Search results for: forest residue

Authors: Zainudin Kornain, Azman Jalar, Rozaidi Rasid, Fong Chee Seng

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Void formation in underfill is considered as failure in flip chip manufacturing process. Void formation possibly caused by several factors such as poor soldering and flux residue during die attach process, void entrapment due moisture contamination, dispense pattern process and setting up the curing process. This paper presents the comparison of single step and two steps curing profile towards the void and black dots formation in underfill for Hi-CTE Flip Chip Ceramic Ball Grid Array Package (FC-CBGA). Statistic analysis was conducted to analyze how different factors such as wafer lot, sawing technique, underfill fillet height and curing profile recipe were affected the formation of voids and black dots. A C-Mode Scanning Aqoustic Microscopy (C-SAM) was used to scan the total count of voids and black dots. It was shown that the 2 steps curing profile provided solution for void elimination and black dots in underfill after curing process.

Keywords: black dots formation, curing profile, FC-CBGA, underfill, void formation,

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Authors: R. K. Saxena, Ravi Saxena

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In view of their importance and usefulness in reliability theory and probability distributions, several generalizations of the inverse Gaussian distribution and the Krtzel function are investigated in recent years. This has motivated the authors to introduce and study a new generalization of the inverse Gaussian distribution and the Krtzel function associated with a product of a Bessel function of the third kind )(zKQ and a Z - Fox-Wright generalized hyper geometric function introduced in this paper. The introduced function turns out to be a unified gamma-type function. Its incomplete forms are also discussed. Several properties of this gamma-type function are obtained. By means of this generalized function, we introduce a generalization of inverse Gaussian distribution, which is useful in reliability analysis, diffusion processes, and radio techniques etc. The inverse Gaussian distribution thus introduced also provides a generalization of the Krtzel function. Some basic statistical functions associated with this probability density function, such as moments, the Mellin transform, the moment generating function, the hazard rate function, and the mean residue life function are also obtained.KeywordsFox-Wright function, Inverse Gaussian distribution, Krtzel function & Bessel function of the third kind.

Keywords: Fox-Wright function, Inverse Gaussian distribution, Krtzel function & Bessel function of the third kind.

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Authors: Melis Inalpulat, Levent Genc

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Land Use Land Cover (LULC) changes due to human activities and natural causes have become a major environmental concern. Assessment of temporal remote sensing data provides information about LULC impacts on environment. Land Surface Temperature (LST) is one of the important components for modeling environmental changes in climatological, hydrological, and agricultural studies. In this study, LULC changes (September 7, 1984 and July 8, 2014) especially in agricultural lands together with population changes (1985-2014) and LST status were investigated using remotely sensed and census data in South Marmara Watershed, Turkey. LULC changes were determined using Landsat TM and Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM and OLI images were classified using supervised classification method to prepare LULC map including five classes including Forest (F), Grazing Land (G), Agricultural Land (A), Water Surface (W), Residential Area-Bare Soil (R-B) classes. The LST image was also derived from thermal bands of the same dates. LULC classification results showed that forest areas, agricultural lands, water surfaces and residential area-bare soils were increased as 65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In comparison, a dramatic decrement occurred in grazing land (107985 ha) within three decades. The population increased 29% between years 1984-2014 in whole study area. Along with the natural causes, migration also caused this increase since the study area has an important employment potential. LULC was transformed among the classes due to the expansion in residential, commercial and industrial areas as well as political decisions. In the study, results showed that agricultural lands around the settlement areas transformed to residential areas in 30 years. The LST images showed that mean temperatures were ranged between 26-32°C in 1984 and 27-33°C in 2014. Minimum temperature of agricultural lands was increased 3°C and reached to 23°C. In contrast, maximum temperature of A class decreased to 41°C from 44°C. Considering temperatures of the 2014 R-B class and 1984 status of same areas, it was seen that mean, min and max temperatures increased by 2°C. As a result, the dynamism of population, LULC and LST resulted in increasing mean and maximum surface temperatures, living spaces/industrial areas and agricultural lands.

Keywords: Census data, landsat, land surface temperature (LST), land use land cover (LULC).

