Search results for: tree anchorage

Authors: Victoria L. Chester, Usha Kuruganti

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The forest industry is a major economic sector of Canada and also one of the most dangerous industries for workers. The use of mechanized mobile forestry harvesting machines has successfully reduced the incidence of injuries in forest workers related to manual labor. However, these machines have also created additional concerns, including a high machine operation learning curve, increased the length of the workday, repetitive strain injury, cognitive load, physical and mental fatigue, and increased postural loads due to sitting in a confined space. It is critical to obtain objective performance data for employers to develop appropriate work practices for this industry, however ergonomic field studies of this industry are lacking mainly due to the difficulties in obtaining comprehensive data while operators are cutting trees in the woods. The purpose of this study was to establish a measurement and experimental protocol to examine the effects of worker skill level and movement training speed (joystick crane speed) on harvesting performance using a forestry simulator. A custom wrist angle measurement device was developed as part of the study to monitor Euler angles during operation of the simulator. The device of the system consisted of two accelerometers, a Bluetooth module, three 3V coin cells, a microcontroller, a voltage regulator and an application software. Harvesting performance and crane data was provided by the simulator software and included tree to frame collisions, crane to tree collisions, boom tip distance, number of trees cut, etc. A pilot study of 3 operators with various skill levels was tested to identify factors that distinguish highly skilled operators from novice or intermediate operators. Dependent variables such as reaction time, math skill, past work experience, training movement speed (e.g. joystick control speeds), harvesting experience level, muscle activity, and wrist biomechanics were measured and analyzed. A 10-channel wireless surface EMG system was used to monitor the amplitude and mean frequency of 10 upper extremity muscles during pre and postperformance on the forestry harvest stimulator. The results of the pilot study showed inconsistent changes in median frequency pre-and postoperation, but there was the increase in the activity of the flexor carpi radialis, anterior deltoid and upper trapezius of both arms. The wrist sensor results indicated that wrist supination and pronation occurred more than flexion and extension with radial-ulnar rotation demonstrating the least movement. Overall, wrist angular motion increased as the crane speed increased from slow to fast. Further data collection is needed and will help industry partners determine those factors that separate skill levels of operators, identify optimal training speeds, and determine the length of training required to bring new operators to an efficient skill level effectively. In addition to effective and employment training programs, results of this work will be used for selective employee recruitment strategies to improve employee retention after training. Further, improved training procedures and knowledge of the physical and mental demands on workers will lead to highly trained and efficient personnel, reduced risk of injury, and optimal work protocols.

Keywords: EMG, forestry, human factors, wrist biomechanics

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Authors: Khalid Al Mutairi, Mashhor Mansor, Magdy El-Bana, Saud L. Al-Rowaily, Asyraf Mansor

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The present study was carried out in 20 islands of Farasan Archipelago in Saudi Arabia to describe the biogeography patterns of plants. A total of 191 species belonging to 129 genera and 53 families were identified. Following island biogeography theory, total plant species richness and their ecological groups were positively influenced by island size, number of habitats,elevation and were not affected by isolation. The high level of nestedness, the strong effect of area on total plant species richness and ecological groups, and the similarity of vegetation composition on the islands has several implications for conservation. In conclusion the large and richest islands in Farasan Archipelago such as Farasan Alkbir would conserve higher diversity than several smaller islands. This island also includes rare habitats like coral rocks and rare species. The invasion of the unique habitats such as wadi channels and water catchments in this island by the exotic tree Prosopis juliflora should be managed to conserve the native biodiversity. The protection of such critical habitats is very important on the other large island (e.g. Zufaf), due to their limited distribution in the country.

Keywords: island biogeography, conservation, farasan archipelago, saudi arabia, plant diversity

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Benjamen Nathaniel, Eludiora Safiriyu Ijiyemi, Egume Oneme Lucky

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A review on machine translation systems shows that a lot of computational artefacts has been carried out to translate written or spoken texts from a source language to Yorùbá language through Machine Translation systems. However, there are no work on French to Yorùbá language machine translation system; hence, the study investigated the process involved in the translation of French-to-Yorùbá language equivalent with the view to adopting a rule- based MT approach to build a Machine Translation framework from simple sentences administered through questionnaire. Articles and relevant textbooks were reviewed with key speakers of both languages interviewed to find out the processes involved in the translation of French language and their equivalent in Yorùbálanguage simple sentences using home domain terminologies. Achieving this, a model was formulated using phrase grammar structure, re-write rule, parse tree, automata theory- based techniques, designed and implemented respectively with unified modeling language (UML) and python programming language. Analysing the result, it was observed when carrying out the result that, the Machine Translation system performed 18.45% above Experimental Subject Respondent and 2.7% below Linguistics Expert when analysed with word orthography, sentence syntax and semantic correctness of the sentences. And, when compared with Google Machine Translation system, it was noticed that the developed system performed better on lexicons of the target language.

