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Chirpine ( Pinus roxburghii ), also known as longleaf Indian pine, is the most tapped pine species in several Asian countries, including Nepal, for its high resin yield. Resin tapping in Chirpine trees has been a longstanding practice in mid-hills of Nepal for the last several decades. This study aimed to evaluate the social impacts of resin tapping from Chirpine forests on the rural socio-ecological dynamics of Nepal’s mid-hills. Using a representative sample of 302 forest users from 20 community forest user groups in three far-western districts, we applied the structural equation modeling approach to examine both the positive and negative perceived effects of resin tapping on the rural livelihoods. While respondents acknowledged the positive impacts of resin tapping on local employment and community development activities, many respondents stressed the negative environmental outcomes, including increased fire incidents, water scarcity, and higher mortality of pine trees. Results suggest that rich and male respondents are less likely to realize the negative impacts of resin tapping, whereas a member of community forest user groups is more likely to view resin tapping favorably. Understanding the perspectives of forest users on resin tapping within evolving socio-ecological dynamics shaped by changing social, demographic, and economics factors is critical for ensuring the sustainability of Chirpine based human-nature interactions.

Short-term load forecasting of distributed user groups is crucial for the efficient operation of electricity markets, but existing methods mainly rely on intra-group consistency while neglecting inter-group correlations, which limits the utilization of cross-group information and reduces forecasting accuracy. To overcome these limitations, this study introduces a clustering and multi-task learning-based framework for short-term load forecasting of distributed user groups. First, historical load data are clustered to form representative consumption groups. Next, a Transformer encoder is used as a hard parameter shared network for multi-task learning. Within the multi-task framework, we apply dynamic task weighting and task-specific prediction heads, which balance multi-task losses while optimizing the forecasting performance of each group. Moreover, a filter-attention mechanism and an Inception convolution module are introduced into the encoder to improve local pattern extraction and multi-scale feature fusion. Experiments conducted on two publicly available datasets show that, for the London smart meter dataset, the MAE values of the clusters are 0.2858 and 0.4312, and the RMSE values are 0.5042 and 0.5266. On different clusters of the UCI electricity load dataset, the MAE values are 0.1617, 0.1554, and 0.2608, and the RMSE values are 0.2299, 0.2130, and 0.3678, respectively. These results demonstrate that our method outperforms baseline models and significantly improves the accuracy of distributed user short-term load forecasting in electricity markets.

The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges.

Meaningful engagement and partnerships with people who use drugs are essential to conducting research that is relevant and impactful in supporting desired outcomes of drug consumption as well as reducing drug-related harms of overdose and COVID-19. Community-based participatory research is a key strategy for engaging communities in research that directly affects their lives. While there are growing descriptions of community-based participatory research with people who use drugs and identification of key principles for conducting research, there is a gap in relation to models and frameworks to guide research partnerships with people who use drugs. The purpose of this paper is to provide a framework for research partnerships between people who use drugs and academic researchers, collaboratively developed and implemented as part of an evaluation of a provincial prescribed safer supply initiative introduced during dual public health emergencies (overdose and COVID-19) in British Columbia, Canada. The framework shifts from having researchers choose among multiple models (advisory, partnership and employment) to incorporating multiple roles within an overall community-based participatory research approach. Advocacy by and for drug users was identified as a key role and reason for engaging in research. Overall, both academic researchers and Peer Research Associates benefited within this collaborative partnerships approach. Each offered their expertise, creating opportunities for omni-directional learning and enhancing the research. The shift from fixed models to flexible roles allows for a range of involvement that accommodates varying time, energy and resources. Facilitators of involvement include development of trust and partnering with networks of people who use drugs, equitable pay, a graduate-level research assistant dedicated to ongoing orientation and communication, technical supports as well as fluidity in roles and opportunities. Key challenges included working in geographically dispersed locations, maintaining contact and connection over the course of the project and ensuring ongoing sustainable but flexible employment.

In social-enhanced recommendation systems such as Twitter and Weibo, users could get information from both personalized recommendation and social diffusion modules. In real-world scenarios, the user-group-user based social diffusion plays an essential role to efficiently broadcast information to groups of target users. Through this diffusion path, users first click items provided by the recommendation module, and then share the clicked items to the target user groups. Other users in the group can click the shared items, and return back to the recommendation module for more contents and related items. However, most social-enhanced recommendation systems merely focus on the recommendation module that they can directly influence, ignoring explicitly modeling and predicting for the social diffusion module. In this work, we propose a novel Group-based social diffusion (GSD) model, which aims to jointly optimize the click, share, and return stages in social-enhanced recommendation. We design a heterogeneous ternary graph neural network to jointly model the complex binary and ternary relations among users, items, and groups. We conduct extensive experiments and achieve significant improvements on all click, share, and return prediction tasks, and also achieve promising results on a new full-chain social impact prediction task.