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Authors: Mehran Bakhshi, Mohammad Raouf Hosseini, Mohammadhosein Rahimi

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The cadmium sulfide nanoparticles were synthesized from the nickel-cadmium cake of a hydrometallurgical zinc producing plant and sodium sulfide as Cd²⁺ and S^-2 sources, respectively. Also, the synthesis process was performed by using the secretions of Bacillus licheniformis as bio-surfactant. Initially, in order to obtain a cadmium rich solution, two following steps were carried out: 1) Alkaline leaching for the removal of zinc oxide from the cake, and 2) acidic leaching to dissolve cadmium from the remained solid residue. Afterward, the obtained CdSO₄ solution was used for the nanoparticle biosynthesis. Nanoparticles were characterized by the energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) to confirm the formation of CdS crystals with cubic structure. Also, transmission electron microscopy (TEM) was applied to determine the particle sizes which were in 2-10 nm range. Moreover, the presence of the protein containing bio-surfactants was approved by using infrared analysis (FTIR). In addition, the absorbance below 400 nm confirms quantum particles’ size. Finally, it was shown that valuable CdS quantum dots could be obtained from the industrial waste products via environment-friendly biological approaches.

Keywords: Biosynthesis, cadmium cake, cadmium sulfide, nanoparticle, zinc smelter.

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Authors: Ishtiaq Ahmed, Muhammad Anjum Zia, Hafiz Muhammad Nasir Iqbal

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The purpose of the present work was to study the production and process parameters optimization for the synthesis of cellulase from Trichoderma viride in solid state fermentation (SSF) using an agricultural wheat straw as substrates; as fungal conversion of lignocellulosic biomass for cellulase production is one among the major increasing demand for various biotechnological applications. An optimization of process parameters is a necessary step to get higher yield of product. Several kinetic parameters like pretreatment, extraction solvent, substrate concentration, initial moisture content, pH, incubation temperature and inoculum size were optimized for enhanced production of third most demanded industrially important cellulase. The maximum cellulase enzyme activity 398.10±2.43 μM/mL/min was achieved when proximally analyzed lignocellulosic substrate wheat straw inocubated at 2% HCl as pretreatment tool along with distilled water as extraction solvent, 3% substrate concentration 40% moisture content with optimum pH 5.5 at 45°C incubation temperature and 10% inoculum size.

Keywords: Cellulase, Lignocellulosic residue, Processoptimization, Proximal analysis, SSF, Trichoderma viride.

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Authors: P. Weerachanchai, C. Tangsathitkulchai, M. Tangsathitkulchai

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Attempt was made to improve certain characteristics of bio-oil derived from palm kernel pyrolysis by blending it with diesel fuel and alcohols. Two types of alcohol, ethanol or butanol, was used as cosolvent to stabilize the phase of ternary systems. Phase behaviors and basic fuel properties of palm kernel bio-oildiesel- alcohol systems were investigated in this study. Alcohol types showed a significant influence on the phase characteristics with palm kernel bio-oil-diesel-butanol system giving larger soluble area than that of palm kernel bio-oil-diesel-ethanol system. For fuel properties, blended fuels showed superior properties including lower values of density (~860 kg/m3 at 25°C), viscosity (~4.12 mm2/s at 40°C), carbon residue (1.02-2.53 wt%), ash (0.018-0.034 wt%) and pour point (<-25 to -7 °C), increased pH (~ 6.4) and giving reasonable heating values of 32.5-41.2 MJ/kg. To enable the prediction of some properties of fuel mixtures, the measured fuel properties including heating value, density, ash content and pH were fitted by Kay-s mixing rule, whereas the viscosities of blended fuels at different temperatures were correlated by the modified Grunberg-Nissan equation and Andrade equation.

Keywords: Bio-oil, fuel blend, fuel properties, phase behaviour.