Keywords: machine translation (MT), rule-based, French language, Yoru`ba´ language

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Joe Modise, Bamidel Joseph Okoli, Nas Molefe, Imelda Ledwaba

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The present study describes the chemical profile and antioxidant potential of the stem bark of Acacia decurrens. The methanol fraction of A. decurrens stem bark gave the highest yield (20 %), while the hexane fraction had the lowest yield (0.2 %). The GC-MS spectra of the hexane, chloroform and ethyl acetate fractions confirm the presence of fifty two major compounds and the ICP-OES analysis of the stem bark was found to contain Co(0.41), Zn(1.75), Mn(3.69), Ca(8.67), Ni(10.54), Mg(12.98), Cr(24.38), K(47.88), Fe(154.62) ppm; which is an indication of hyper-accumulation capacity. The UV-Visible spectra of showed four absorption maxima for hexane fraction at 665 (0.028), 410 (0.116), 335 (0.278) and 250 (0.007) nm, three for chloroform fraction at 665 (0.028), 335 (0.278) and 250 (0.007) nm , three for ethyl acetate fraction at 665 (0.070), 390 (0.648) and 345 (0.663) nm and three for methanol fraction at 385 (0.508), 310 (0.886) and 295 (0.899) nm respectively. Quantitative phytochemical screening indicated that the alkaloid (0.6-3.3) % and saponins (5.1-8.6) % contents of the various fractions were significantly lower than the tannin (30.9-55.8) mg TAE/g, steroid(13.92-41.2) %, phenol (40.6-65.5) mgGAE/g and flavonoids (210.2 -284.9) mg RUE/g contents. The antioxidant activity of the fractions was analysed by different methods and revealed good to moderate antioxidant potential with different IC50 values viz. (42.2-49.6) mg/mL for ABTS and (37.8-75.0) μg/ml for DPPH respectively, compared to standard antioxidants. Based on obtained results, the A.decurrens stem bark fractions can be a source of safe, sustainable natural antioxidant drug and can be exploited as a source of controlled green-heavy metal cleaner.

Keywords: Acacia decurrens, antioxidant, DPPH, ABTS, hyperaccumulation, Menstruum, ICP-OES, GC-MS, UV/visible

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Authors: W. E. Igwegbe, B. C. Okoro, J. C. Osuagwu

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The study was aimed at assessing the effectiveness of reducing the concentrations of heavy metals in waste water using Pawpaw (Carica papaya) wood as a bio-sorbent. The heavy metals considered include; zinc, cadmium, lead, copper, iron, selenium, nickel, and manganese. The physiochemical properties of carica papaya stem were studied. The experimental sample was obtained from a felled trunk of matured pawpaw tree. Waste water for experimental use was prepared by dissolving soil samples collected from a dump site at Owerri, Imo state in water. The concentration of each metal remaining in solution as residual metal after bio-sorption was determined using Atomic absorption Spectrometer. The effects of ph, contact time and initial heavy metal concentration were studied in a batch reactor. The results of Spectrometer test showed that there were different functional groups detected in the carica papaya stem biomass. Optimum bio-sorption occurred at pH 5.9 with 5g/100ml solution of bio-sorbent. The results of the study showed that the treated wastewater is fit for irrigation purpose based on Canada wastewater quality guideline for the protection of Agricultural standard. This approach thus provides a cost effective and environmentally friendly option for treating waste water.

