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This study aims to understand how the assembly of rotating ring affects the axial forced vibration of gas face seals.

A three-mass kinematic model is established to investigate the axial movement of the rotating ring with bilateral constraints. The separation, collision and frictional sliding of the rotating ring in sleeve are discussed under rotor excitation. The effects of operating parameters and O-ring dynamic characteristics on the separation degree and film thickness disturbance are analyzed. A dimensionless axial characteristic force is defined to determine the critical conditions for the occurrence of separation. Several effective methods to eliminate the separation are proposed based on the adjustment of typical installation parameters.

Under rotor excitation, there may be two collisions between the rotating ring and the sleeve surfaces in one excitation period. This will cause self-excited vibration of the fluid film, increasing the risk of seal failure. The separation and collision can be prevented by increasing the equilibrium ratio, the installation radius of the O-ring on the outer surface of the rotating ring and the friction in the sleeve.

The results develop the modeling of multibody dynamics of gas face seals, enabling more accurate prediction of vibration characteristics.

The purpose of this study is to investigate and analyse the potential of existing buildings in the UK to contribute to the net-zero emissions target. Specifically, it aims to address the significant emissions from building fabrics which pose a threat to achieving these targets if not properly addressed.

The study, based on a literature review and ten (10) case studies, explored five investigative approaches for evaluating building fabric: thermal imaging, in situ U-value testing, airtightness testing, energy assessment and condensation risk analysis. Cross-case analysis was used to evaluate both case studies using each approach. These methodologies were pivotal in assessing buildings’ existing condition and energy consumption and contributing to the UK’s net-zero ambitions.

Findings reveal that incorporating the earlier approaches into the building fabric showed great benefits. Significant temperature regulation issues were identified, energy consumption decreased by 15% after improvements, poor insulation and artistry quality affected the U-values of buildings. Implementing retrofits such as solar panels, air vents, insulation, heat recovery and air-sourced heat pumps significantly improved thermal performance while reducing energy consumption. Pulse technology proved effective in measuring airtightness, even in extremely airtight houses, and high airflow and moisture management were essential in preserving historic building fabric.

The research stresses the need to understand investigative approaches’ strengths, limitations and synergies for cost-effective energy performance strategies. It emphasizes the urgency of eliminating carbon dioxide (CO₂) and greenhouse gas emissions to combat global warming and meet the 1.5° C threshold.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature

The purpose of this paper is to investigate cutting-edge coagulant materials and procedures for the removal of harmful microplastics from the water.

Traditional methods of removing microplastics from water bodies, like filtration, face limitations due to the small sizes involved. Hence, coagulation and flocculation emerge as essential strategies to enhance filtration efficacy. This paper summarizes recent research on coagulant materials, including novel hybrids, for water purification. It also looks at the most recent improvements in coagulation and flocculation processes, as well as the factors that influence their efficiency.

This paper highlights recent research on coagulant materials, including novel hybrids, used in water purification. It also examines the most recent advancements in coagulation and flocculation procedures, as well as the elements influencing their effectiveness.

The environmental threat posed by plastics, especially in their non-naturally degradable forms, such as microplastics, has reached alarming proportions. These minute particles pervade our air, soil and water bodies, driven by various factors and sources. Their diminutive size, whether in micro or nano form, renders them ingestible by marine and freshwater organisms, as well as humans, posing significant health risks. Traditional methods of water cleaning are not effective in dealing with very small-sized plastics and hence this paper summarizes recent research on coagulant materials, including various novel hybrids, for water purification from tiny microplastics in detail.

Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of concrete waste are produced globally per year. In addition, concrete also accelerates the consumption of natural resources, leading to the depletion of these natural resources. Therefore, this study uses artificial intelligence (AI) to examine the utilization of recycled concrete aggregate (RCA) in concrete.

An extensive database of 583 data points are collected from the literature for predictive modeling. Four machine learning algorithms, namely artificial neural network (ANN), random forest (RF), ridge regression (RR) and least adjacent shrinkage and selection operator (LASSO) regression (LR), in predicting simultaneously concrete compressive and tensile strength were evaluated. The dataset contains 10 independent variables and two dependent variables. Statistical parameters, including coefficient of determination (R²), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE), were employed to assess the accuracy of the algorithms. In addition, K-fold cross-validation was employed to validate the obtained results, and SHapley Additive exPlanations (SHAP) analysis was applied to identify the most sensitive parameters out of the 10 input parameters.