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Authors: Liliana Feleagă, Niculae Feleagă, Vasile Răileanu

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Although agriculture is an important part of the world economy, accounting in agriculture still has many shortcomings. The adoption of IAS 41 “Agriculture” has tried to improve this situation and increase the comparability of financial statements of entities in the agricultural sector. Although controversial, IAS 41 is the first step of a consistent transition to fair value assessment in the agricultural sector. The objective of our work is the analysis of IAS 41 and current accounting agricultural situation in Romania. Accounting regulations in Romania are in accordance with European directives and, in many respects, converged with IFRS referential. Provisions of IAS 41, however, are not reflected directly in Romanian regulations. With the increase of forest land transactions, it is expected that recognition and measurement of biological assets under IAS 41 to become a necessity.

Keywords: Accounting Agricultural, Biological Assets, Fair value, IAS 41

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Authors: P. Ninduangdee, V. I. Kuprianov

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Palm kernel shell is an important bioenergy resource in Thailand. However, due to elevated alkali content in biomass ash, this oil palm residue shows high tendency to bed agglomeration in a fluidized-bed combustion system using conventional bed material (silica sand). In this study, palm kernel shell was burned in the conical fluidized-bed combustor (FBC) using alumina and dolomite as alternative bed materials to prevent bed agglomeration. For each bed material, the combustion tests were performed at 45kg/h fuel feed rate with excess air within 20–80%. Experimental results revealed rather weak effects of the bed material type but substantial influence of excess air on the behavior of temperature, O2, CO, CxHy, and NO inside the reactor, as well as on the combustion efficiency and major gaseous emissions of the conical FBC. The optimal level of excess air ensuring high combustion efficiency (about 98.5%) and acceptable level of the emissions was found to be about 40% when using alumina and 60% with dolomite. By using these alternative bed materials, bed agglomeration can be prevented when burning the shell in the proposed conical FBC. However, both bed materials exhibited significant changes in their morphological, physical and chemical properties in the course of the time.

Keywords: Palm kernel shell, fluidized-bed combustion, alternative bed materials, combustion and emission performance, bed agglomeration prevention.

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Authors: Alexander J. Severinsky

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Radiative forces of greenhouse gases (GHG) increase the temperature of the Earth's surface, more on land, and less in oceans, due to their thermal capacities. Given this inertia, the temperature increase is delayed over time. Air temperature, however, is not delayed as air thermal capacity is much lower. In this study, through analysis and synthesis of multidisciplinary science and data, an estimate of atmospheric temperature increase is made. Then, this estimate is used to shed light on current observations of ice and snow loss, desertification and forest fires, and increased extreme air disturbances. The reason for this inquiry is due to the author’s skepticism that current changes cannot be explained by a "~1 ^oC" global average surface temperature rise within the last 50-60 years. The only other plausible cause to explore for understanding is that of atmospheric temperature rise. The study utilizes an analysis of air temperature rise from three different scientific disciplines: thermodynamics, climate science experiments, and climactic historical studies. The results coming from these diverse disciplines are nearly the same, within ± 1.6%. The direct radiative force of GHGs with a high level of scientific understanding is near 4.7 W/m² on average over the Earth’s entire surface in 2018, as compared to one in pre-Industrial time in the mid-1700s. The additional radiative force of fast feedbacks coming from various forms of water gives approximately an additional ~15 W/m². In 2018, these radiative forces heated the atmosphere by approximately 5.1 ^oC, which will create a thermal equilibrium average ground surface temperature increase of 4.6 ^oC to 4.8 ^oC by the end of this century. After 2018, the temperature will continue to rise without any additional increases in the concentration of the GHGs, primarily of carbon dioxide and methane. These findings of the radiative force of GHGs in 2018 were applied to estimates of effects on major Earth ecosystems. This additional force of nearly 20 W/m² causes an increase in ice melting by an additional rate of over 90 cm/year, green leaves temperature increase by nearly 5 ^oC, and a work energy increase of air by approximately 40 Joules/mole. This explains the observed high rates of ice melting at all altitudes and latitudes, the spread of deserts and increases in forest fires, as well as increased energy of tornadoes, typhoons, hurricanes, and extreme weather, much more plausibly than the 1.5 ^oC increase in average global surface temperature in the same time interval. Planned mitigation and adaptation measures might prove to be much more effective when directed toward the reduction of existing GHGs in the atmosphere.

