Observation of temporary accommodation for construction workers according to the code of practice for temporary construction site workers amenities and accommodation (ms2593:2015) in Johor, Malaysia

The Malaysian government is currently improving the quality of workers temporary accommodation by introducing MS2593:2015 (Code of Practice for Temporary Site Workers Amenities and Accommodation) in 2015. It is in line with the initiative in the Construction Industry Transformation Programme (2016-2020) to increase the quality and well-being of construction workers in Malaysia. Thus, to gauge the current practice of temporary accommodation on complying with the particular guideline, this paper has put forth the observation of such accommodation towards elements in Section 3 within MS2593:2015. A total of seventeen (17) temporary accommodation provided by Grade 6 and Grade 7 contractors in Johor were selected and assessed. The results disclosed that most of the temporary accommodation was not complying with the guideline, where only thirteen (13) out of fifty-eight (58) elements have recorded full compliance (100%), and the lowest compliance percentage (5.9%) are discovered in the Section 3.12 (Signage). In a nutshell, given the significant gap of compliance between current practices of temporary accommodation and MS2593:2015, a holistic initiative need to be in place for the guideline to be worthwhile

Insecticidal and repellant activities of Southeast Asia plants towards insect pests: a review

Crops are being damaged by several plant pests. Several strategies have been developed to restrict the damage of cultivated plants by using synthetic pesticides and repellants. However, the use to control these insects is highly discouraged because of their risks on humans. Therefore, several alternatives have been developed from plant extracts to protect crops from plant pests. Accordingly, this review focuses on outlining the insecticidal and repellant activities of Southeast Asia plants towards insect pests. Several extracts of plants from Southeast Asia were investigated to explore their insecticidal and repellant activities. Azadiracha indica (neem) and Piper species were highly considered for their insecticidal and repellant activities compared to other plants. This review also addressed the investigation on extracts of other plant species that were reported to exert insecticidal and repellant activities. Most of the conducted studies have been still in the primarily stage of investigation, lacking a focus on the insecticidal and repellant spectrum and the identification of the active constituents which are responsible for the insecticidal and repellant activity

Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

A linear model based on Kalman filter for improving neural network classification performance

Neural network has been applied in several classification problems such as in medical diagnosis, handwriting recognition, and product inspection, with a good classification performance. The performance of a neural network is characterized by the neural network's structure, transfer function, and learning algorithm. However, a neural network classifier tends to be weak if it uses an inappropriate structure. The neural network's structure depends on the complexity of the relationship between the input and the output. There are no exact rules that can be used to determine the neural network's structure. Therefore, studies in improving neural network classification performance without changing the neural network's structure is a challenging issue. This paper proposes a method to improve neural network classification performance by constructing a linear model based on the Kalman filter as a post processing. The linear model transforms the predicted output of the neural network to a value close to the desired output by using the linear combination of the object features and the predicted output. This simple transformation will reduce the error of neural network and improve classification performance. The Kalman filter iteration is used to estimate the parameters of the linear model. Five datasets from various domains with various characteristics, such as attribute types, the number of attributes, the number of samples, and the number of classes, were used for empirical validation. The validation results show that the linear model based on the Kalman filter can improve the performance of the original neural network

Classification and severity prediction of maize leaf diseases using Deep Learning CNN approaches

No key words availableMaize (zea mays) is the staple food of Southern Africa and most of the African regions. This staple food has been threatened by a lot of diseases in terms of its yield and existence. Within this domain, it is important for researchers to develop technologies that will ensure its average yield by classifying or predicting such diseases at an early stage. The prediction, and to some degree classifying, of such diseases, with much reference to Southern Africa staple food (Maize), will result in a reduction of hunger and increased affordability among families. Reference is made to the three diseases which are Common Rust (CR), Grey Leaf Spot (GLS) and Northern Corn Leaf Blight (NCLB) (this study will mainly focus on these). With increasing drought conditions prevailing across Southern Africa and by extension across Africa, it is very vital that necessary mitigation measures are put in place to prevent additional loss of crop yield through diseases. This study introduces the development of Deep Learning (DL) Convolutional Neural Networks (CNNs) (note that in this thesis deep learning or convolution neural network or the combination of both will be used interchangeably to mean one thing) in order to classify the disease types and predict the severity of such diseases. The study focuses primarily on the CNNs, which are one of the tools that can be used for classifying images of various maize leaf diseases and in the severity prediction of Common Rust (CR) and Northern Corn Leaf Blight (NCLB). In essence the objectives of this study are: i. To create and test a CNN model that can classify various types of maize leaf diseases. ii. To set up and test a CNN model that can predict the severities of a maize leaf disease known as the maize CR. The model is to be a hybrid model because fuzzy logic rules are intended to be used with a CNN model. iii. To build and test a CNN model that can predict the severities of a maize leaf disease known as the NCLB by analysing lesion colour and sporulation patterns. This study follows a quantitative study of designing and developing CNN algorithms that will classify and predict the severities of maize leaf diseases. For instance, in Chapter 3 of this study, the CNN model for classifying various types of maize leaf diseases was set up on a Java Neuroph GUI (general user interface) framework. The CNN in this chapter achieved an average validation accuracy of 92.85% and accuracies of 87% to 99. 9% on separate class tests. In Chapter 4, the CNN model for the prediction of CR severities was based on fuzzy rules and thresholding methods. It achieved a validation accuracy of 95.63% and an accuracy 89% when tested on separate images of CR to make severity predictions among 4 classes of CR with various stages of the disease’ severities. Finally, in Chapter 5, the CNN that was set up to predict the severities of NCLB achieved 100% of validation accuracy in classification of the two NCLB severity stages. The model also passed the robustness test that was set up to test its ability of classifying the two NCLB stages as both stages were trained on images that had a cigar-shaped like lesions. The three objectives of this study are met in three separate chapters based on published journal papers. Finally, the research objectives were evaluated against the results obtained in these three separate chapters to summarize key research contributions made in this work.College of Engineering, Science and TechnologyPh. D. (Science, Engineering and Technology

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique