The most appropriate organizational software is always a real challenge for managers, especially, the IT directors. The illustration of the term "enterprise software selection", is to purchase, create, or order a software that; first, is best adapted to require of the organization; and second, has suitable price and technical support. Specifying selection criteria and ranking them, is the primary prerequisite for this action. This article provides a method to evaluate, rank, and compare the available enterprise software for choosing the apt one. The prior mentioned method is constituted of three-stage processes. First, the method identifies the organizational requires and assesses them. Second, it selects the best method throughout three possibilities; indoor-production, buying software, and ordering special software for the native use. Third, the method evaluates, compares and ranks the alternative software. The third process uses different methods of multi attribute decision making (MADM), and compares the consequent results. Based on different characteristics of the problem; several methods had been tested, namely, Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Elimination and Choice Expressing Reality (ELECTURE), and easy weight method. After all, we propose the most practical method for same problems.
Opportunistic routing is an emerging routing technology that was proposed to overcome the drawback of unreliable transmission, especially in Wireless Sensor Networks (WSNs). Over the years, many forwarder methods were proposed to improve the performance in opportunistic routing. However, based on existing works, the findings have shown that there is still room for improvement in this domain, especially in the aspects of latency, network lifetime, and packet delivery ratio. In this work, a new relay node selection method was proposed. The proposed method used the minimum or maximum range and optimum energy level to select the best relay node to forward packets to improve the performance in opportunistic routing. OMNeT++ and MiXiM framework were used to simulate and evaluate the proposed method. The simulation settings were adopted based on the benchmark scheme. The evaluation results showed that our proposed method outperforms in the aspect of latency, network lifetime, and packet delivery ratio as compared to the benchmark scheme.
Porcine circovirus type 2 is the primary etiological agent associated with a group of complex multi-factorial diseases classified as Porcine Circovirus Associated Diseases (PCVAD). Sporadic cases reported in Malaysia in 2007 caused major economic losses to the 2.2 billion Malaysian ringgit (MYR) (approximately 0.7 billion US dollar) swine industry. The objective of the present study was to determine the association between the presence of PCV2 and occurrences of PCVAD.
Malaysia is the second largest palm oil producer in the world and this industry generates more than 80 million tonnes of biomass every year. When considering the potential of this biomass to be used as a fermentation feedstock, many studies have been conducted to develop a complete process for sugar production. One of the essential processes is the pre-treatment to modify the lignocellulosic components by altering the structural arrangement and/or removing lignin component to expose the internal structure of cellulose and hemicellulose for cellulases to digest it into sugars. Each of the pre-treatment processes that were developed has their own advantages and disadvantages, which are reviewed in this study.
Nitrification of mature sanitary landfill leachate with high-strength of N-NH(4) + (1080-2350 mg L(-1)) was performed in a 10 L continuous nitrification activated sludge reactor. The nitrification system was acclimatized with synthetic leachate during feed batch operation to avoid substrate inhibition before being fed with actual mature leachate. Successful nitrification was achieved with an approximately complete ammonium removal (99%) and 96% of N-NH(4) + conversion to N-NO(-) (3) . The maximum volumetric and specific nitrification rates obtained were 2.56 kg N-NH(4) (+) m(-3) day(-1) and 0.23 g N-NH(4) ( +) g(-1) volatile suspended solid (VSS) day(-1), respectively, at hydraulic retention time (HRT) of 12.7 h and solid retention time of 50 days. Incomplete nitrification was encountered when operating at a higher nitrogen loading rate of 3.14 kg N-NH(4) (+) m(-3) day(-1). The substrate overloading and nitrifiers competition with heterotrophs were believed to trigger the incomplete nitrification. Fluorescence in situ hybridization (FISH) results supported the syntrophic association between the ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria. FISH results also revealed the heterotrophs as the dominant and disintegration of some AOB cell aggregates into single cells which further supported the incomplete nitrification phenomenon.
Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.
Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.