Keywords: biomass, bio-sorption, Carica papaya, heavy metal, wastewater

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Authors: Jae Young Jang, Jong Hoon Ahn, Jae-Woong Lim, So Young Kang, Mi Kyeong Lee

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Rhus verniciflua is commonly known as a lacquer tree in Korea. Stem barks of R. verniciflua have been used as an immunostimulator in traditional medicine. It contains phenolic compounds and is known for diverse biological activities such as antioxidant and antimicrobial activity. However, it also causes allergic dermatitis due to urushiols derivatives. For the development of active natural resources with less toxicity, the content of phenolic compounds and urushiols of different parts of R. verniciflua such as stem barks, lignum and leaves were quantitated by colorimetric assay and HPLC analysis. The urushiols content were the highest in stem barks, and followed by leaves. The lignum contained trace amount of urushiols. The phenolic contents, however, were the most abundant in lignum, and followed by leaves and stem barks. These results clear showed that the content of urushiols and phenolic differs depending on the parts of R. verniciflua. Antimicrobial activity of different parts of R. verniciflua against fish pathogenic bacteria was also investigated using Edwardsiella tarda. Lignum of R. verniciflua was the most effective in antimicrobial activity against E. tarda and phenolic constituents are suggested to be active constituents for activity. Taken together, phenolic compounds are responsible for antimicrobial activity of R. verniciflua. The lignum of R. verniciflua contains high content of phenolic compounds with less urushiols, which suggests efficient antimicrobial activity with less toxicity. Therefore, lignum of R. verniciflua are suggested as good sources for antimicrobial activity against fish bacterial diseases.

Keywords: different parts, phenolic compounds, Rhus verniciflua, urushiols

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Authors: Enkelejda Shkurti, Diamant Shtiza

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Background: Health-related quality of life (HRQoL) is a significant consequence in health care. The objective of our study was to recognize features related to lower HRQoL scores in children with anorectal malformation (ARM) and Hirschsprung disease (HD). Methods: Children younger than 18 years, with HD or ARM, that were assessed at our private clinic in Tirana, Albania, from December 2018 to October 2019, were acknowledged. The outcomes of broad questionnaires concerning diagnosis, symptoms, and preceding health/surgical history and authenticated tools to measure urinary status, stooling grade, and HRQoL were appraised. Results: In patients aged 0-6 years, vomiting and abdominal enlargement were allied with a substantial decrease in total HRQoL scores. In children > 6 years of age, vomiting, abdominal swelling, and abdominal discomfort were also linked to a considerably lower HRQoL. The main indicator of lower HRQoL scores on regression tree analysis in all age clusters was the occurrence of psychosomatic, behavioral, or progressive comorbidity. Conclusion: Children with both HD or ARM that have a psychosomatic, behavioral, or growing problem experience considerably lower HRQoL than patients deprived of such problems, proposing that establishment of behavioral/growing sustenance as part of the care of these patients may have a considerable influence on their HRQoL.

Keywords: anorectal malformation, Hirsch Sprung disease, quality of life, Albania

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Authors: Songul Cinaroglu

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Background: Random Forest is an efficient, multi-class machine learning method using for classification, regression and other tasks. This method is operating by constructing each tree using different bootstrap sample of the data. Determining the number of trees in random forests is an open question in the literature for studies about improving classification performance of random forests. Aim: The aim of this study is to analyze whether there is an optimal number of trees in Random Forests and how performance of Random Forests differ according to increase in number of trees using sample health data sets in R programme. Method: In this study we analyzed the performance of Random Forests as the number of trees grows and doubling the number of trees at every iteration using “random forest” package in R programme. For determining minimum and optimal number of trees we performed Mc Nemar test and Area Under ROC Curve respectively. Results: At the end of the analysis it was found that as the number of trees grows, it does not always means that the performance of the forest is better than forests which have fever trees. In other words larger number of trees only increases computational costs but not increases performance results. Conclusion: Despite general practice in using random forests is to generate large number of trees for having high performance results, this study shows that increasing number of trees doesn’t always improves performance. Future studies can compare different kinds of data sets and different performance measures to test whether Random Forest performance results change as number of trees increase or not.

Keywords: classification methods, decision trees, number of trees, random forest

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Authors: Asim Mehmood, Abdul Karim, Muhammad J. Jaskani, Faisal S. Awan, Muhammad W. Sajid

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Guava (Psidium guajava L.) is an important commercial fruit crop of Pakistan. It is an allogamous crop having 25-40% cross pollination which on the one hand leads to clonal degradation and on the other hand can add variations to generated new cultivars. Morpho-genetic characterization of 37 guava accessions was carried out for study of the genetic diversity among guava accessions located in province Punjab, Pakistan. For morphological analysis, 17 morphological traits were studied, and strong positive correlation was found among the 7 morphological traits which included thickness of outer flesh in relation to core diameter, fruit length, fruit width, fruit juiciness, fruit size, fruit sweetness and number of seeds. For genetic characterization, 18 microsatellites were used, and the sizes of reproducible and scorable bands ranged from 150 to 320 bp. These 18 primer pairs amplified a total of 85 alleles in P. guajava, with an average total number of 4.7 alleles per locus and no more than two displayed bands (nuclear SSR loci). The phylogenetic tree based on the morphological and genetic traits showed the diversity of these 37 guava genotypes into two major groups. These results indicated that Pakistani guava is quite diverse and a more detail study is needed to define the level of genetic variability.

