scholarly journals Analysis of the Influence of Socio-Economic Factors on Occupational Safety in the Construction Industry

2019 ◽  
Vol 11 (16) ◽  
pp. 4469 ◽  
Author(s):  
Bożena Hoła ◽  
Tomasz Nowobilski
Keyword(s):  
Construction Industry ◽  
Mean Square Error ◽  
Occupational Safety ◽  
Coefficient Of Determination ◽  
Economic Factors ◽  
Mean Square ◽  
Construction Site ◽  
Generic Structure ◽  
The Impact ◽  
Socio Economic Factors

The purpose of the article was to identify the socio-economic factors generated in a construction environment, which affect the number of accidents at a construction site. Moreover, the objective was to construct a mathematical model that correlates selected factors with the number of employees injured in accidents at work at a construction site, and also to estimate the influence of the identified factors on the level of occupational safety. Based on the analysis of the literature, it was stated that there are no studies describing the impact of socio-economic factors on accident rates in construction. The research included 104 factors that characterize the production value, the potential of enterprises, the generic structure of entities, and also employment and its volatility in the construction industry. In order to solve the problem, multiple regression analysis, available in the Statistica software, was applied. The developed mathematical multifactor model reflects empirical values very well, which was confirmed by the values of multiple correlation coefficients, the coefficient of determination, the adjusted coefficient of determination, as well as the mean square error and root mean square error. The construction of the model does not include qualitative factors, e.g., factors that describe the level of safety culture in the society. The developed model was used to determine the number of people injured in accidents at work. The model has certain limitations regarding its applicability. The model was developed for four selected Polish voivodeships that have a similar level of economic development and occupational safety.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals Predictive Algorithm for Handover Decisions between LTE and LTE-A Networks

2021 ◽  
Vol 9 (4) ◽  
pp. 110-126
Author(s):  
Wafa Benaatou ◽  
Adnane Latif ◽  
Vicent Pla
Keyword(s):  
Power Consumption ◽  
Mean Square Error ◽  
Fuzzy Inference ◽  
Hybrid Approach ◽  
Mobile Terminal ◽  
Base Station ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Statistical Parameters ◽  
Inference System

A heterogeneous wireless network needs to maintain seamless mobility and service continuity; for this reason, we have proposed an approach based on the combination of particle swarm optimization (PSO) and an adaptive neuro-fuzzy inference system (ANFIS) to forecast a handover during a movement of a mobile terminal from a serving base station to target base station. Additionally, the handover decision is made by considering several parameters, such as peak data rate, latency, packet loss, and power consumption, to select the best network for handover from an LTE to an LTE-A network. The performance efficiency of the new hybrid approach is determined by computing different statistical parameters, such as root mean square error (RMSE), coefficient of determination (R2), mean square error (MSE), and error standard deviation (StD). The execution of the proposed approach has been performed using MATLAB software. The simulation results show that the hybrid PSO-ANFIS model has better performance than other approaches in terms of prediction accuracy and reduction of handover latency and the power consumption in the network.  

scholarly journals Socio-economic Factors Influencing the Development of Renewable Energy Production Sector in Poland

2021 ◽  
Vol 24 (1) ◽  
pp. 38-54
Author(s):  
Tadeusz A. Grzeszczyk ◽  
Waldemar Izdebski ◽  
Michał Izdebski ◽  
Tadeusz Waściński
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Hard Coal ◽  
Economic Factors ◽  
Eu Law ◽  
Factors Influencing ◽  
Level Ii ◽  
Renewable Energy Production ◽  
The Impact ◽  
Socio Economic Factors

