In an oil-film lubricating system, fit clearance and the different types of lubricating oil can result in changes in the orbit of shaft center, thereby affecting the stability of the system. Subject of this paper is the camshaft lubricating system of airspace engine, to figure out the effects of fit clearance and the type of lubricating oil on the shaft center orbit of camshaft, in this study, a 3D model of the camshaft lubricating system was built for simulation purpose based on Reynolds equation, and the calculation results suggest that, as the fit clearance grows larger, the convergence position of shaft center gradually moves away from the starting position, and the stability of shaft center declines; in terms of the type of lubricating oil, the higher the viscosity of the lubricating oil, the closer of the position of shaft center to the starting point, and the higher the stability. Our research method can be applied to common oil-film lubricating systems and we hope it could provide a theoretical evidence for the selection of fit clearance and type of lubricating oil for such systems.

Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.

In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. Therefore, rapid mitigation and reduction are required for this ever-growing problem. The marine habitats are believed to be the highest emitters and absorbers of O₂ and CO₂ respectively. As such, every day, the prominence of managing the litter in the ocean is growing effectively and efficiently. One of the most significant challenges in oceanography is creating a comprehensive meshless algorithm to handle the mathematical representation of waste plastic management in the ocean. This research dedicates to studying the dynamics of waste plastic management model governed by a mathematical representation depending on three components viz. Waste plastic (W), Marine litter (M) and Recycling of debris (R), i.e., WMR model. In this regard, an unsupervised machine learning approach, namely Mexican Hat Wavelet Neural Network (M_hWNN) refined by the efficient Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (L-BFGS), i.e., M_hWNN-LBFGS model has been implemented for handling the non-linear phenomena of WMR models. Besides, the obtained solution is meshfree and compared with the state-of-art numerical result to establish the precision of the M_hWNN-LBFGS model. Furthermore, different global statistical measures (MAPE, TIC, RMSE, and ENSE) have been computed at twenty testing points to validate the stability of the proposed algorithm.

(1) Purpose: This study aimed to investigate the relationship between Sustainability Reporting (SR) and financial performance and put forward the effect of the SR on financial performance. (2) Methodology: In order to test our hypothesis that financial performance increases the likelihood of firms reporting sustainability, a regression model was built based on the data of firms included in the Borsa Istanbul Stock Market Sustainability Index. Independent variables included in the model are Return on Assets (ROA) and Return on Equity (ROE) values, which are considered as financial performance indicators. Application of sustainable report is a dependent variable of the model. (3) Results: Company size had a positive effect on sustainability activities, while profitability had no significant effect. Large firms were usually more willing to play a role in social and environmental issues and explain their strategies on these issues. (4) Conclusions: It is important for firms to implement sustainability initiatives inside the firms from a strategic point of view, not as a result of pressure from stakeholders, such as official institutions, non-governmental organizations, suppliers or consumers. (5) Implications: With the linear regression estimation performed, the causal relationship between sustainability and financial performance has quantitatively demonstrated the positive effect of sustainability on financial performance. The main purpose of the study is to reveal the importance of publishing a sustainability report for firms and raise their awareness, thus examining the long-term effects of publishing the report. It is suggested that future research may investigate possible differences of sustainability according to the development levels of markets and countries.

Research & Development (R&D) expenditures and technological development and innovation are crucial for higher and sustainable economic growth of countries. This paper aimed to study the effects of R&D expenditures and high-tech product exports on economic growth rates. Vector autoregressive model (VAR) analysis was made using annual data between 2000-2021 in sampled BRICS countries and Turkey. It was determined that a country's economic growth rates significantly affected R&D expenditures. In addition, R&D expenditures and high-tech exports had no significant effect on economic growth rates. With economic development, the R&D expenditures increased, which was in line with expected results.

