We present the results of our tenth annual horizon scan. We identified 15 emerging priority topics that may have major positive or negative effects on the future conservation of global biodiversity, but currently have low awareness within the conservation community. We hope to increase research and policy attention on these areas, improving the capacity of the community to mitigate impacts of potentially negative issues, and maximise the benefits of issues that provide opportunities. Topics include advances in crop breeding, which may affect insects and land use; manipulations of natural water flows and weather systems on the Tibetan Plateau; release of carbon and mercury from melting polar ice and thawing permafrost; new funding schemes and regulations; and land-use changes across Indo-Malaysia.
A brief review of snoring with regard to the aetiology, patho-physiology, investigations and treatment is presented. Questions and unresolved issues are highlighted, hoping to point out directions towards future studies.
Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.
The year 2005 marked the rebirth of JUMMEC when the Editorial Board took over from its previous Editor with modest but realistic expectations. One year on, we have successfully achieved our initial expectation, that is, to encourage greater participation from our junior academics to write and publish in our very own journal, JUMMEC. As we head towards the end of 2006 and prepare to usher in 2007, we see JUMMEC consolidating and gaining in strength. Let us reflect on its past achievements and our expectations for the future.(Copied from article).
The main aim of this paper was to validate the relative price monetary model (RPMM) of exchange rate determination for the Malaysian exchange rate (RM/USD) using monthly data set from 1986-2010. The Johansen multivariate cointegration test and vector error correction model were employed. Because the time period under consideration includes the South
East Asian financial crisis, the analysis is done using two time periods; the full time period as well as the period after the crisis. Two interesting results were observed from this empirical exercise. First, there is a long-run relationship between exchange rate and the selected macro variables only for the period after the crisis. Second, the forecasting performance of monetary approach based on the error correction model outperformed the Random Walk model.
A hybrid climate model (HCM) is a novel proposed model based on the combination of self-organizing map (SOM) and analog method (AM). The main purpose was to improve the accuracy in rainfall forecasting using HCM. In combination process of HCM, SOM algorithm classifies high dimensional input data to low dimensional of several disjointed clusters in which similar input is grouped. AM searches the future day that has similar property with the day in the past. Consequently, the analog day is mapped to each cluster of SOM to investigate rainfall. In this study, the input data, geopotential height at 850 hPa from the Climate Forecast System Reanalysis (CFSR) are training set data and also the complete rainfall data at 30-meteorological stations from Thai meteorological department (TMD) are observed. To improve capability of rainfall forecasting, three different measures were evaluated. The experimental results showed that the performance of HCM is better than the traditional AM. It is illustrated that the HCM can forecast rainfall proficiently.
MAIN CONCLUSION: Crops For the Future (CFF), as an entity, has established a broad range of research activities to promote the improvement and adoption of currently underutilised crops. This paper summarises selected research activities at Crops For the Future (CFF) in pursuit of its mission 'to develop solutions for diversifying future agriculture using underutilised crops'. CFF is a research company focussed on the improvement of underutilised crops, so that they might be grown and consumed more widely with benefits to human food and nutritional security; its founding guarantors were the Government of Malaysia and the University of Nottingham. From its base in Malaysia, it engages in research around the world with a focus on species and system diversification. CFF has adopted a food system approach that adds value by delivering prototype food, feed and knowledge products. Bambara groundnut (Vigna subterranea) was adopted as an exemplar crop around which to develop CFF's food system approach with emphasis on the short-day photoperiod requirement for pod-filling and the hard-to-cook trait. Selective breeding has allowed the development of lines that are less susceptible to photoperiod but also provided a range of tools and approaches that are now being exploited in other crops such as winged bean (Psophocarpus tetragonolobus), amaranth (Amaranthus spp.), moringa (Moringa oleifera) and proso (Panicum miliaceum) and foxtail (Setaria italica) millets. CFF has developed and tested new food products and demonstrated that several crops can be used as feed for black soldier fly which can, in turn, be used to feed fish thereby reducing the need for fishmeal. Information about underutilised crops is widely dispersed; so, a major effort has been made to develop a knowledge base that can be interrogated and used to answer practical questions about potential exploitation of plant and nutritional characteristics. Future research will build on the success with Bambara groundnut and include topics such as urban agriculture, rural development and diversification, and the development of novel foods.