Keywords: Psidium guajava L, genetic diversity, SSR markers, polymorphism, dendrogram

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Authors: Hamid Mustafa, Adeela Ajmal, Kim EuiSoo, Noor-ul-Ain

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World wild, buffalo production is considered as most important component of food industry. Efficient buffalo production is related with reproductive performance of this species. Lack of knowledge of reproductive efficiency and its related genes in buffalo species is a major constraint for sustainable buffalo production. In this study, we performed some bioinformatics analysis on Follicle Stimulating Hormone Receptor (FSHR) gene and explored the possible relationship of this gene among different buffalo breeds and with other farm animals. We also found the evolution pattern for this gene among these species. We investigate CDS lengths, Stop codon variation, homology search, signal peptide, isoelectic point, tertiary structure, motifs and phylogenetic tree. The results of this study indicate 4 different motif in this gene, which are Activin-recp, GS motif, STYKc Protein kinase and transmembrane. The results also indicate that this gene has very close relationship with cattle, bison, sheep and goat. Multiple alignment (MA) showed high conservation of motif which indicates constancy of this gene during evolution. The results of this study can be used and applied for better understanding of this gene for better characterization of Follicle Stimulating Hormone Receptor (FSHR) gene structure in different farm animals, which would be helpful for efficient breeding plans for animal’s production.

Keywords: buffalo, FSHR gene, bioinformatics, production

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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Traditional execution process follows the path of execution drawn by the process analyst without observing the behaviour of resource and other real-time constraints. Identifying process model, predicting the behaviour of resource and recommending the optimal path of execution for a real time process is challenging. The proposed AlfyMiner: αyM iner gives a new dimension in process execution with the novel techniques Process Model Analyser: PMAMiner and Resource behaviour Analyser: RBAMiner for recommending the probable path of execution. PMAMiner discovers next probable activity for currently executing activity in an online process using variant matching technique to identify the set of next probable activity, among which the next probable activity is discovered using decision tree model. RBAMiner identifies the resource suitable for performing the discovered next probable activity and observe the behaviour based on; load and performance using polynomial regression model, and waiting time using queueing theory. Based on the observed behaviour αyM iner recommend the probable path of execution with; next probable activity and the best suitable resource for performing it. Experiments were conducted on process logs of CoSeLoG Project1 and 72% of accuracy is obtained in identifying and recommending next probable activity and the efficiency of resource performance was optimised by 59% by decreasing their load.

Keywords: cross-organization process mining, process behaviour, path of execution, polynomial regression model

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Authors: James Taylor

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This paper considers the political, geo-philosophical musings of Deleuze and Guattari on spatialisation, place and movement in relation to the religious nomad (wandering ascetics and reclusive forest monks) inhabiting the borderlands of Thailand. A nomadic science involves improvised ascetic practices between the molar lines striated by modern state apparatuses. The wandering ascetics, inhabiting a frontier political ecology, stand in contrast to the appropriating, sedentary metaphysics and sanctifying arborescence of statism and its corollary place-making, embedded in rootedness and territorialisation. It is argued that the religious nomads, residing on the endo-exteriorities of the state, came to represent a rhizomatic and politico-ontological threat to centre-nation and its apparatus of capture. The paper also theorises transitions and movement at the borderlands in the context of the state’s monastic reforms. These reforms, and its pervasive royal science, problematised the interstitial zones of the early ascetic wanderers in their radical cross-cutting networks and lines, moving within and across demarcated frontiers. Indeed, the ascetic wanderers and their allegorical war machine were seen as a source of wild, free-floating charisma and mystical power, eventually appropriated by the centre-nation in it’s becoming unitary and fixed.