This study regards the reform of rural credit cooperatives into rural commercial banks as a “quasi-natural experiment.” Based on the panel data of 158 counties and districts from 2005 to 2019, it uses the progressive Differences-in-Differences (DID) to systematically evaluate the effect of the reform of rural credit cooperatives on county economic growth. The study finds that the reform of rural credit cooperatives has significantly promoted county economic growth, which is still valid after parallel trend tests, replacement of explained variables, and consideration of sample self-selection. Heterogeneity analysis finds that the reform of rural credit cooperatives can promote county economic growth more obviously in the samples of urban agglomeration, power-expanding counties, non-impoverished counties, and non-agricultural counties in Central Plains. The mechanism analysis finds that the reform of rural credit cooperatives can promote county economic growth through channels such as improving the level of financial development and optimizing the industrial structure. The conclusions of this study not only expand the understanding of existing references on the effect of rural credit cooperatives on county economic growth, but also provide important inspiration for the government to further deepen the reform of rural credit cooperatives and accelerate the pace of rural revitalization.

Due to simple structure, easy maintenance and low cost, induction motors (IMs) are widely applied in various industries, accounting for 60-80% alternating current (AC) motors used in industry. However, the efficiency of IMs is very low, and even small improvement can result in significant energy saving. For instance, 1% efficiency increase saves billions of kilowatt hours. Therefore, this paper aimed to improve the efficiency of IMs, thus reducing energy consumption and greenhouse gas emissions. For an IM with 7.5kW rated power and IE3 energy efficiency, the efficiency is improved by making various changes. Sequential quadratic algorithm and fmincon function are proposed to change the rotor slot and teeth structures, realizing nearly 91% motor efficiency, which is a significant improvement over the original efficiency. It is worth noting that improving the efficiency of IMs saves a lot of energy, especially in cases where IMs account for a large proportion of AC motors.

China’s logistics industry has been flourishing in recent years, with the high carbon emissions caused thereby receiving widespread attention. In this paper, the emissions and intensity of embodied carbon in the logistics industry in China are calculated for 2012, 2015 and 2017, using the multiregional input-output model and the changing trend. Additionally, influences of the intensity of embodied carbon emissions in the logistics industry across 30 provinces of China are assessed using the structural decomposition analysis method. The results show that from 2012 to 2017, the emissions of embodied carbon in the logistics industry from 30 provinces increased, while the intensity of embodied carbon emissions mainly decreased. The changes in the embodied carbon emissions intensity of the logistics industry are mainly affected by the direct carbon emission coefficient and added value coefficient. The intermediate input structure technology and the total scale of the final demand play a slight role in promoting the intensity of the embodied carbon emissions in the logistics industry. The direct carbon emission coefficient plays a major role in restraining provinces with negative intensity of embodied carbon emissions and promoting provinces with positive embodied carbon emissions intensity. The added value coefficient plays a major role in promoting the intensity of embodied carbon emissions. Finally, based on the analysis results, this paper presents suggestions for reducing the embodied carbon emissions in the logistics industry in 30 Chinese provinces, which include adjusting measures to local conditions, increasing the proportion of clean energy and clean technology in the logistics industry, increasing investment in green technology research and development, and improving the green technology innovation. Currently, researches on the implicit carbon emissions of the logistics industry focus mainly on the national, regional, and inter-provincial levels, with relatively few studies on the implicit carbon emissions of the logistics industry in each province. However, understanding the differences in the implicit carbon emissions of the logistics industry in each province and their influencing factors is crucial for identifying key emission reduction factors and developing carbon-neutral and carbon-reduction policies at the provincial level, which is the contribution that this paper makes.