Keywords: GHG radiative forces, GHG air temperature, GHG thermodynamics, GHG historical, GHG experimental, GHG radiative force on ice, GHG radiative force on plants, GHG radiative force in air.

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Authors: Ewa. M. Sztendur, Neil T. Diamond

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Random Forests are a powerful classification technique, consisting of a collection of decision trees. One useful feature of Random Forests is the ability to determine the importance of each variable in predicting the outcome. This is done by permuting each variable and computing the change in prediction accuracy before and after the permutation. This variable importance calculation is similar to a one-factor-at a time experiment and therefore is inefficient. In this paper, we use a regular fractional factorial design to determine which variables to permute. Based on the results of the trials in the experiment, we calculate the individual importance of the variables, with improved precision over the standard method. The method is illustrated with a study of student attrition at Monash University.

Keywords: Random Forests, Variable Importance, Fractional Factorial Designs, Student Attrition.

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Authors: P. Řehoř, D. Holátová, V. Doležalová

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Paper deals with analysis of strategic management methods in non-profit making organization in the Czech Republic. Strategic management represents an aggregate of methods and approaches that can be applied for managing organizations - in this article the organizations which associate owners and keepers of nonstate forest properties. Authors use these methods of strategic management: analysis of stakeholders, SWOT analysis and questionnaire inquiries. The questionnaire was distributed electronically via e-mail. In October 2013 we obtained data from a total of 84 questionnaires. Based on the results the authors recommend the using of confrontation strategy which improves the competitiveness of non-profit making organizations.

Keywords: Strategic management, non-profit making organization, strategy analysis, SWOT analysis, strategy, competitiveness.

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Authors: Praveen Boinee, Alessandro De Angelis, Gian Luca Foresti

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Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experiment to apply these Meta learning techniques to the random forests. We experiment the working of the ensembles of random forests on the standard data sets available in UCI data sets. We compare the original random forest algorithm with their ensemble counterparts and discuss the results.

Keywords: Random Forests [RF], ensembles, UCI.

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Authors: Norma Rodrigues Gomes

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This paper presents initiatives of Knowledge Management (KM) applied to Forensic Sciences field, especially developed at the Forensic Science Institute of the Brazilian Federal Police. Successful projects, related to knowledge sharing, drugs analysis and environmental crimes, are reported in the KM perspective. The described results are related to: a) the importance of having an information repository, like a digital library, in such a multidisciplinary organization; b) the fight against drug dealing and environmental crimes, enabling the possibility to map the evolution of crimes, drug trafficking flows, and the advance of deforestation in Amazon rain forest. Perspectives of new KM projects under development and studies are also presented, tracing an evolution line of the KM view at the Forensic Science Institute.

Keywords: Business Intelligence, Digital Library, Forensic Science, Knowledge Management

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Authors: Diana Fajardo, Sebastián Ortiz, Oscar Herrera, Angélica Santis

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Currently, in Colombia is arising a problem related to collecting used lubricant oils which are generated by the increment of the vehicle fleet. This situation does not allow a proper disposal of this type of waste, which in turn results in a negative impact on the environment. Therefore, through the comparative analysis of various heuristics, the best solution to the VRP (Vehicle Routing Problem) was selected by comparing costs and times for the collection of used lubricant oils in the city of Pereira, Colombia; since there is no presence of management companies engaged in the direct administration of the collection of this pollutant. To achieve this aim, six proposals of through methods of solution of two phases were discussed. First, the assignment of the group of generator points of the residue was made (previously identified). Proposals one and four of through methods are based on the closeness of points. The proposals two and five are using the scanning method and the proposals three and six are considering the restriction of the capacity of collection vehicle. Subsequently, the routes were developed - in the first three proposals by the Clarke and Wright's savings algorithm and in the following proposals by the Traveling Salesman optimization mathematical model. After applying techniques, a comparative analysis of the results was performed and it was determined which of the proposals presented the most optimal values in terms of the distance, cost and travel time.

Keywords: Heuristics, optimization model, savings algorithm used vehicular oil, VRP.

