With the rising global consumer demand for green and healthy food, the tea industry is facing unprecedented competitive pressure. Therefore, how to build tea enterprises with sustainable competitiveness has become a key issue facing the industry. This paper firstly reviews the concept of traceability systems and their evolution and, based on the theory of enterprise competitive advantage, explores the influence mechanism of traceability as a strategic resource on the long-term competitiveness of tea enterprises; secondly, it analyzes the multi-dimensional role of traceability on enterprise competitiveness from five aspects, namely, quality and safety control and guarantee, brand image shaping and trust construction, market dynamics response and consumer feedback, risk response and product recall, as well as technological innovation and efficiency enhancement; finally, combined with the above analysis, this paper constructs a theoretical framework for the competitiveness of tea enterprises, integrates the impact of traceability in different dimensions, and proposes a multi-level competitiveness enhancement model. Through this framework, tea enterprises can more comprehensively understand and grasp the close relationship between traceability and the long-term competitive advantage of enterprises and then make strategic adjustments according to their own actual situation so as to realize sustainable competitiveness enhancement in the future market competition.

Extensive research on pro-environmental behaviour (PEB) reveals a significant knowledge gap in understanding the influence of social class, perceived status and the middling tendency on pro-environmental behaviour. Using the International Social Survey Programme Environment dataset, and conducting multilevel mixed-effects linear regressions, we find that the middling tendency and biased status perceptions significantly influences pro-environmental behaviour. Those who deflate their social position have higher pro-environmental behavior and this reinforces the idea that pro-environmental behaviour is driven by a post-materialist effect rather than a status enhancement effect. Moreover, the objective middle class is still a stronger contributor to higher PEB levels compared to subjective middle class. We also find the relation between class, status and PEB vary by country. These findings provide vital insights into the intricate and heterogenous dynamics between class, status and pro-environmental behaviour among different countries and shed light on class and status as driving forces behind pro-environmental behaviour.

The employees in academic sector had to face an abrupt change due to Covid-19 pandemic and transformation of education into online and remote learning. This has led to virtual work intensity as an aftermath that negatively influences employees’ job satisfaction. In addition, due to remote working conditions, the lines between work and life had been dimmed and thus, the current situation is important to be addressed for wellbeing of academic staff. This research specifically aims to examine impact of virtual work intensity on job satisfaction among university staff. Furthermore, mediating effect of organizational support and work-life balance on the aforementioned relationship are analyzed to better understand the underlying effects. Through PLS-SEM and using a questionnaire survey, a total of 183 data were collected from teachers and administrative staff of two universities. The results show that virtual work intensity can hinder job satisfaction, while organizational support and work-life balance can improve job satisfaction of academic employees. This is due to the fact that support, and balance act against work intensity that diminishes wellbeing of individuals. This implies the vital role of organizations (e.g., human resource department) in providing support for their staff, and creating an environment, where academic staff can have a better work-life balance, leading to higher rates of job satisfaction as an important factor for psychological wellbeing.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

The fresh dried pollen of grape and seedless grape varieties were used as the research material. Each cultivar was stored at room temperature, 5 C, 0 C, -18 C and -40 C respectively. Sucrose 200g / L + boric acid 50mg / L + agar 8g / L as the nutrient substrate, and the comparative study on the germination and culture of the nuclear breed and the seedless cultivar. The results showed that the pollen viability decreased with the increase of storage time at different temperatures. The pollen viability decreased at -18 C and -40 , and the pollen viability decreased at the same temperature The There was a significant difference in pollen viability between different cultivars at the beginning of storage, and the pollen viability of the kernel breed was higher than that of the seedless grape.

The study examined the socio-demographic factors affecting access to and utilization of social welfare services in Yenagoa Local Government Area of Bayelsa State, Nigeria. Quantitative and qualitative approaches were adopted to select 570 respondents from the study area. Probability and non-probability sampling techniques were adopted in the selection of communities, and respondents. The quantitative data were analyzed using frequency distribution tables and percentages, while chi-square statistic was used to determine the relationship between socio-demographic variables and access to and utilization of social welfare services. The qualitative data were analyzed in themes as a complement to the quantitative data. This study reveals that although all the respondents reported knowing available social welfare services, 44.3% reported not having access to existing social services due to factors connected to serendipity variables, such as terrain condition, ethnicity and knowing someone in government. Therefore, the study recommends that the government and other stakeholders should push for the massive delivery of much-needed social welfare services to address the issue of welfare service deficit across the nation, irrespective of the ethnic group and whether the community is connected to the government of the day or not, primarily in rural areas.