According to the objective of achieving "an intelligent, sustainable and inclusive growth" in the European Union (EU) 2020 agenda, the information disclosure regarding sustainability of large companies mainly aims to contribute to the potential of the single market, thus creating sustainable development and employment levels. It is very important for companies to increase their transparency for interested parties, improve their risk management and provide better results. High transparency, including the diversity of management bodies, determines the trust growth of people in companies and markets, and allows more efficient capital distribution and the possibility of making a more realistic decision (e.g., by investors) according to available information. For current challenges, such as global warming, effects of the consumption society on environment, emphasis of disparities, worsening of environmental problems and urbanization of population, related replied have been made, representing that more efforts have been made for the transition towards a green model and for meeting the sustainable development objectives. Significant steps have been taken in this direction, but greater efforts should be made. The new economic system should rely on sustainable development, which is the solution to overcome current social, environmental and economic problems. Non-financial reporting is just a tool. In order to add value, companies should use the tool to contribute more, thus being closer to the global initiatives related to the green economy and building a sustainable society. Increase of these global trends has a significant impact on business environment, leading to measures taken immediately. Therefore, the true benefits of non-financial reporting will not be noticed unless entities change their focus from strict evaluation of financial performance to non-financial elements and integrate sustainability in their business model. Meanwhile, companies should focus on reporting the aspects, which are significant for them, the interested parties and the investors, and ensure that all these aspects are communicated to the management. Analysis of the non-financial information, presented by the active entities in the field of electrical energy production in Romania between 2017-2020, showed that only six companies met the legal requirements of non-financial information disclosure, which are state-owned companies with full or majority state capital and state-owned companies/societies.

Nowadays more and more devices and appliances are operated with electricity, thus the electrical crisis is increasing exponentially day by day. In order to avoid the occurrence of electricity crisis, various power generation resources are used at the utility side to enhance the power generation to meet the consumers’ demand for electricity. Hence, a suitable control scheme has to be implemented at the microgrid to reduce the electrical fluctuation, power loss and manage the power quality. The Adaptive Proportional Integral Voltage Controller (APIVC) and hysteresis current controller (HCC) are integrated to enhance the quality of power generated. The electrical fluctuation is reduced by the proposed efficient hybrid parallel source controller model for DC Microgrid. The proposed model exerts decentralized control, which is an advanced droop control where communication is not required. The outer voltage control loop and inner current control loop provide faster control to maintain the grid voltage constant. The grid voltage is set as the reference value and the actual value is sensed to generate error value, which sets the reference value of current. The error signal is processed to provide switching signals for the converters. The performance analysis and simulation results show that the proposed mechanism performed better than the conventional methods such as Hysteresis Band Current Controller (HBCC) with Pulse Width Modulation (PWM) and Proportional Integral Voltage Controller (PIVC) with Hysteresis Current Controller (HCC), in terms of the electrical fluctuation, power loss and manage the power quality in the microgrid.

With the rapid development of industrial technology, the application fields of AGV are constantly expanding. In this article, a differential vehicle is selected to construct a dynamic model of differential vehicle and establish a co-simulation platform of MATLAB/Simulink and ADAMS, which fully considers the nonlinear friction between wheels and the ground, the body mass and its own moment of inertia during steering, simulates the actual motion trajectory of the vehicle under different paths, and compares the ideal trajectory with the actual ADAMS output, which is generally consistent with the theory, and the basic path trend tends to be consistent. The deviation between them also reflects that the differential vehicle is a multi-degree-of-freedom strong nonlinear system, so the platform can better simulate the actual motion process of the vehicle.

The Tourism Awareness Group is essential in waterfall management because they can develop sustainable tourism. This study aims to determine the role of the Tourism Awareness Group in managing the Way Kalam Waterfall at the Way Pisang Forest Management Unit, Lampung Province, Indonesia. Data were first collected through observation and interviews and then analyzed descriptively on planning, organizing, implementation, and monitoring. The results showed that the Tourism Awareness Group in Way Kalam Village had made short-, mid-, and long-term planning. In organizing, the group has made sure members have performed their respective duties. In actuating, activities are carried out correctly according to the plans. And in controlling, the work carried out by the Tourism Awareness Group is directly supervised by the village government. There needs to be motivation from relevant stakeholders so that Tourism Awareness Group can be more active.