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Authors: Chun-Wei Tung, Chyn Liaw, Shinn-Jang Ho, Shinn-Ying Ho

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Protein subchloroplast locations are correlated with its functions. In contrast to the large amount of available protein sequences, the information of their locations and functions is less known. The experiment works for identification of protein locations and functions are costly and time consuming. The accurate prediction of protein subchloroplast locations can accelerate the study of functions of proteins in chloroplast. This study proposes a Random Forest based method, ChloroRF, to predict protein subchloroplast locations using interpretable physicochemical properties. In addition to high prediction accuracy, the ChloroRF is able to select important physicochemical properties. The important physicochemical properties are also analyzed to provide insights into the underlying mechanism.

Keywords: Chloroplast, Physicochemical properties, Proteinlocations, Random Forests.

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Authors: Waranya Pongpaiboon, Warangkana Srichamnong, Supat Chaiyakul

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Enzymatic modification of rice flour can produce highly functional derivatives use in food industries. This study aimed to evaluate the physical properties and resistant starch content of rice flour residues hydrolyzed by α-amylase. Rice flour hydrolyzed by α-amylase (60 and 300 u/g) for 1, 24 and 48 hours were investigated. Increasing enzyme concentration and hydrolysis time resulted in decreased rice flour residue’s lightness (L*) but increased redness (a*) and yellowness (b*) of rice flour residues. The resistant starch content and peak viscosity increased when hydrolysis time increased. Pasting temperature, trough viscosity, breakdown, final viscosity, setback and peak time of the hydrolyzed flours were not significantly different (p>0.05). The morphology of native flour was smooth without observable pores and polygonal with sharp angles and edges. However, after hydrolysis, granules with a slightly rough and porous surface were observed and a rough and porous surface was increased with increasing hydrolyzed time. The X-ray diffraction patterns of native flour showed A-type configuration, which hydrolyzed flour showed almost 0% crystallinity indicated that both amorphous and crystalline structures of starch were simultaneously hydrolyzed by α-amylase.

Keywords: α-Amylase, Enzymatic hydrolysis, Pasting properties, Resistant starch

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: Affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, Signal Detection Theory, student engagement.

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: Complementary and alternative medicine, Iridology, iris, feature extraction, classification, disease prediction.

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: Machine learning, medical diagnosis, meningitis detection, gradient boosting.

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Authors: Safyeh Soufian, Hoosein Naderi-Manesh, Abdoali Alizadeh, Mohammad Nabi Sarbolouki

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Aurein 1.2 is a 13-residue amphipathic peptide with antibacterial and anticancer activity. Aurein1.2 and its retro analog were synthesized to study the activity of the peptides in relation to their structure. The antibacterial test result showed the retro-analog is inactive. The secondary structural analysis by CD spectra indicated that both of the peptides at TFE/Water adopt alpha-helical conformation. MD simulation was performed on aurein 1.2 and retro-analog in water and TFE in order to analyse the factors that are involved in the activity difference between retro and the native peptide. The simulation results are discussed and validated in the light of experimental data from the CD experiment. Both of the peptides showed a relatively similar pattern for their hydrophobicity, hydrophilicity, solvent accessible surfaces, and solvent accessible hydrophobic surfaces. However, they showed different in directions of dipole moment of peptides. Also, Our results further indicate that the reversion of the amino acid sequence affects flexibility .The data also showed that factors causing structural rigidity may decrease the activity. Consequently, our finding suggests that in the case of sequence-reversed peptide strategy, one has to pay attention to the role of amino acid sequence order in making flexibility and role of dipole moment direction in peptide activity. KeywordsAntimicrobial peptides, retro, molecular dynamic, circular dichroism.

Keywords: Antimicrobial peptides, retro, molecular dynamic, circular dichroism.

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Authors: Amitabh Wahi, Sundaramurthy S.

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Natural outdoor scene classification is active and promising research area around the globe. In this study, the classification is carried out in two phases. In the first phase, the features are extracted from the images by wavelet decomposition method and stored in a database as feature vectors. In the second phase, the neural classifiers such as back-propagation neural network (BPNN) and resilient back-propagation neural network (RPNN) are employed for the classification of scenes. Four hundred color images are considered from MIT database of two classes as forest and street. A comparative study has been carried out on the performance of the two neural classifiers BPNN and RPNN on the increasing number of test samples. RPNN showed better classification results compared to BPNN on the large test samples.