The community forests (CF) in Nepal, facilitated by Community Forest User Groups (CFUGs), is widely recognized as an effective model of community-based forest management. Despite this recognition, there is a notable lack of comprehensive studies assessing the performance of CFUGs in sustaining community forests. Addressing this gap, this study examined twenty-two indicators across five performance criteria, such as user group management, forest management, financial management, livelihood management, and collaboration and networking management, within four CFUGs in central Nepal. Data were collected through household surveys (n = 275) and focus group discussions (n = 4), and indicators of performance criteria were assessed using a Likert scale. Reliability was measured using the coefficient of Cronbach’s alpha. ANOVA was employed to compare mean performance criteria across the four CFUGs, providing an evaluative perspective on overall CFUG performance. The findings underscored collaboration and networking management as high performers, showing an index value of 0.71, while user group management exhibited moderate performance with an index value of 0.56, alongside other moderately performing criteria. Noteworthy disparities were evident across the four performance criteria (p < 0.01), with the exception of collaboration and network management. Approximately 55% of the indicators were rated low to moderate, revealing CFUGs’ deficiencies in regular functions, limited uptake of adaptive and market-oriented management practices, minimal contributions to biodiversity conservation, insufficient capacity for forest revenue generation and mobilization, and restricted income generation and benefit-sharing with communities. The absence of timely and pertinent actions further stifled interaction between CFUGs and community forests, undermining the potential for revenue generation, job creation, and collective actions essential for productive community forest management. Prioritization of the indicators based on the performance index value offers critical policy direction to ensure CFUG sustainability and augment participatory management of common pool resources. Strategies to address identified weaknesses and build on strengths are essential for the success of Nepal’s community forests.

[Purpose/Significance] The development of the Internet and digital economy has brought new opportunities for the development of digital marketing. At the same time, digital marketing is the implementer and forerunner of digital economy. Digital marketing is an important way to promote the digital transformation of enterprises. Therefore, the research of this paper has certain significance for the development of digital economy and digital marketing as well as the digital transformation of enterprises. [Method/Process] In line with the development direction and policy of digital marketing, this paper analyzes the policy direction of digital marketing and reviews and summarizes the enlightenment of the policy. Combined with the development process of the Internet technology, it visually reviews the evolution of digital marketing practice, providing reference for digital marketing research and guidance for the practice of digital marketing. Following the path of user-preference-evaluation-promotion, the research emphases of digital marketing were analyzed and reviewed. [Results/Conclusions] In terms of policy direction, the policy enlightenment of digital marketing can be summarized as follows: (1) Digital marketing should serve the national strategy and accelerate the construction and development of digital economy. (2) Data security belongs to the national strategy, and privacy security is the basis of digital marketing. (3) Connectivity builds a new ecology and new way of digital marketing, and the whole-area marketing reshapes the brand growth. (4) It is suggested to vigorously develop digital marketing to promote the rise of domestic products. In terms of practical development, the practical development of digital marketing is divided into four stages: one-way marketing, interactive marketing, precision marketing and smart marketing. The practical development of digital marketing can be summarized as follows: (1) The application scope of digital marketing is more extensive, involving all aspects of market behavior, such as medical industry, supply chain, and agricultural products. (2) The current focus of digital marketing has changed from the original dissemination of customer acquisition to user operation, the whole process of interaction, user experience, etc. (3) Data can drive marketing. The core of digital marketing is the analysis and mining of data. The current digital marketing practice focuses more on the collection and analysis of user data and the generation of marketing strategies and behaviors driven by data. In terms of research focus, it can be divided into the research of digital community user group, digital community user preference, consumption potential evaluation and development, and digital marketing effectiveness evaluation.

In recent years, with the development of social media platforms, more and more people express their emotions online through short messages. It is quite valuable to detect emotions and relevant topics from such data. However, the feature sparsity of short texts brings challenges to joint topic-emotion models. In many cases, it is necessary to know not only what people think of specific topics, but also which individuals have similar feedback, and what characteristics of these users have. In this paper, we propose a user group based topic-emotion model named UGTE for emotions detection and topic discovery, which can alleviate the above feature sparsity problem of short texts. Specifically, the characteristics of each user are used to discover groups of individuals who share similar emotions, and UGTE aggregates short texts within a group into long pseudo-documents effectively. Experiments conducted on a real-world short text dataset validate the effectiveness of our proposed model.

By providing an overview of library services as well as the building layout, the library orientation can help newcomers make optimal use of the library. The benefits of this outreach can be curtailed, however, by the significant staffing required to offer in-person tours. One academic library overcame this issue by turning to user experience research and gamification to provide an individualized online library orientation for four specific user groups: undergraduate students, graduate students, faculty, and community members. The library surveyed 167 users to investigate preferences regarding orientation format, as well as likelihood of future library use as a result of the gamified orientation format. Results demonstrated a preference for the gamified experience among undergraduate students as compared to other surveyed groups.