Keywords: Deleuze and Guattari, religious nomad, centre-nation, borderlands, Buddhism

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Authors: Djouhou Fowe Michelle Carole

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In the last few decades, there have been considerable efforts by the international community to develop strategies that reduce global poverty and hunger. Despite the modest success in reducing food insecurity, there are still around 795 million people worldwide who remain undernourished, the majority of whom are in sub-Saharan Africa. In many of these impoverished communities, agriculture still remains one of the most important sectors in driving economic growth and reducing poverty. For the growing population, with higher food demand and fixed agricultural land, sustainable intensification is proposed as an important strategy to respond to the challenges of low yields, environmental degradation, and adaptation to climate change. Adoption of agroforestry technologies is increasingly being promoted as a promising solution. This study was conducted to determine the perceptions of the Cameroonian population and farmers on agroforestry. The methodology used was based on a survey to determine their knowledge level of agroforestry, their representation of its advantages and disadvantages, and the reasons that might motivate them whether or not to adopt agroforestry. Participants were randomly selected and received a questionnaire. Data were subjected to a descriptive analysis using SPSS software. The obtained results showed that less than 50% of the general population had already heard about agroforestry at least once; they have basic knowledge about this concept and its advantages. Farmers had been particularly sensitive to tree's food production function and seemed to value their environmental assets. However, various constraints could affect the possible adoption of agroforestry techniques.

Keywords: agroforestry, quality and sustainable agriculture, perceptions, advantages, limits

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Authors: Regina Chua

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To reduce the costs of re-fielding a large survey, a Random Forest classifier was applied to measure the accuracy of classifying individuals into their assigned segments with the fewest possible questions. Given a long survey, one needed to determine the most predictive ten or fewer questions that would accurately assign new individuals to custom segments. Furthermore, the solution needed to be quick in its classification and usable in non-Python environments. In this paper, a supervised Random Forest classifier was modeled on a dataset with 7,000 individuals, 60 questions, and 254 features. The Random Forest consisted of an iterative collection of individual decision trees that result in a predicted segment with robust precision and recall scores compared to a single tree. A random 70-30 stratified sampling for training the algorithm was used, and accuracy trade-offs at different depths for each segment were identified. Ultimately, the Random Forest classifier performed at 87% accuracy at a depth of 10 with 20 instead of 254 features and 10 instead of 60 questions. With an acceptable accuracy in prioritizing feature selection, new tools were developed for non-Python environments: a worksheet with a formulaic version of the algorithm and an embedded function to predict the segment of an individual in real-time. Random Forest was determined to be an optimal classification model by its feature selection, performance, processing speed, and flexible application in other environments.

Keywords: machine learning, supervised learning, data science, random forest, classification, prediction, predictive modeling

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Dalia. G. Aseel

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Begomoviruses are economically important plant viruses that infect dicotyledonous plants and exclusively transmitted by the whitefly Bemisia tabaci. Here, replicative form was isolated from Okra, Cotton, Tomato plants and whitefly infected with Begomoviruses. Using coat protein specific primers (AV1), the viral infection was verified with amplicon at 450 bp. The sequence of OLCuV-AV1 gene was recorded and received an accession number (FJ441605) from Genebank. The phylogenetic tree of OLCuV was closely related to Okra leaf curl virus previously isolated from Cameroon and USA with nucleotide sequence identity of 92%. The protein purification was carried out using His-Tag methodology by using Affinity Chromatography. The purified protein was separated on SDS-PAGE analysis and an enriched expected size of band at 30 kDa was observed. Furthermore, RAPD and SDS-PAGE were used to detect genetic variability between different hosts of okra leaf curl virus (OLCuV), cotton leaf curl virus (CLCuV), tomato yellow leaf curl virus (TYLCuV) and the whitefly vector. Finally, the present study would help to understand the relationship between the whitefly and different economical crops in Egypt.

Keywords: okra leaf curl virus, AV1 gene, sequencing, phylogenetic, cloning, purified protein, genetic diversity and viral proteins

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Authors: C. C. Igwe

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Several parts of Neem tree (Azadirachta indica) are used in traditional medicine in many West African countries for the treatment of various human diseases. The leaf, stem - bark and seed were air dried for 8, 5 and 7 days, respectively. The shells were carfully separated from the seeds, each powdered sample obtained with mechanical miller and 250 mm sieve. The neem samples were individually subjected to extraction with acetone, n-hexane for 48hr and 72 hr, respectively. Physico-chemical and antibacterial evaluation were carried out using standard methods. Results of physico - chemical analyses of the extracted oil from the seed shows that it has a brownish colour, with a smell similar to garlic while the moisture content, refractive index are 0.76% and 1.47 respectively. Other vital chemical results obtained from the neem oil such as saponification value (234.62), acid value (10.84 %), free fatty acid (5.84 %) and peroxide value (10.52%) indicated the oil extracted satisfied standard oils parameters for quality soap and cosmetics production. The antibacterial screening by disc diffusion revealed the oil demonstrated high activity against Staphylococcus aureus. Both the physio-chemical and antibacterial of samples have been certified by National Agency for Food and Drugs Administration and Control. The preliminary results of this study may validate the medicinal value of the plant. Further studies are in progress to clarify the in vivo potentials of neem extracts in the management of human communicable diseases and this is a subject of investigation in our group.

