This paper presents an overview of the use of lineaments in landslide hazard mapping. The lineaments are normally derived either from aerial photographs or satellite imagery. The relative advantages and disadvantages of digital image processing and manual (visual) lineament interpretation are discussed. Most researchers prefer the manual technique, despite the fact it is more time-consuming and subjective, as it allows a higher degree of operator control. Ways of increasing objectivity in the interpretation are suggested. It is hoped that lineament mapping will increasingly be incorporated in landslide hazard assessment hence the paper emphasizes the need for care and a proper understanding of these methods and their limitations.

Mapping landslide-prone regions are crucial in natural hazard management and urban development activities in hilly and tropical regions. This research aimed to delineate a spatial prediction of landslide hazard areas along the Jelapang Corridor of the North-South Expressway in Malaysia by using two statistical models, namely, logistic regression (LR) and evidential belief function (EBF). Landslides result in high economic and social loses in Malaysia, particularly to highway concessionaries such as PLUS Expressways Berhad. LR and EBF determine the correlation between conditioning factors and landslide occurrence. EBF can also be applied in bivariate statistical analysis. Thus, EBF can be used to assess the effect of each class of conditioning factors on landslide occurrence. A landslide inventory map with 26 landslide sites was recorded using field measurements. Subsequently, the landslide inventory was randomly divided into two data sets. Approximately 70 % of the data were used for training the models, and 30 % were used for validating the results. Eight landslide conditioning factors were prepared for landslide susceptibility analysis: altitude, slope, aspect, curvature, stream power index, topographic wetness index, terrain roughness index, and distance from river. The landslide probability index was derived from both methods and subsequently classified into five susceptible classes by using the quantile method. The resultant landslide susceptibility maps were evaluated using the area under the curve technique. Results revealed the proficiency of the LR method in landslide susceptibility mapping. The achieved success and prediction rates for LR were 90 and 88 %, respectively. However, EBF was not successful in providing reasonable accurate results. The acquired success and prediction rates for EBF were 53 and 50 %, respectively. Hence, the LR technique can be utilized in landslide hazard studies for land use management and planning.

Geological structural features, such as the discontinuities that may be detected on satellite imagery as lineaments, in many cases control landslide occurrences. Lineament may represent the plane of weakness where the strength of the slope material has been reduced, eventually resulting in slope failure. The main objective of this study is to assess the relationship between lineament and landslide occurrences along the Simpang Pulai to Kg Raja highway, Malaysia. Lineament mapping was undertaken utilizing Landsat imagery and landslide distributions were identified based on field mapping and historical records. Lineament density maps of length, number and intersections were generated and compared with landslide distributions. The lineaments were also visually compared with the landslide occurrences. The results showed that there is an association between the lineaments and landslide distribution. Thus, lineament mapping is essential for the early stages of planning to prevent hazard potential from landslides.

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A colourimetric assay for the detection of DNA fragments associated with the oil palm pathogen Ganoderma boninense and other fungi DNA is reported. The assay is based on the aggregation of DNA-nanoparticle conjugates in the presence of complementary DNA from the target organism. Here, various designs of DNAnanoparticle conjugates were evaluated, and it was found that the best design gave a visually observable colour change with as little as 2 pmol of doublestranded DNA analyte even in the presence of a large excess of a mixture of noncomplementary DNA. Overall, this label-free system is rapid, sensitive, selective, simple in design, and easy to carry out. It does not require specialist equipment or specialist training for the interpretation of the results, and therefore has the potential to be deployed for agricultural diagnostics in the field.

Holistic understanding of wind behaviour over space, time and height is essential for harvesting wind energy application. This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining (MDSPM). This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded (0.125°× 0.125°) wind data for the Netherlands every 6 h and at six height levels. The wind data were first transformed into two spatio-temporal sequence databases (for speed and direction, respectively). Then, the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns, which were then visualized using a 3D wind rose, a circular histogram and a geographical map. These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines. Our analysis identified four frequent wind profile patterns. One of them highly suitable to harvest wind energy at a height of 128 m and 68.97% of the geographical area covered by this pattern already contains wind turbines. This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.ARTICLE HISTORY

Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth's surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time.ARTICLE HISTORY

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