HABITAT UTILIZATION PATTERN OF Lantana camara IN UDAWAlAWA NATIONAL PARK

Time to time, many plant species has been introduced to Sri Lanka either intentionally oraccidentally. However, with the present interest on biodiversity it was realized that some of thesespecies are posing a threat to the existence of many other native species. These species areknown as invasive species.Lantana camara an invasive plant is introduced to Sri Lanka in 1926 through the Royal BotanicGardens of Sri Lanka and currently has spread across the island to a significant extent. Now ithas become invasive and a threat to the Udawalawe National Park.Further more, growth of this species in Udawalawe NP at an alarming rate would eliminate thenatural vegetation in this area, causing loss of habitat for many animal and plant speciesendangering their survival. To effective manage and control L. camara in Udawalawa NP, it isnecessary to have knowledge of the distribution within this park and asses the invasivebehaviour of this species.Main objectives of this research study were to find the extent and distribution. This study wasconducted in purposely-selected vegetation types such as degraded open secondary forest, scrubgrasslands, medium high scrub vegetation, scrub forest transitional vegetation and teakplantation. The percentage coverage was assessed using 2mx2m quadrats. In each quadrate %cover of reproductives and non-renroductives of L. camara were measured. Seed samples weregerminated in the lab using soil collected from the natural habitat. The study had shown thatwithin the Udawalawa NP, the L. camara cover affects some vegetation types such as degradedopen secondary forest, scrub grass lands, medium high scrub vegetation, scrub forest transitionalvegetation and teak plantation. The plant is capable of producing a large number of seeds and itis spreading fast. The seeds showed high capability of direct germination. Total area ofUdawalawa NP is 30821 hectares and the area, which consists of L. camara, is 20%, spread intoabove vegetation types covering Udawalawa NP. The distribution pattern of the species showedthat the area around the main road the main road and VeheragoIla, Seenuggala, Mauara,Thimbiriyamankada and 5th milepost are the most densely and continuously distributed areas.Except this, there are few isolated patches on either side of the road and some vegetation types.It was interesting to note that the density of the L. camara is high in open areas than naturalforest areas.As for control methods, uprooting and burning was tested for plants with different cover.Uprooting of small plants was effective than mature plants. Mature plants regenerated even fromsmall pieces of rootlets. Cutting drring drought season, showed more effectiveness.This alien invasive plant, which reduce land productivity and value is a threat specially to thispark. Present investigation highlights the necessity of proper control method for L. camara inUdawalawa NP

A Novel Machine Learning based Autonomous Farming Robot for Small-Scale Chili Plantations

The agricultural sector is a major economic force in Sri Lanka, which contributes to the national economy, food security, and employment. The traditional methods practiced by farmers mainly drove the growth of the agriculture sector over the last 2500 years. However, these traditional methods have often been ineffective against pest attacks in recent years causing significant losses to farmers and threatening food security. To counter these issues, officials and researchers have started formulating novel technology-based smart solutions. This study proposes a smart, autonomous mobile robot that can help detect pests and diseases in advance and assist in crop estimation of chili plants. The model is created as such for pest and plant disease detection in small-scale chili plantations with the hope of using it in other crop types for the same purpose in the future. Thus, the proposed approach together with the developed model can be used to enhance the growth of other plants as well. Identification of the type of garden and the detection of pests and plant diseases are achieved using machine learning techniques while the identification of nutrient deficiencies is achieved using image processing techniques. This proposed mobile robot incorporates sensory inputs, machine learning, robotics, and image processing. Furthermore, a mobile application acts as the interface between the user and the robot

Site Catchment Analysis of Mahalena Cave of Rajagala, Sri Lanka: With Special Reference to the Prehistoric and Anuradhapura Phase