Keywords: anti-bacterial, neem extract, physico-chemical analyses, staphylococcus aureus

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Authors: Thanasit Suksutdhi

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This survey research aims to study the standard of service quality of food and beverage service staffs in hotel business by studying the service standard of three sample hotels, Siam Kempinski Hotel Bangkok, Four Seasons Resort Chiang Mai, and Banyan Tree Phuket. In order to find the international service standard of food and beverage service, triangular research, i.e. quantitative, qualitative, and survey were employed. In this research, questionnaires and in-depth interview were used for getting the information on the sequences and method of services. There were three parts of modified questionnaires to measure service quality and guest’s satisfaction including service facilities, attentiveness, responsibility, reliability, and circumspection. This study used sample random sampling to derive subjects with the return rate of the questionnaires was 70% or 280. Data were analyzed by SPSS to find arithmetic mean, SD, percentage, and comparison by t-test and One-way ANOVA. The results revealed that the service quality of the three hotels were in the international level which could create high satisfaction to the international customers. Recommendations for research implementations were to maintain the area of good service quality, and to improve some dimensions of service quality such as reliability. Training in service standard, product knowledge, and new technology for employees should be provided. Furthermore, in order to develop the service quality of the industry, training collaboration between hotel organization and educational institutions in food and beverage service should be considered.

Keywords: service standard, food and beverage department, sequence of service, service method

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Authors: Siwapech Silapaprayoon, Januluk Khanobdee, Sompid Samipak

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Chili peppers are the fruits of Capsicum pepper plants well known for their fiery burning sensation on the tongue after consumption. They are members of the Solanaceae or common nightshade family along with potato, tomato and eggplant. Thai cuisine has gained popularity for its distinct flavors due to usages of various spices and its heat from the addition of chili pepper. Though being used in little quantity for each dish, chili pepper holds a special place in Thai cuisine. There are many varieties of chili peppers in Thailand, and thirty accessions were collected at Rajamangala University of Technology Lanna, Lampang, Thailand. To effectively manage any germplasm it is essential to know the diversity and relationships among members. Thirty-six Inter Simple Sequence Repeat (ISSRs) DNA markers were used to analyze the germplasm. Total of 335 polymorphic bands was obtained giving the average of 9.3 alleles per marker. Unweighted pair-group mean arithmetic method (UPGMA) clustering of data using NTSYS-pc software indicated that the accessions showed varied levels of genetic similarity ranging from 0.57-1.00 similarity coefficient index indicating significant levels of variation. At SM coefficient of 0.81, the germplasm was separated into four groups. Phenotypic variation was discussed in context of phylogenetic tree clustering.

Keywords: diversity, germplasm, Chili pepper, ISSR

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Authors: N. Khater, I. Djbablia, A. Telaoumaten, S. A. Menina, H. Benbouza

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Thureferous Juniper is an endemic cupressacée constitutes a forest cover in the mountains of Aures (Algeria ). It is an heritage and important ecological richness, but continues to decline, highly endangered species in danger of extinction, these populations show significant originality due to climatic conditions of the environment, because of its strength and extraordinary vitality, made a powerful but fragile and unique ecosystem in which natural regeneration by seed is almost absent in Algeria. Because of the quality of seeds that are either dormant or affected at the tree and the ground level by a large number of pests and parasites, which will lead to the total disappearance of this species and consequently leading to the biodiversity. View the ecological and social- economic interest presented by this case, it deserves to be preserved and produced in large quantities in this respect. The present work aims to try to regenerate the Juniperus thurefera via vegetative propagation. We studied the potential of cuttings to form adventitious roots and buds. Cuttings were taken from young subjects from 5 to 20 years treated with indole butyric acid (AIB) and planted out inside perlite under atomizer whose temperature and light are controlled. The results show that the rate of rooting is important and encourages the regeneration of this species through vegetative propagation.

