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

Application of integrated AHP and TOPSIS techniques for determining the best Fresh Fruit Bunches (FFB)

This study covers the importance of high quality of palm oil Fresh Fruit Bunches (FFB) to ensure high production in palm oil industry. The most important process to classify the palm oil FFB ripeness is the grading process. Usually, the grading process performed by some graders in each mill manually. However, this method takes time and may lead to errors in the classification process, especially if the graders have less experience. Analytical Hierarchy Process (AHP) and TOPSIS are the useful tools that can be employed to make decisions in classification process. The methodology in this study consists of five phases ie; data collection from expert grader and industries visited, identifying the most important criteria, analysis by AHP method, validation by TOPSIS technique and finally the ranking of the best criteria of high quality FFB. The Expert Choice Software and Microsoft Office Excel are tools used to analyze the data collected from expert graders in the AHP and TOPSIS techniques. The main objective of this study is to determine the best quality of FFB using AHP and TOPSIS techniques. The result found that the number of detached fruitlets is the most important criteria to determine the FFB ripeness with 0.560 priority vector followed by color with 0.219 priority vector compared to other criteria. The sensitivity analysis performed to ensure the results are consistent and reliable. It will help the graders to conduct a proper grading process at mills to increase the quality of OER

Developing items using by pilot test, confirmatory factor analysis statistical in literacy for communication and supervision clinical elements provider

This research about the communication and supervision developments as an element in the future set of questionnaire. The set of questionnaire aspect need regarding medical learning of trainee doctor. The selected of two elements based on previous research, the theory and model support the importance of both. The researcher also use several of set a questionnaire from previous of research as adaptation process to create the elements and further the strength by validity testing via experts of study. A number of samples selected as respondents are 222 from 250 number of population. Referring on research objective achievement, the number of items has been changed after validity test. For the reliability test, all items after validity test selected are accepted. The confirmatory factor analysis has been shown that two items from communication elements and two items from supervision have been rejected. The final decision numbers of items in communication elements are eight, while in supervision are 12 items

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

Towards Sustainable Green Production: Exploring Automated Grading for Oil Palm Fresh Fruit Bunches (FFB) Using Machine Vision and Spectral Analysis

Over the last decade, Indonesian palm oil industry has become a leading producer of the world, and been able to generatenotable foreign export reserves. In spite of this, problems still persist in this industry, including low productivity due to mishandling of raw material in post-harvest operations. One of the prime causes of this is manual grading/sorting of fresh fruit bunches, which is prone to error and misjudgement, as well as subjectivity. High demand of oil palm establishes its high price in world market, which drives the industry to expand its plantation area to increase production. Ultimately, it compromise forests and agricultural land, resulting stagnation or decline in several food products. Alternatively, before expanding plantation extent, oil extraction productivity of existing plantation can be improved by carefully selecting appropriate FFBs for post-harvest processing through introduction of automation. The use of machine vision and spectral analysis has shown to assist productivity of agricultural processing industry. This study employs automation technology for FFB grading in oil palm mills, resulting in improved raw material quality, thereby increasing the oil extraction productivity, and simultaneously contributing to partly release the pressure of deforestation by maintaining green agricultural areas

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

Enhanced faster region-based convolutional neural network for oil palm tree detection

Oil palm trees are important economic crops in Malaysia. One of the audit procedures is to count the number of oil palm trees for plantation management, which helps the manager predict the plantation yield and the amount of fertilizer and labor force needed. However, the current counting method for oil palm tree plantation is manually counting using GIS software, which is tedious and inefficient for large scale plantation. To overcome this problem, researchers proposed automatic counting methods based on machine learning and image processing. However, traditional machine learning and image processing methods used handcrafted feature extraction methods. It can only extract low-middle level features from the image and lack of generalization ability. It’s applicable only for one application and will need reprogramming for other applications. The widely used feature extraction methods are local binary patterns (LBP), scale-invariant feature transform (SIFT), and the histogram of oriented gradients (HOG), which usually achieve low accuracy because of their limited feature representation ability and without generalization capability. Hence, this research aims to close the research gaps by exploring the deep learning-based object detection algorithm and the classical convolutional neural network (CNN) to build an automatic deep learning-based oil palm tree detection and counting framework. This study proposed a new deep learning method based on Faster RCNN for oil palm tree detection and counting. To reduce the overfitting problem during the training, this study uses the image processing method to augment the training dataset by random flipping the image and to increase the data’s contrast and brightness. The transfer learning model of ResNet50 was used as the CNN backbone and the Faster RCNN network was retrained to get the weight for automatic oil palm tree counting. To improve the performance of Faster RCNN, feature concatation method was used to integrate the high-level and low-level feature from ResNet50. The proposed model validated the testing dataset of three palm tree regions with mature, young, and mixed mature and young palm trees. The detection results were compared with two machine learning methods of ANN, SVM, image processing-based TM method, and the original Faster RCNN model respectively. The proposed enhanced Faster RCNN model shows a promising result of oil palm tree detection and counting. It achieved an overall accuracy of 97% in the testing dataset, 97.2% in the mixed palm tree region, and 96.9% in the mature and young palm tree region, while the traditional ANN, SVM, and TM methods are less than 90%. The accuracy of comparison reveals that the proposed EFRCNN model outperforms the Faster RCNN and the traditional ANN, SVM, and TM methods. It has the potential to apply in counting a large area of oil palm tree plantation

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

Determining Additional Modulus of Subgarde Reaction Based on Tolerable Settlement for the Nailed-slab System Resting on Soft Clay.