The systematic archaeological survey was carried out around the 0-10 km radius of Mahalena cave site. Documentation of natural resources available within the estimated radius. A few satellite settlements or supportive settlements were identified within the radius of Mahalena cave. The chief aim of this research is to understand the suitability of the landscape and natural resources available within the vicinity of this archaeological site. Mahalena cave site was subjected to large-scale excavation for several seasons by Sri Lankan and Indian archaeologists. A Few seasons of detailed excavations have provided us with sufficient data to study the resource exploitation pattern around the Mahalena cave. The study of the resource exploitation pattern or site catchment study is one of the important tools to reconstruct the economy of ancient settlers of any particular region. Resources lying within the economic range of individual archaeological sites support ancient inhabitants for their day-to-day living. The current research will be helpful in identifying suitable factors which lead the Prehistoric and Early Historic inhabitants of the Mahalena cave to choose this particular location for their settlement.  DOI: http://doi.org/10.31357/fhss/vjhss.v08i02.1

Predictive coupled-cluster isomer orderings for some Sin{}_nCm{}_m (m,n≤12m, n\le 12) clusters; A pragmatic comparison between DFT and complete basis limit coupled-cluster benchmarks

The accurate determination of the preferred ${\rm Si}_{12}{\rm C}_{12}$ isomer is important to guide experimental efforts directed towards synthesizing SiC nano-wires and related polymer structures which are anticipated to be highly efficient exciton materials for opto-electronic devices. In order to definitively identify preferred isomeric structures for silicon carbon nano-clusters, highly accurate geometries, energies and harmonic zero point energies have been computed using coupled-cluster theory with systematic extrapolation to the complete basis limit for set of silicon carbon clusters ranging in size from SiC$_3$ to ${\rm Si}_{12}{\rm C}_{12}$. It is found that post-MBPT(2) correlation energy plays a significant role in obtaining converged relative isomer energies, suggesting that predictions using low rung density functional methods will not have adequate accuracy. Utilizing the best composite coupled-cluster energy that is still computationally feasible, entailing a 3-4 SCF and CCSD extrapolation with triple-$\zeta$ (T) correlation, the {\it closo} ${\rm Si}_{12}{\rm C}_{12}$ isomer is identified to be the preferred isomer in support of previous calculations [J. Chem. Phys. 2015, 142, 034303]. Additionally we have investigated more pragmatic approaches to obtaining accurate silicon carbide isomer energies, including the use of frozen natural orbital coupled-cluster theory and several rungs of standard and double-hybrid density functional theory. Frozen natural orbitals as a way to compute post MBPT(2) correlation energy is found to be an excellent balance between efficiency and accuracy

Smell, Taste, and Temperature Interfaces

Everyday life hinges on smell, taste, and temperature-based experiences, from eating to detecting potential hazards (e.g., smell of rotten food, microbial threats, and non-microbial threats such as from hazardous gases) to responding to thermal behavioral changes. These experiences are formative as visceral, vital signals of information, and contribute directly to our safety, well-being, and enjoyment. Despite this, contemporary technology mostly stimulates vision, audition, and - more recently - touch, unfortunately leaving out the senses of smell taste and temperature. In the last decade, smell, taste, and temperature interfaces have gained a renewed attention in the field of Human Computer Interaction, fueled by the growth of virtual reality and wearable devices. As these modalities are further explored, it is imperative to discuss underlying cultural contexts of these experiences, how researchers can robustly stimulate and sense these modalities, and in what contexts such multisensory technologies are meaningful. This workshop addresses these topics and seeks to provoke critical discussions around chemo- and thermo-sensory HCI

Modelling predictors of molecular response to frontline imatinib for patients with chronic myeloid leukaemia

BACKGROUND: Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction 'rules' for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. PRINCIPLE FINDINGS: The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models.Haneen Banjar, Damith Ranasinghe, Fred Brown, David Adelson, Trent Kroger, Tamara Leclercq, Deborah White, Timothy Hughes, Naeem Chaudhr

ALTMM 2018 - 3rd international workshop on multimedia alternate realities

AltMM 2018 is the 3rd edition of the International Workshop on Multimedia Alternate Realities at ACM Multimedia. Our ambition remains to engage researchers and practitioners in discussions on how we can successfully create meaningful multimedia 'alternate realities' experiences. One of the main strengths of this workshop is that we combine different perspectives to explore how the synergy between multimedia technologies can foster and shape the creation of alternate realities and make their access an enriching and valuable experience