Keywords: juniperus thurefera, indole butyric acid, cutting, buds, rooting

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Authors: Charuvan Kasemsap

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This research was studied in Bangnampriao District, Chachernsao Province, Thailand. The primary data relating to flooding, drought, and saline intrusion problem on agriculture were collected by surveying, focus group, and in-depth interview with agricultural officers, technical officers of irrigation department, and local government leader of Bangnampriao District. The likelihood and consequence of risk were determined the risk index by risk assessment matrix. In addition, the risk index and the total coping capacity scores were investigated the vulnerability index by vulnerability matrix. It was found that the high-risk drought and saline intrusion was dramatically along Bang Pakong River owing to the end destination of Chao Phraya Irrigation system of Central Thailand. This leads yearly the damage of rice paddy, mango tree, orchard, and fish pond. Therefore, some agriculture avoids rice growing during January to May, and also pumps fresh water from a canal into individual storage pond. However, Bangnampriao District will be strongly affected by the impacts of climate change. Monthly precipitations are expected to decrease in number; dry seasons are expected to be more in number and longer in duration. Thus, the risk and vulnerability of agriculture are also increasing. Adaptation strategies need to be put in place in order to enhance the resilience of the agriculture.

Keywords: agriculture, bangnampriao, climate change, risk assessment

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Authors: Seongcheol Kwon, Jeongmin Kim, Kwang Ryel Ryu

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We assume an IoT-based smart-home environment where the on-off status of each of the electrical appliances including the room lights can be recognized in a real time by monitoring and analyzing the smart meter data. At any moment in such an environment, we can recognize what the household or the user is doing by referring to the status data of the appliances. In this paper, we focus on a smart-home service that is to activate a robot vacuum cleaner at right time by recognizing the user situation, which requires a situation-aware model that can distinguish the situations that allow vacuum cleaning (Yes) from those that do not (No). We learn as our candidate models a few classifiers such as naïve Bayes, decision tree, and logistic regression that can map the appliance-status data into Yes and No situations. Our training and test data are obtained from simulations of user behaviors, in which a sequence of user situations such as cooking, eating, dish washing, and so on is generated with the status of the relevant appliances changed in accordance with the situation changes. During the simulation, both the situation transition and the resulting appliance status are determined stochastically. To compare the performances of the aforementioned classifiers we obtain their learning curves for different types of users through simulations. The result of our empirical study reveals that naïve Bayes achieves a slightly better classification accuracy than the other compared classifiers.

Keywords: situation-awareness, smart home, IoT, machine learning, classifier

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Authors: Ingrid Argote, John Archila, Marcelo Becker

Abstract:

In agriculture, intelligent systems applications have generated great advances in automating some of the processes in the production chain. In order to improve the efficiency of those systems is proposed a vision system to estimate the amount of fruits in sweet orange trees. This work presents a system proposal using capture of thermal images and fuzzy logic. A bibliographical review has been done to analyze the state-of-the-art of the different systems used in fruit recognition, and also the different applications of thermography in agricultural systems. The algorithm developed for this project uses the metrics of the fuzzines parameter to the contrast improvement and segmentation of the image, for the counting algorith m was used the Hough transform. In order to validate the proposed algorithm was created a bank of images of sweet orange Citrus (L.) Osbeck acquired in the Maringá Farm. The tests with the algorithm Indicated that the variation of the tree branch temperature and the fruit is not very high, Which makes the process of image segmentation using this differentiates, This Increases the amount of false positives in the fruit counting algorithm. Recognition of fruits isolated with the proposed algorithm present an overall accuracy of 90.5 % and grouped fruits. The accuracy was 81.3 %. The experiments show the need for a more suitable hardware to have a better recognition of small temperature changes in the image.

Keywords: Agricultural systems, Citrus, Fuzzy logic, Thermal images.