Poland is not one of the leaders in the use of renewable energy sources (RES), and most energy is still produced using hard coal and lignite. Therefore, there are noteworthy emissions of air pollution (including ashes and greenhouse gases), and the Polish energy sector is characterized by a substantial degree of carbonization, which, as a result, threatens to expressively increase the costs of electricity production, leading to financial penalties imposed by the EU. The aim of this paper is to analyze socio-economic factors influencing the development of the RES sector in Poland. According to this aim, expert research was carried out, in which the factors influencing development potential of RES were assessed at two levels (level II – 5 factors, level III – 15 factors) according to the factor tree analysis. Based on the analysis of the level II factors, it can be concluded that the development of the RES sector in Poland will depend to a decisive extent on factors such as: EU decisions and Polish legislation affecting the development of the RES sector in Poland, prices and availability of conventional energy carriers. Other two factors – regional policy on ecology and ecological awareness in Poland – have so far little impact on the development of this sector in the state. The analysis of the level III factors shows that the greatest impact on the development of the RES sector in Poland is the influence of European lobbying of manufacturers of machinery and equipment for renewable energy production on EU law, the impact of Polish lobbying of conventional energy producers on Polish law in the production of renewable energy and the influence of European lobbying of renewable energy producers into EU law.

scholarly journals Menentukan Nilai Penggantian Wajar Lahan Terdampak Pekerjaan Reaktifasi Jalur Kereta Api Trase Muaro Logas, Kabupaten Sijunjung, Provinsi Sumatera Barat

2019 ◽  
Vol 17 (1) ◽  
pp. 46-55
Author(s):  
Hasta Herlan Asymar
Keyword(s):  
Local Government ◽  
Vulnerable Groups ◽  
Economic Factors ◽  
The Public ◽  
Railway Line ◽  
The Poor ◽  
Reform Plan ◽  
The Social ◽  
The Impact ◽  
Socio Economic Factors

Abstract  – The calculation of the value of the Reasonable Land Turnover Terdamapak Job Reaktifasi railway line for arbitration was Muaro Logas was part of the study of the action of liberation/land and buildings for the reform plan of the reaktifasi railway line between Muaro-Logas is part of the planning of the reactivation railway line. This study analyzes regulations and policies, perceived an inventory and survey/census by identifying the affected community land procurement, with regard to the characteristics and the types of harm experienced, agreement agreement between the local government, the province and the Center in funding the acquisition of land; analyze optimum land procurement and analyse livelihood for the population affected by socio-economic factors, analyzing the parameters with the social, cultural, and economic related to population, the impact of the procurement of land and influence implementation of the work against the poor, residents of the tribal minorities, alienated, and other vulnerable groups, including women, as well as the institutional framework in planning the liberation of land and the settlements back including duties and responsibilities each institution. In the calculation of the Reasonable Replacement Value using Standar Penilaian Indonesia306 (SPI 306) about the assessment of the provision of Land for development for the benefit of the public

scholarly journals Predicting the Mechanical Properties of RCA-Based Concrete Using Supervised Machine Learning Algorithms

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 647
Author(s):  
Meijun Shang ◽  
Hejun Li ◽  
Ayaz Ahmad ◽  
Waqas Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Mean Square Error ◽  
Coarse Aggregate ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Environmental Damage ◽  
Fine Aggregate ◽  
Mean Square ◽  
The Impact

Environment-friendly concrete is gaining popularity these days because it consumes less energy and causes less damage to the environment. Rapid increases in the population and demand for construction throughout the world lead to a significant deterioration or reduction in natural resources. Meanwhile, construction waste continues to grow at a high rate as older buildings are destroyed and demolished. As a result, the use of recycled materials may contribute to improving the quality of life and preventing environmental damage. Additionally, the application of recycled coarse aggregate (RCA) in concrete is essential for minimizing environmental issues. The compressive strength (CS) and splitting tensile strength (STS) of concrete containing RCA are predicted in this article using decision tree (DT) and AdaBoost machine learning (ML) techniques. A total of 344 data points with nine input variables (water, cement, fine aggregate, natural coarse aggregate, RCA, superplasticizers, water absorption of RCA and maximum size of RCA, density of RCA) were used to run the models. The data was validated using k-fold cross-validation and the coefficient correlation coefficient (R2), mean square error (MSE), mean absolute error (MAE), and root mean square error values (RMSE). However, the model’s performance was assessed using statistical checks. Additionally, sensitivity analysis was used to determine the impact of each variable on the forecasting of mechanical properties.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

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