposed volume decomposition process. The user can also define the sequence and priority of using these methods manually to achieve different decomposition patterns. The proposed idea is also applicable to other decomposition algorithm. Some implementation details and the corresponding problems of the proposed methods are also discussed.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.