The results indicate that the RF prediction model performance is better and more satisfactory than other algorithms. Furthermore, the ANN algorithm ranks as the second most accurate algorithm. However, RR and LR exhibit poor findings with low accuracy. K-fold cross-validation was successfully applied to validate the obtained results and SHAP analysis indicates that cement content and recycled aggregate percentages are the effective input parameter. Therefore, special attention should be given to sensitive parameters to enhance the concrete performance.

This study uniquely applies AI to optimize the use of RCA in concrete production. By evaluating four machine learning algorithms, ANN, RF, RR and LR on a comprehensive dataset, this study identities the most effective predictive models for concrete compressive and tensile strength. The use of SHAP analysis to determine key input parameters and K-fold cross-validation for result validation adds to the study robustness. The findings highlight the superior performance of the RF model and provide actionable insights into enhancing concrete performance with RCA, contributing to sustainable construction practice.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

Climate change has led to a rise in the frequency, intensity and scope of droughts, posing significant implications for businesses. This study examines the impact of local community drought levels on audit pricing. Additionally, it explores the moderating effects of high-tech industries, auditor busyness and the level of local community concern regarding the drought crisis.

This study employs a mixed-methods approach to rigorously test the research hypotheses. The quantitative phase of the study utilizes a sample of 1,278 firm-year observations from Iran’s capital market. For the analysis of the quantitative data, ordinary least squares regression with clustered robust standard errors is used. Additionally, this research supplements its quantitative findings with qualitative evidence obtained through semi-structured interviews with 19 Iranian audit partners.

The results suggest that firms operating in provinces facing severe droughts experience notably higher audit fees. Furthermore, the positive relationship between drought and audit fees is weakened when auditors are busy, local community concern regarding the drought crisis is high or the firm operates within high-tech industries. These findings are supported by a range of robustness checks and qualitative evidence gathered from the field.

This research contributes to the growing literature on climate change by examining the influence of local community drought levels on audit pricing within an Iranian context. Additionally, our study sheds light on how high-tech industries, auditor workload and the level of local community concern regarding the drought crisis moderate the relationship between drought and audit fees. Importantly, our study pioneers in providing mixed-methods evidence of the association between drought severity and audit fees.

The purpose of the paper is to analyze the impact of involuntary frugality and deliberate frugality on the household intentions to adopt energy-efficient and energy-generating products. Additionally, the study aims to explore the role of motivation to save as a mediating factor between different types of frugality and the adoption of different kinds of energy products.

The study involved a survey of 413 households, gathering information through questionnaires from both tier I and tier II urban areas in India. The investigation used confirmatory factor analysis and structural equation modeling with Amos to explore the impact of frugality and also mediating impacts of motivation to save on the correlation between different forms of frugality (involuntary and deliberate) and the desire to acquire energy-efficient and energy-producing goods. This methodology facilitated a thorough examination of how various levels of frugality impact the uptake of sustainable energy solutions, with a specific emphasis on the fundamental motivational drivers behind these choices.

The study uncovers specific connections between various forms of frugality and the desire to embrace energy-efficient and energy-producing items. Unintentional frugality, characterized by sensitivity to prices, is shown to have a positive correlation with the adoption of energy-efficient devices but a negative association with the intention to adopt energy-generating products. Conversely, intentional frugality, distinguished by deliberate reduction actions, positively impacts the inclination to adopt both energy-efficient and energy-generating products. The results suggest that the mediating impact of motivation for savings varies depending on the type of frugality and the class of energy products being considered, emphasizing the subtle ways in which frugality influences sustainable consumption behaviors.

The contrasting effects of involuntary and voluntary frugality on the adoption of energy-efficient versus energy-generating products highlight the need to explore the underlying psychological and economic mechanisms. Future research should investigate the factors influencing the preferences of price-sensitive and deliberate frugal consumers towards this energy-efficient and energy-generating products.

Policymakers should develop specific subsidies and financial strategies for low-income households and incentive programs for conscientious consumers. Educational campaigns emphasizing the benefits of energy-generating goods and creating incentive structures with tax advantages, refunds and financial aid are essential. Companies should continue to emphasize cost savings for energy-efficient appliances and consider leasing or instalment plans for energy-generating products to appeal to price-sensitive consumers.

Literature shows that 82% of Indians prefer frugality to conserve energy through reduced consumption. However, consumer motivations for frugality vary. This study analyses the distinct impacts of involuntary and voluntary frugality on adopting energy-efficient and energy-generating products, offering a nuanced understanding of consumer behavior in sustainability—a topic underexplored in existing research. Additionally, this study investigates the role of the motivation to save as a mediator between frugality and energy product adoption, providing a novel perspective on how different frugality motivations influence different category of energy products.