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

Abstract:

There has been a need in recent years to predict student academic achievement prior to graduation. This is to assist them in improving their grades, especially for those who have struggled in the past. The purpose of this research is to use supervised learning techniques to create a model that predicts student academic progress. Many scholars have developed models that predict student academic achievement based on characteristics including smoking, demography, culture, social media, parent educational background, parent finances, and family background, to mention a few. This element, as well as the model used, could have misclassified the kids in terms of their academic achievement. As a prerequisite to predicting if the student will perform well in the future on related courses, this model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester. With a 96.7 percent accuracy, the model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost. This model is offered as a desktop application with user-friendly interfaces for forecasting student academic progress for both teachers and students. As a result, both students and professors are encouraged to use this technique to predict outcomes better.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Muna A. Eissawi, Safaa Abed Elfataah, Haytham Hago, Fatima E Abukunna, Ibtisam Amin Goreish, Nahid Gornas

Abstract:

The study examined and analyzed the genetic diversity of DRB1locus of exon 2 of major histocompatibility complex of Sudanese desert sheep using PCR-RFLP and DNA sequencing. Five hundred samples belonging to five ecotypes of Desert Sudanese sheep (Abrag (Ab), Ashgar (Ash), Hamari (H), Kabashi (K) and Watish (W) were included. Amplification of exon 2 of the DRB1 gene yielded (300bp) amplified product in different ecotypes. Nine different digestion patterns corresponding to Five distinct alleles were observed with Rsa1 digestion. Genotype (ag) was the most common among all ecotypes, with a percentage comprised (40.4 %). The Hardy-Weinberg equilibrium (HWE) test showed that the studied ecotypes have significantly deviated from the theoretical proportions of Rsa1 patterns; probability values of the Chi-square test for HWE for MHC-DRB1 gene in SDS were 0.00 in all ecotypes. The constructed phylogenetic tree revealed the relation of 22 Sudanese isolates with each other and showed the shared sequences with 47 published foreign sequences randomly selected from different geographic regions. The results of this study highlight the effect of heterozygosity of MHC genes of the Desert sheep of Sudan which may clarify some of genetic back ground of their disease resistance and adaptation to environment.

Keywords: desert sheep, MHC, Ovar-DRB1, polymerase chain reaction (PCR), restriction fragment length polymorphism (RFLP)

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

Abstract:

Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Maria Toader, Gheorghe Valentin Roman

Abstract:

The importance of fruit trees and bushes growing for Romania is due the concordance that exists between the different ecological conditions in natural basins, and the requirements of different species and varieties. There are, in Romania, natural areas dedicated to the main trees species: plum, apple, pear, cherry, sour cherry, finding optimal conditions for harnessing the potential of fruitfulness, making fruit quality both in terms of ratio commercial, and content in active principles. The share of fruits crops in the world economy of agricultural production is due primarily to the role of fruits in nourishment for human, and in the prevention and combating of diseases, in increasing the national income of cultivator countries and to improve comfort for human life. For Romania, the perspectives of the sector are positive, and are due to European funding opportunities, which provide farmers a specialized program that meets the needs of development and modernization of fruit growing industry, cultivation technology and equipment, organization and grouping of producers, creating storage facilities, conditioning, marketing and the joint use of fresh fruit. This paper shows the evolution of fruit growing, in Romania compared to other states. The document presents the current situation of the main tree species both in terms of surface but also of the productions and the role that this activity may have for the development of rural communities.

Keywords: fruit growing, fruits trees, productivity, rural development

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Authors: Frantisek Kopriva, Kamila Michalkova, Radim Dudek, Jana Volejnikova

Abstract:

Rationale: Diagnostic criteria of asthma–COPD overlap syndrome (ACOS) are controversial in pediatrics. Emphysema is characteristic of COPD and usually does not occur in typical asthma; its presence in patients with asthma suggests the concurrence with COPD. Low-dose chest computed tomography (CT) allows a non-invasive assessment of the lung tissue structure. Here we present CT findings of emphysematous changes in a child with ACOS. Patient and Methods: In a 6-year-old boy, atopy was confirmed by a skin prick test using common allergen extracts (grass and tree pollen, house dust mite, molds, cat, dog; manufacturer Stallergenes Greer, London, UK), where reactions over 3 mm were considered positive. Treatment with corticosteroids was started during the course of severe asthma. At 12 years of age, his spirometric parameters deteriorated despite treatment adjustment (VC 1.76 L=85%, FEV1 1.13 L=67%, TI%VCmax 64%, MEF25 19%, TLC 144%) and the bronchodilator test became negative. Results: Low-dose chest CT displayed irregular regions with increased radiolucency of pulmonary parenchyma (typical for hyperinflation in emphysematous changes) in both lungs. This was in accordance with the results of spirometric examination. Conclusions: ACOS is infrequent in children. However, low-dose chest CT scan can be considered to confirm this diagnosis or eliminate other diagnoses when the clinical condition is deteriorating and treatment response is poor.

Keywords: child, asthma, low-dose chest CT, ACOS

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