Exploring Visitors’ Pro-environmental Behaviors at Urban Forest Destinations

Our theoretical framework was intricately crafted to illuminate the decision-making dynamics of visitors in urban forest destinations. The primary objectives of this study were to scrutinize visitors' pro-environmental behavior (PRB), employing the Theory of Reasoned Action (TRA) as a foundational framework, and extending it by incorporating key factors such as connectedness to nature, biospheric value, environmental empathy, attitude, positive anticipated emotion, moral norms, subjective norms, and natural/local resource conservation intentions, within the unique context of urban forest destinations. Utilizing GSCAM, the framework’s measurement quality is affirmed as being adequate. Findings from the structural model and necessary condition analysis (NCA) robustly supported the hypothesized associations within the proposed theoretical framework. Notably, cognitive and affective appraisals, along with moral considerations, emerged as salient in shaping visitor intentions. Furthermore, empirical support was observed for the hypothesized impact of natural resource conservation intentions on determining PRB, providing nuanced insights into the factors influencing pro-environmental actions in urban forest destinations. This comprehensive approach not only enhances theoretical understanding but also offers practical applications for promoting sustainable behaviors in these unique settings

The role of bovine seminal plasma in fertility

Bovine seminal plasma (SP) is known to have an effect on spermatozoa and the female reproductive tract. The differences in fertility among individuals could be due to variations in SP composition and its effect on both spermatozoa and the inseminated female. Single Layer Centrifugation (SLC) has been shown to select the most robust spermatozoa from the ejaculate and can be used to separate spermatozoa from SP. The purpose of this thesis was to study the various effects of bovine SP. The thesis was divided into 4 studies: Study I was to determine the effects of SLC on post-thaw sperm quality. Study II investigated the effect of adding SP back to SP-free sperm samples. Study III investigated the effect of bovine SP on bovine endometrial epithelial cells (bEEC) in culture. Study IV determined the effect of season and SLC on sperm quality. Our results show that bull spermatozoa selected by SLC had a higher proportion with high mitochondrial membrane potential (MMP) and a higher superoxide production than controls. The SLC-selected samples had a higher proportion of spermatozoa with normal morphology and a lower proportion with bent tails than controls; they had better kinematics than controls. However, sperm viability and chromatin integrity were not different between treatments. Adding 5% bovine SP had a beneficial effect on sperm velocity. Moreover, there was a beneficial effect of adding 5% heterologous SP from high fertility bulls on sperm velocity but a deleterious effect on chromatin integrity. Total cell number and viability of bEEC after challenge with 1% SP from either high or low fertility bulls did not differ from controls. In contrast, challenge with 4% SP from high or low fertility bulls (300H or 300L) negatively affected bEEC cell number and viability. Challenge with 300L had a greater adverse effect than 300H. There were differences in semen characteristics and sperm morphology among seasons. However, sperm kinematics, viability, chromatin integrity and MMP were not different between seasons. In conclusion, these results indicated that SLC can be used to enhance bull sperm quality. Moreover, adding bovine SP prior to cryopreservation affected sperm quality depending on the proportion of SP and the fertility of the bull from which it came. Bovine SP had a negative effect on bEEC in both a dose-dependent and fertility–dependent manner. Season had a slight effect on sperm morphology. Further studies are needed to investigate the factors involved in the interaction between SP and spermatozoa or bEEC, such as differences in SP composition between low and high fertility bulls, and breed and age of bull, as well as their effects on fertility. Keywords: bovine SP, fertility, proportion of SP, Uterus cell, sperm quality, Climate, frozen seme

Effect of season on bovine seminal plasma proteins in Thailand

Although season has been shown to affect bull sperm quality and fertility in some studies, the effect of season on seminal plasma proteins has not been examined. In the present study, seminal plasma proteins were analysed by Fast Protein Liquid Chromatography (FPLC), to separate the phosphorylcholine-binding proteins and heparin-binding proteins from the other proteins. Semen samples were collected from bulls in three seasons: winter, summer and the rainy season. Sperm quality was analysed by flow cytometry and computer assisted sperm analysis, and further aliquots of semen were used to prepare the seminal plasma for FPLC. Meteorological data were available from a location close to the bull station. There were slight differences in sperm kinematics between seasons, but other parameters of sperm quality were not different. Minor differences in the phosphorylcholine-binding proteins were detected according to season, being lower in summer than in winter or in the rainy season, although there were no changes in the heparin-binding proteins. Temperature, humidity and rainfall differed between winter and the rainy season, but no differences were observed between summer and the rainy season except in the temperature humidity index (THI). However, the THI was above the threshold indicative of heat stress in all seasons, which could explain why few seasonal differences in protein composition were detected in this study. Alternatively, the bulls could have been well-adapted to heat stress. In conclusion, there were only slight differences in bull sperm quality and seminal plasma proteins between seasons during this study