Keywords: BPNN, Classification, Feature extraction, RPNN, Wavelet.

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Authors: Zaineb Ben Messaoud, Dorra Gargouri, Saida Zribi, Ahmed Ben Hamida

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This paper presents a formant-tracking linear prediction (FTLP) model for speech processing in noise. The main focus of this work is the detection of formant trajectory based on Hidden Markov Models (HMM), for improved formant estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of a time- sequence of peaks which satisfies continuity constraints on parameter; the within peaks are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning multi-band spectral subtraction stage to reduce the effect of residue noise on formants (2) estimation stage where an initial estimate of the LP model of speech for each frame is obtained (3) a formant classification using probability models of formants and Viterbi-decoders. The evaluation results for the estimation of the formant tracking LP model tested in Gaussian white noise background, demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and LPC variable order analysis, results in a significant reduction in errors and distortions. The performance was evaluated with noisy natual vowels extracted from international french and English vocabulary speech signals at SNR value of 10dB. In each case, the estimated formants are compared to reference formants.

Keywords: Formants Estimation, HMM, Multi Band Spectral Subtraction, Variable order LPC coding, White Gauusien Noise.

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Authors: Dubbale Daniel A., Tsutsumi J., Michael J. Bendewald

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Addis Ababa is a seat of African Union (AU), United Nations Economic Commission for Africa (UN-ECA) and hundreds of embassies and consular representatives. Addis Ababa is one of the highest capitals in the world with an average 2400 meters above sea level. It is dichotomous city with a blend of modern high-rise and deteriorating slum quarters. Water supply and sanitation, waste management and housing are continuing to be serious problems. Forest wood based domestic energy use as well as uncontrolled emissions from mobile and fixed sources has endangered the state of the urban environment. Analysis based on satellite imagery has revealed the deteriorating urban environment within the last three decades. The recently restructured city administration has brought improvements in the condition of the urban environment. However, the overwhelming size of the challenges faced by the city dwarfed their fairly good results.

Keywords: Addis Ababa, Urban environment, Slum, Housing, Relocation

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Authors: Shen Weishou, Ji Di, Zhang Hui, Yan Shouguang, Li Haidong, Lin Naifeng

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Based on a long-term vegetation index dataset of NDVI and meteorological data from 68 meteorological stations in the Qinghai-Tibet plateau and their relations with major climate factors were analyzed. The results show the following: 1) The linear trends of temperature in the Qinghai-Tibet plateau indicate that the temperature in the plateau generally increased, but it rose faster in the last 20 years. 2) The most significant NDVI increase occurred in the eastern and southern plateau. However, the western and northern plateau demonstrate a decreasing trend. 3) There is a significant positive linear correlation between NDVI and temperature and a negative correlation between NDVI and mean wind speed. However, no significant statistical relationship was found between NDVI and relative humidity, precipitation or sunshine duration.4) The changes in NDVI for the plateau are driven by temperature-precipitation, but for the desert and forest areas, the relation changes to precipitation-temperature-wind velocity and wind velocity-temperature-precipitation.

Keywords: Qinghai-Tibet plateau, NDVI, climate warming.

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Authors: A. Appe, B. Poluparthi, L. Kasivajjula, U. Mv, S. Bagadi, P. Modi, A. Singh, H. Gunupudi, S. Troiano, J. Paul, J. Stovall, J. Yamamoto

Abstract:

The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data are considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP (SHapley Additive exPlanations), to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since it is data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for e.g., quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP, a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: Competition, DAGs, hospital, healthcare, machine learning, market share, random forest, SHAP.

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Authors: Norah Alshahrani, Abdulaziz Almaleh

Abstract:

Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: Autism Spectrum Disorder, ASD, Machine Learning, ML, Feature Selection, Support Vector Machine, SVM.

