The construction of gas plants often experiences delays caused by various factors, which can lead to significant financial and operational losses. This research aims to develop an accurate risk model to improve the schedule performance of gas plant projects. The model uses Quantitative Risk Analysis (QRA) and Monte Carlo simulation methods to identify and measure the risks that most significantly impact project schedule performance. A comprehensive literature review was conducted to identify the risk variables that may cause delays. The risk model, pre-simulation modeling, result analysis, and expert validation were all developed using a Focused Group Discussion (FGD). Primavera Risk Analysis (PRA) software was used to perform Monte Carlo simulations. The simulation output provides information on probability distribution, histograms, descriptive statistics, sensitivity analysis, and graphical results that aid in better understanding and decision-making regarding project risks. The research results show that the simulated project completion timeline after mitigation suggested an acceleration of 61–65 days compared to the findings of the baseline simulation. This demonstrates that activity-based mitigation has a major influence on improving schedule performance. This research makes a significant contribution to addressing project delay issues by introducing an innovative and effective risk model. The model empowers project teams to proactively identify, measure, and mitigate risks, thereby improving project schedule performance and delivering more successful projects.

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.

Realistic project scheduling and control are critical for running a profitable enterprise in the construction industry. Finance-based scheduling aims to produce more realistic schedules by considering both resource and cash constraints. Since the introduction of finance-based scheduling, its literature has evolved from a single-objective model to a multi-objective model and also from a single-project problem to a multi-project problem for a contractor. This study investigates the possibility of cooperation among contractors with concurrent projects to minimize financial costs. Contractors often do not use their entire credit and may be required to pay a penalty for the unused portions. Therefore, contractors are willing to share these unused portions to decrease their financing costs and consequently improve their overall profits. This study focuses on the partnering of two contractors in a joint finance-based scheduling where contractors are allowed to lend credit to or borrow credit from each other at an internal interest rate. We apply this approach to an illustrative example in which two concurrent projects have the potential for partnering. Results show that joint finance-based scheduling reduces the financing cost for both contractors and leads to additional overall profits. Our further analyses highlight the intricate dynamics impacting additional net profit, revealing optimal scenarios for cooperation in complex project networks.

This study investigates the application of Operational Agility Management in Thai SMEs, examining its impact on Employee Dynamic Capability and the resulting Employee Value Proposition. Using a quantitative approach with a questionnaire survey targeted at Thai SME executives, the research analyzes the relationships between “Value of Work”, “Goal Orientation”, and “Network Communication” as independent variables, “Employee Dynamic Capability” as a mediating variable, and “Employee Value Proposition” as the dependent variable. The findings reveal that Thai Small and Medium-sized Enterprises (SMEs) struggle particularly with “Network Communication” in enhancing their “Employee Value Proposition”, primarily due to their predominant hierarchical command structure. This challenge highlights the need for Thai SMEs to reassess their organizational structures and communication practices to improve employee dynamic capabilities and overall employee value proposition. The study provides novel insights into the application of Operational Agility Management in Thai SMEs, bridging the gap between high-performance management theories and the practical realities faced by SMEs in unpredictable business environments, thus offering a unique perspective on cultivating employee dynamic capabilities in this setting.

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 study of the performance of high-efficiency heat pump systems has been a hot issue of general interest in the field of heat pump air conditioning. For the designed and developed two-stage casing tandem heat exchanger of heat pump system, the 3D finite volume method and the realizable k-ε model are used to numerically analyze the influence law of inlet fluid temperature and flow velocity on the overall heat transfer coefficient as well as the Nussle number of inner and outer tubes. The results show that decreasing the inlet water temperature or increasing the inlet refrigerant temperature can improve the overall heat transfer performance; Nu_in increases with the increase of water and refrigerant flow rates, while Nu_out increases with the increase of water flow rate but decreases with the increase of refrigerant flow rate; Nu_in and Nu_out both increase with the decrease of water temperature or refrigerant temperature increases.