Environmental factors driving Xyleutes ceramica's infestation in teak plantations

<p>This research investigated factors affecting infestation of teak trees by <em>Xyleutes ceramica</em> in 3 plantations in northern Thailand at different spatial scales, including tree, plot and stand levels.</p&gt

Recorded number of <i>Xyleutes ceramica</i> 's entrance holes and environmental factors in teak plantations.xlsx

This research investigated factors affecting infestation of teak trees by Xyleutes ceramica in 3 plantations in northern Thailand at different spatial scales, including tree, plot and stand levels.</p

Innovation, R&D and Productivity: Evidence from Thai Manufacturing

This paper empirically examines the relationship between innovation, R&D (Research and Development), and productivity in Thai manufacturing using cross-sectional data from the 2007 Industrial Census of Thailand. We utilize a simplified structural model (CDM model) that describes the link between innovation output, R&D and productivity for the Thai case. Various estimation techniques are used to compare and provide evidence for empirical results. Our findings generally suggest that government aid and plant characteristics play an important role for a plant to engage in R&D and to be innovative, both in terms of process innovation and product innovation. Exporting plants, plants in the central region, and plants that are categorized as Head Branch type are more likely to engage in R&D and be innovative. The type of industry and specific technological characteristics of plants are shown to influence innovation effort and decisions to undertake R&D. On average, plant size, foreign ownership, exporting and product innovation are important drivers of productivity enhancement in Thai manufacturing

A Model-Similarity-Based Scheduling Policy for Deep Learning Training Workload in a GPU Cluster

Deep Learning (DL) has witnessed a surge in popularity in recent years, evident from its extensive utilisation and diverse applications spanning various domains. DL has played a pivotal role in tackling challenges such as image recognition, video segmentation, and natural language processing. As its name suggests, DL involves several layers to process matrix computations that are carried out layer by layer. Furthermore, the training process for a DL model demands a significant volume of data to develop proficiency in a specific task. Consequently, training DL models entail a considerable consumption of time and resources. Addressing the issue of resource-intensive demand in DL depends on factors such as the DL architecture and the size of the training dataset, which can be challenging to tackle. However, one effective strategy to tackle the time-consuming nature of DL training is the utilisation of Graphics Processing Units (GPUs). A GPU is preferred for its parallel processing capabilities, which are necessary for training DL models efficiently, especially with large datasets. Due to the ability of parallelisation, distributed training across multiple GPUs has emerged as a practical solution for completing the training process within a reasonable time. This approach is typically implemented in GPU clusters equipped with multiple GPUs. In a GPU cluster, there can be various GPU architectures available as options. Nevertheless, each GPU architecture exhibits different training performances depending on DL models. To maximise the utilisation of a GPU cluster, the scheduler plays an important role in managing resources by appropriately allocating resources to jobs. When handling DL training tasks, an effective scheduling policy ought to consider the varying training performance of each GPU architecture for different DL models. Furthermore, factors such as the number of GPUs for distributed training and batch size significantly impact training performance. Addressing the variability in training performance depending on DL models and accounting for the influential factors are critical for optimising resource usage. In this thesis, we propose a model-similarity-based scheduling policy designed specifically for managing DL training tasks in a heterogeneous GPU cluster. To take into account the variability in training performance depending on DL models, similarity measurement is utilised to compare the DL characteristics of a given job to those in the database. The training behaviour of the closest reference model is then provided to the scheduler to inform proper scheduling decisions based on cluster availability. The findings illustrate that employing the model-similarity-based scheduling policy and allowing the adjustment of batch size according to the scheduling objective can significantly decrease the makespan. Furthermore, our scheduling policy surpasses the performance of the state-of-the-art scheduling policy. To enhance the model-similarity-based scheduling policy, we incorporate cutting-edge scheduling approaches such as the round-based mechanism and job packing. The round-based mechanism enables the scheduler to periodically adjust the scheduling decisions, optimising resource allocation over time. On the other hand, job packing enhances GPU utilisation by accommodating an additional job on a GPU that trains smaller models. The results demonstrate that implementing the round-based mechanism effectively reduces the makespan compared to scenarios without it. Furthermore, integrating job packing further decreases the makespan and reduces queuing delay