Abstract—Nailed-slab System is a proposed alternative solution for rigid pavement problem on soft soils. Equivalent modulus of subgrade reaction (k’) can be used in designing of nailed-slab system. This modular is the cumulative of modulus of subgrade reaction from plate load test (k) and additional modulus of subgrade reaction due to pile installing (∆∆∆∆k). A recent method has used reduction of pile resistance approach in determining ∆∆∆∆k. The relative displacement between pile and soils, and reduction of pile resistance has been identified. In fact, determining of reduction of pile resistance is difficult. This paper proposes an approach by considering tolerable settlement of rigid pavement. Validation is carried out with respect to a loading test of nailed-slab models. The models are presented as strip section of rigid pavement. The theory of beams on elastic foundation is used to calculate the slab deflection by using k’. Proposed approach can results in deflection prediction close to observed one. In practice, the Nailed-slab System would be constructed by multiple-row piles. Designing this system based on one-pile row analysis will give more safety design and will consume less time

Sustainability assesment of biodiesel production in Colombia

Abstract. Sustainability assessment of biodiesel production is a topic of increasing importance due to the interest of governments to stablish sovereignty strategies, diversify their energy matrix and set up the impact of biofuels production. In this context, this work proposes a system dynamic model to assess biodiesel production in a specific context, based on a general hierarchical structure of sustainability assessment that integrates dimensions of sustainable development with principles, criteria and indicators (PC and I). The assessment framework of biodiesel production was defined based on a comprehensive state of the art, resulting in a selection and analysis of 113 documents, including laws, directives and other normative documents, policy documents, certificates and papers published in peer-reviewed journals. To define the final framework, a validation strategy based on expert survey consultations and a descriptive statistical analysis was conducted. As a result, a framework composed of five dimensions (social, economic, environmental, political and technological), 13 principles and 31 criteria was proposed. Subsequently, a System Dynamics (SD) model was developed and applied for assess the sustainability biodiesel production in Colombia. Initially, the SD model was used to simulate the biodiesel production considering the current conditions in Colombia, enabling to determine the baseline (years 2008 to 2014). Subsequently, some exogenous indicators of the baseline scenario were modified in order to generate a sensibility analysis to define several fundamental conditions for the sustainable biodiesel production in the Colombian context. Once the sensibility analysis was conducted, the conditions that promote or discourage biodiesel production were determined and, consequently, optimistic and pessimistic scenarios were proposed. The results of the analysis of the scenarios can help institutions, decision-makers and other agents related to establish the conditions to be carried out to promote a sustainable biodiesel production.La evaluación de la sostenibilidad de la producción de biodiesel es un tema de creciente importancia debido al interés de los gobiernos para establecer estrategias de soberanía, diversificación de su matriz energética, de igual forma establecer el impacto de la producción de biocombustibles sobre el desarrollo sostenible. En este contexto, el presente trabajo propone un modelo dinámico del sistema para evaluar la producción de biodiesel en un contexto específico, basado en una estructura jerárquica general de evaluación de la sostenibilidad que integra las dimensiones del desarrollo sostenible, con principios, criterios e indicadores (PC and I). Se definió un marco de la evaluación de la producción de biodiésel a partir de un estado del arte, lo que resulta en una selección y análisis de 113 documentos, incluyendo leyes, directivas y otros documentos normativos, documentos de política, certificados y artículos publicados en revistas revisadas por pares. Para definir el marco final de evaluación, se estableció una estrategia de validación basada en las consultas a expertos a través de una encuesta y a las respuestas obtenidas se aplicó un análisis estadístico descriptivo. Como resultado, se propuso un marco compuesto por cinco dimensiones (sociales, económicos, ambientales, políticos y tecnológicos), 13 principios y 31 criterios. Posteriormente, fue desarrollado y aplicado un modelo de Dinámica de Sistemas (DS) para evaluar la sostenibilidad de la producción de biodiesel en Colombia. Inicialmente, se utilizó el modelo DS para simular la producción de biodiesel considerando las condiciones actuales en Colombia (entre los años 2008 a 2014), lo que permite determinar la línea base. Posteriormente, algunos indicadores exógenos de la hipótesis de base fueron modificados con el fin de generar un análisis de sensibilidad para definir una serie de condiciones fundamentales para la producción de biodiesel sostenible en el contexto colombiano. Una vez que se realizó el análisis de sensibilidad, se determinaron las condiciones que promueven o desalientan la producción de biodiesel y, en consecuencia, se propusieron dos escenarios, optimista y pesimista. Los resultados del análisis de los escenarios pueden ayudar a las instituciones, los responsables políticos y otros agentes relacionados para establecer las condiciones que deben llevarse a cabo para promover la producción de biodiesel sostenible.Doctorad