The purpose of this paper is to carry out a quantitative analysis of the electricity production at the level of the European Union (EU) and in Romania, in the period of 2011-2020. To address the current environmental concerns, the package “Fit for 55” proposes that by 2050, most of the energy production will have to come from renewable sources, but the question is whether this desideratum can indeed be achieved. Among the methods used in scientific research, the quantitative analysis was selected and applied in this paper, in order to carry out a detailed statistical analysis on the trend of increase or decrease in the electricity production from different energy resources, and then comparative analysis was performed, so as to draw relevant conclusions in this respect. Through this study, it can be found that, at the level of the European Union, the electricity production from renewable energy resources is increasing, while that from solid fossil fuels is decreasing. In Romania, the same trend of increase and decrease can be observed, except on a smaller scale. Accordingly, the greatest increase in electricity production was recorded from renewable energy resources, for both the EU and Romania, while the biggest decrease in electricity production from fossil fuels. In order to address the decarbonization of the energy system in Romania, the hypothesis that the decrease in total electricity production is due to the decrease in electricity production from solid fossil fuels was tested. However, this hypothesis was only partially confirmed, since the production of electricity from other energy resources, apart from renewable resources and natural gas, also experienced a similar downward trend.

This paper aimed to realize intelligent diagnosis of obstetric diseases using electronic medical records (EMRs). The Optimized Kernel Extreme Machine Learning (OKEML) technique was proposed to rebalance data. The hybrid approach of the Hunger Games Search (HGS) and the Arithmetic Optimization Algorithm (AOA) was adopted. This paper tested the effectiveness of the OKEML-HGS-AOA on Chinese Obstetric EMR (COEMR) datasets. Compared with other models, the proposed model outperformed the state-of-the-art experimental results on the COEMR, Arxiv Academic Paper Dataset (AAPD), and the Reuters Corpus Volume 1 (RCV1) datasets, with an accuracy of 88%, 90%, and 91%, respectively.

Floor plans play an essential role in the architecture design and construction, which serves as an important communication tool between engineers, architects and clients. Automatic identification of various design elements in a floor plan image can improve work efficiency and accuracy. This paper proposed a method consists of two stages, Fuzzy C-Means (FCM) segmentation and Convolutional Neural Network (CNN) segmentation. In FCM stage, the given input image was partitioned into homogeneous regions based on similarity for merging. In CNN stage, the interactive information was introduced as markers of the object area and background area, which were input by the users to roughly indicate the position and main features of the object and background. The segmentation evaluation was measured using probabilistic rand index, variation of information, global consistency error, and boundary displacement error. Experiments were conducted on real dataset to evaluate performance of the proposed model. The experimental results revealed the proposed model was successful.

In order to solve the interference caused by the overlapping and extrusion of adjacent plug seedlings, accurately obtain the information of tomato plug seedlings, and improve the transplanting effect of automatic tomato transplanters, this study proposes a seedling information acquisition method based on Cycle-Consistent Adversarial Network (CycleGAN). CycleGAN is a generative unsupervised deep learning method, which can realize the free conversion of the source-domain plug seedling image and the target-domain plug label image. It collects more than 500 images of tomato plug seedlings in different growth stages as a collection image set; follows certain principles to label the plug seedling images to obtain a label image set, and uses two image sets to train the CycleGAN network model. Finally, the trained model is used to process the images of tomato plug seedlings to obtain their label images. According to the labeling principle, the correct rate of model recognition is between 91% and 97%. The recognition results show that the CycleGAN model can recognize and judge whether the seedlings affected by the adjacent seedling holes are suitable for transplanting, so the application of this method can greatly improve the intelligence level of the automatic tomato transplanters.

Underwater image processing area has been a central point of interest to many people in many fields such as control of underwater vehicles, archaeology, marine biology research, etc. Underwater exploration is becoming a big part of our life such as underwater marine and creatures research, pipeline and communication logistics, military use, touristic and entertainment use. Underwater images are subject to poor visibility, distortion, poor quality, etc., due to several reasons such as light propagation. The real problem occurs when these images have to be taken at a depth which is more than 500 feet where artificial light needs to be introduced. This work tackles the underwater environment challenges such as as colour casts, lack of image sharpness, low contrast, low visibility, and blurry appearance in deep ocean images by proposing an end-to-end deep underwater image enhancement network (WGH-net) based on convolutional neural network (CNN) algorithm. Quantitative and qualitative metrics results proved that our method achieved competitive results with the previous work methods as it was experimentally tested on different images from several datasets.