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Authors: Muhammad Hazwan H., Azlina M.F., Hasfalina C.M., Zurina Z.A., Hishamuddin J

Abstract:

Gaharu that produced by Aquilaria spp. is classified as one of the most valuable forest products traded internationally as it is very resinous, fragrant and highly valuable heartwood. Gaharu has been widely used in aromatheraphy, medicine, perfume and religious practices. This work aimed to determine the factors affecting solid liquid extraction of gaharu oil using hexane as solvent under experimental condition. The kinetics of extraction was assumed and verified based on a second-order mechanism. The effect of three main factors, which were temperature, reaction time and solvent to solid ratio were investigated to achieve maximum oil yield. The optimum condition were found at temperature 65°C, 9 hours reaction time and solvent to solid ratio of 12:1 with 14.5% oil yield. The kinetics experimental data agrees and well fitted with the second order extraction model. The initial extraction rate (h) was 0.0115 gmL-1min-1; the extraction capacity (Cs) was 1.282gmL-1; the second order extraction constant (k) was 0.007 mLg-1min-1 and coefficient of determination, R2 was 0.945.

Keywords: Gaharu, solid liquid extraction, optimization, kinetics.

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Authors: A. Ouargui, N. Belouaggadia, M. Ezzine

Abstract:

In Morocco, the building sector is largely responsible for the evolution of energy consumption. The control of energy in this sector remains a major issue despite the rise of renewable energies. The design of an environmentally friendly building requires mastery and knowledge of energy and bioclimatic aspects. This implies taking into consideration of all the elements making up the building and the way in which energy exchanges take place between these elements. In this context, thermal insulation seems to be an ideal starting point for reducing energy consumption and greenhouse gas emissions. In this context, thermal insulation seems to be an ideal starting point for reducing energy consumption and greenhouse gas emissions. The aim of this work is to provide some solutions to reduce energy consumption while maintaining thermal comfort in the building. The objective of our work is to present an experimental study on the characterization of local materials used in the thermal insulation of buildings. These are paper recycling stabilized with cement and clay. The thermal conductivity of these materials, which were constituted based on sand, clay, cement; water, as well as treated paper, was determined by the guarded-hot-plate method. It involves the design of two materials that will subsequently be subjected to thermal and mechanical tests to determine their thermophysical properties. The results show that the thermal conductivity decreases as well in the case of the paper-cement mixture as that of the paper-clay and seems to stabilize around 40%. Measurements of mechanical properties such as flexural strength have shown that the enrichment of the studied material with paper makes it possible to reduce the flexural strength by 20% while optimizing the conductivity.

Keywords: Building, composite material, insulation, thermal conductivity, paper residue.

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Authors: R. S. Chandan, M. Vasudevan, Deecaraman, B. M. Gurupadayya

Abstract:

In Pharmaceutical industries, it is very important to remove drug residues from the equipment and areas used. The cleaning procedure must be validated, so special attention must be devoted to the methods used for analysis of trace amounts of drugs. A rapid, sensitive and specific reverse phase ultra performance liquid chromatographic (UPLC) method was developed for the quantitative determination of Albendazole in cleaning validation swab samples. The method was validated using an ACQUITY HSS C₁₈, 50 x 2.1mm, 1.8μ column with a isocratic mobile phase containing a mixture of 1.36g of Potassium dihydrogenphosphate in 1000mL MilliQ water, 2mL of triethylamine and pH adjusted to 2.3 ± 0.05 with ortho-phosphoric acid, Acetonitrile and Methanol (50:40:10 v/v). The flow rate of the mobile phase was 0.5 mL min^-1 with a column temperature of 35⁰C and detection wavelength at 254nm using PDA detector. The injection volume was 2µl. Cotton swabs, moisten with acetonitrile were used to remove any residue of drug from stainless steel, teflon, rubber and silicon plates which mimic the production equipment surface and the mean extraction-recovery was found to be 91.8. The selected chromatographic condition was found to effectively elute Albendazole with retention time of 0.67min. The proposed method was found to be linear over the range of 0.2 to 150µg/mL and correlation coefficient obtained is 0.9992. The proposed method was found to be accurate, precise, reproducible and specific and it can also be used for routine quality control analysis of these drugs in biological samples either alone or in combined pharmaceutical dosage forms.

Keywords: Cleaning validation, Albendazole, residues, swab analysis, UPLC.

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

Abstract:

The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8.

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