For most natural or naturally-derived liquid products, their color reflects on their quality and occasionally affects customer preferences. To date, there are a few subjective and objective methods for color measurement which are currently utilized by various industries. Researchers are also improving these methods and inventing new methods, as color is proven to have the ability to provide various information on the condition and quality of the liquid. However, a review on the methods, especially for amber-colored liquid, has not been conducted yet. This paper presents a comprehensive review on the subjective and objective methods for color measurement of amber-colored liquids. The pros and cons of the measurement methods, the effects of the color on customer preferences, and the international industry standards on color measurements are reviewed and discussed. In addition, this study elaborates on the issues and challenges related to the color measurement techniques as well as recommendations for future research. This review demonstrates that the existing color measurement technique can determine the color according to the standards and color scales. However, the efforts toward minimizing the complexity of the hardware while maximizing the signal processing through advanced computation are still lacking. Therefore, through this critical review, this review can hopefully intensify the efforts toward finding an optimized method or technique for color measurement of liquids and thus expedite the development of a portable device that can measure color accurately.
Dielectrophoresis (DEP) bioparticle research has progressed from micro to nano levels. It has proven to be a promising and powerful cell manipulation method with an accurate, quick, inexpensive, and label-free technique for therapeutic purposes. DEP, an electrokinetic phenomenon, induces particle movement as a result of polarization effects in a nonuniform electrical field. This review focuses on current research in the biomedical field that demonstrates a practical approach to DEP in terms of cell separation, trapping, discrimination, and enrichment under the influence of the conductive medium in correlation with bioparticle viability. The current review aims to provide readers with an in-depth knowledge of the fundamental theory and principles of the DEP technique, which is influenced by conductive medium and to identify and demonstrate the biomedical application areas. The high conductivity of physiological fluids presents obstacles and opportunities, followed by bioparticle viability in an electric field elaborated in detail. Finally, the drawbacks of DEP-based systems and the outlook for the future are addressed. This article will aid in advancing technology by bridging the gap between bioscience and engineering. We hope the insights presented in this review will improve cell suspension medium and promote DEP-viable bioparticle manipulation for health-care diagnostics and therapeutics.
The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models' performance discloses the most appropriate architecture for Malaysia's specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus's spread and minimize its effects on Malaysia's population.
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall's tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.
Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.
This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.
Previous researches show that buy (growth) companies conduct income increasing earnings management in order to meet forecasts and generate positive forecast Errors (FEs). This behavior however, is not inherent in sell (non-growth) companies. Using the aforementioned background, this research hypothesizes that since sell companies are pressured to avoid income increasing earnings management, they are capable, and in fact more inclined, to pursue income decreasing Forecast Management (FM) with the purpose of generating positive FEs. Using a sample of 6553 firm-years of companies that are listed in the NYSE between the years 2005-2010, the study determines that sell companies conduct income decreasing FM to generate positive FEs. However, the frequency of positive FEs of sell companies does not exceed that of buy companies. Using the efficiency perspective, the study suggests that even though buy and sell companies have immense motivation in avoiding negative FEs, they exploit different but efficient strategies, respectively, in order to meet forecasts. Furthermore, the findings illuminated the complexities behind informative and opportunistic forecasts that falls under the efficiency versus opportunistic theories in literature.
From the time when Roentgen and other physicists made the discoveries which led to the development of radiology, radiotherapy and nuclear medicine, medical physicists have played a pivotal role in the development of new technologies that have revolutionized the way medicine is practiced today. Medical physicists have been transforming scientific advances in the research laboratories to improving the quality of life for patients; indeed innovations such as computed tomography, positron emission tomography and linear accelerators which collectively have improved the medical outcomes for millions of people. In order for radiation-delivery techniques to improve in targeting accuracy, optimal dose distribution and clinical outcome, convergence of imaging and therapy is the key. It is timely for these two specialties to work closer again. This can be achieved by means of cross-disciplinary research, common conferences and workshops, and collaboration in education and training for all. The current emphasis is on enhancing the specific skill development and competency of a medical physicist at the expense of their future roles and opportunities. This emphasis is largely driven by financial and political pressures for optimizing limited resources in health care. This has raised serious concern on the ability of the next generation of medical physicists to respond to new technologies. In addition in the background loom changes of tsunami proportion. The clearly defined boundaries between the different disciplines in medicine are increasingly blurred and those between diagnosis, therapy and management are also following suit. The use of radioactive particles to treat tumours using catheters, high-intensity focused ultrasound, electromagnetic wave ablation and photodynamic therapy are just some areas challenging the old paradigm. The uncertainty and turf battles will only explode further and medical physicists will not be spared. How would medical physicists fit into this changing scenario? We are in the midst of molecular revolution. Are we prepared to explore the newer technologies such as nanotechnology, drug discovery, pre-clinical imaging, optical imaging and biomedical informatics? How are our curricula adapting to the changing needs? We should remember the late Professor John Cameron who advocated imagination and creativity - these important attributes will make us still relevant in 2020 and beyond. To me the future is clear: "To achieve more, we should imagine together."