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Construction projects are complexity, multidisciplinary, have thousands of activities and details, and involve many participants. This intricate and fragmented nature of construction projects coupled with tight budgets and limited resources makes them good candidates for failure and promotes the emergence of risks. These risks must be addressed in the decision-making process and properly managed to mitigate their effect. However, risk management is one of the most difficult tasks and the assessment and analysis of the cost and schedule risks of construction activities are considered the most challenging tasks in the whole risk management process and require careful considerations throughout the life cycle of a project. Despite the high cost associated with managing risk in construction projects, the outcome of this task normally provides fruitful benefits. To address this crucial issue, this study employs a mixed methodology approach utilizing both qualitative and quantitative methods to gather feedback from construction experts and identify schedule and cost risk events associated with construction activities, prioritize their likelihood of occurrence, and suggest responses to mitigate them. The Program Evaluation and Review Technique (PERT) and earned value (EV) analysis are then used to estimate the expected cost at completion and the risk associated with it. A spreadsheet framework is then developed to help construction practitioners identify the most severe risks and measure their effect on the project's duration and cost. The framework also suggests risk responses for each of the risk events. The study then provides recommendations to mitigate risks with high impact and severity. [ABSTRACT FROM AUTHOR]

While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (R2: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions. [ABSTRACT FROM AUTHOR]

The estimation of construction costs for shield tunneling projects is typically based on a standard quota, which fails to consider the variation of geological parameters and often results in significant differences in unit cost. To address this issue, we propose a novel model based on a random forest machine learning procedure for analyzing the construction cost of shield tunnelling in complex geological conditions. We focus specifically on the unit consumption of grease, grouting, labor, water, and electricity. Using a dataset of geotechnical parameters and consumption quantities from a shield tunneling project, we employ KNN and correlation analysis to reduce the input dataset dimension from 17 to 6 for improved model accuracy and efficiency. Our proposed approach is applied to a shield tunneling project, with results showing that the compressive strength of geomaterial is the most influential parameter for grease, labor, water, and electricity, while it is the second most influential for grouting quantity. Based on these findings, we calculate the unit consumption and cost of the tunnelling project, which we classify into three geological categories: soil, soft rock, and hard rock. Comparing our results to the standard quota value, it is found that the unit cost of shield tunneling in soil is slightly lower (6%), while that in soft rock is very close to the standard value. However, the cost in the hard rock region is significantly greater (38%), which cannot be ignored in project budgeting. Ultimately, our results support the use of compressive strength as a classification index for shield tunneling in complex geological conditions, representing a valuable contribution to the field of tunneling cost prediction. [ABSTRACT FROM AUTHOR]

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed. [ABSTRACT FROM AUTHOR]

A healthcare resource allocation generally plays a vital role in the number of patients treated (pnt) and the patient waiting time (wt) in healthcare institutions. This study aimed to estimate pnt and wt as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δi) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δi of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for pnt; 0.9514, 0.9517, 0.9514, and 0.9514 for wt, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ0.2 (AB had a better accuracy at 0.8709 based on the value of δ0.2 for pnt) in the test stage. According to the AB algorithm based on the δ0.0, δ0.1, δ0.2, and δ0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for pnt; 0.8820, 0.8821, 0.8819, and 0.8818 for wt in the training phase, respectively. All scenarios created by the δi coefficient should be preferred for ED since the income provided by the pnt value to the hospital was more than the cost of healthcare resources. On the contrary, the wt estimation results of ML algorithms based on the δi coefficient differed. Although wt values in all ML algorithms with δ0.0 and δ0.1 coefficients reduced the cost of the hospital, wt values based on δ0.2 and δ0.3 increased the cost of the hospital. [ABSTRACT FROM AUTHOR]

A plethora of technologies has been developed over decades of extensive research on arsenic remediation, although the technical and financial perspective of arsenic removal plants in the field requires critical evaluation. In the present study, focusing on some of the pronounced arsenic-affected areas in West Bengal, India, we assessed the implementation and operation of different arsenic removal technologies using a dataset of 4000 spatio-temporal data collected from an in-depth field survey of 136 arsenic removal plants engaged in the public water supply. Our statistical analysis of this dataset indicates a 120% rise in the average cumulative capacity of the plants during 2014–2021. The majorities of the plants are based on the activated alumina with FeCl3 technology and serve about 49% of the population in the study area. The average cost of water production for the activated alumina with FeCl3 technology was found to be ₹7.56/m3 (USD $1 ≈ INR ₹70), while the lowest was ₹0.39/m3 for granular ferric hydroxide technology. A machine learning-based framework was employed to analyze the impact of water quality and treatment plant parameters on the removal efficiency, capital, and operational cost of the plants. The artificial neural network model exhibited adequate statistical significance, with a high F-value and R2 of 5830.94 and 0.72 for the capital cost model, 136,954, and 0.98 for the operational cost model, respectively. The relative importance of the process variables was identified through random forest models. The models indicated that flow rate, media, and chemicals are the predominant costs, while contaminant loading in influent water and a coagulating agent was important for removal efficiency. The established framework may be instrumental as a decision-making tool for water providers to assess the expected performance and financial involvement for proposed or ongoing arsenic removal plants concerning various design and quality parameters. [ABSTRACT FROM AUTHOR]