Search results for: Crop yield prediction

Authors: Chang S. Woo, Hyun S. Park, Wan D. Kim

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Fatigue life prediction and evaluation are the key technologies to assure the safety and reliability of automotive rubber components. The objective of this study is to develop the fatigue analysis process for vulcanized rubber components, which is applicable to predict fatigue life at initial product design step. Fatigue life prediction methodology of vulcanized natural rubber was proposed by incorporating the finite element analysis and fatigue damage parameter of maximum strain appearing at the critical location determined from fatigue test. In order to develop an appropriate fatigue damage parameter of the rubber material, a series of displacement controlled fatigue test was conducted using threedimensional dumbbell specimen with different levels of mean displacement. It was shown that the maximum strain was a proper damage parameter, taking the mean displacement effects into account. Nonlinear finite element analyses of three-dimensional dumbbell specimens were performed based on a hyper-elastic material model determined from the uni-axial tension, equi-biaxial tension and planar test. Fatigue analysis procedure employed in this study could be used approximately for the fatigue design.

Keywords: Rubber, Material test, Finite element analysis, Strain, Fatigue test, Fatigue life prediction.

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Authors: Chia-Ta Tsai, Wen-Lin Huang, Shinn-Jang Ho, Li-Sun Shu, Shinn-Ying Ho

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Prediction of bacterial virulent protein sequences can give assistance to identification and characterization of novel virulence-associated factors and discover drug/vaccine targets against proteins indispensable to pathogenicity. Gene Ontology (GO) annotation which describes functions of genes and gene products as a controlled vocabulary of terms has been shown effectively for a variety of tasks such as gene expression study, GO annotation prediction, protein subcellular localization, etc. In this study, we propose a sequence-based method Virulent-GO by mining informative GO terms as features for predicting bacterial virulent proteins. Each protein in the datasets used by the existing method VirulentPred is annotated by using BLAST to obtain its homologies with known accession numbers for retrieving GO terms. After investigating various popular classifiers using the same five-fold cross-validation scheme, Virulent-GO using the single kind of GO term features with an accuracy of 82.5% is slightly better than VirulentPred with 81.8% using five kinds of sequence-based features. For the evaluation of independent test, Virulent-GO also yields better results (82.0%) than VirulentPred (80.7%). When evaluating single kind of feature with SVM, the GO term feature performs much well, compared with each of the five kinds of features.

Keywords: Bacterial virulence factors, GO terms, prediction, protein sequence.

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Authors: Chuan-Ching Sue, Shi-Zhou Chen, Ting-Yu Huang

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Dynamic bandwidth allocation in EPONs can be generally separated into inter-ONU scheduling and intra-ONU scheduling. In our previous work, the active intra-ONU scheduling (AS) utilizes multiple queue reports (QRs) in each report message to cooperate with the inter-ONU scheduling and makes the granted bandwidth fully utilized without leaving unused slot remainder (USR). This scheme successfully solves the USR problem originating from the inseparability of Ethernet frame. However, without proper setting of threshold value in AS, the number of QRs constrained by the IEEE 802.3ah standard is not enough, especially in the unbalanced traffic environment. This limitation may be solved by enlarging the threshold value. The large threshold implies the large gap between the adjacent QRs, thus resulting in the large difference between the best granted bandwidth and the real granted bandwidth. In this paper, we integrate AS with a cooperative prediction mechanism and distribute multiple QRs to reduce the penalty brought by the prediction error. Furthermore, to improve the QoS and save the usage of queue reports, the highest priority (EF) traffic which comes during the waiting time is granted automatically by OLT and is not considered in the requested bandwidth of ONU. The simulation results show that the proposed scheme has better performance metrics in terms of bandwidth utilization and average delay for different classes of packets.

Keywords: EPON, Inter-ONU and Intra-ONU scheduling, Prediction, Unused slot remainder

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Authors: A. Manivannan, R. T. Narendhirakannan

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The objective of this research was to investigate biodegradation of water hyacinth (Eichhornia crassipes) to produce bioethanol using dilute-acid pretreatment (1% sulfuric acid) results in high hemicellulose decomposition and using yeast (Pachysolen tannophilus) as bioethanol producing strain. A maximum ethanol yield of 1.14g/L with coefficient, 0.24g g^-1; productivity, 0.015g l^-1h^-1 was comparable to predicted value 32.05g/L obtained by Central Composite Design (CCD). Maximum ethanol yield coefficient was comparable to those obtained through enzymatic saccharification and fermentation of acid hydrolysate using fully equipped fermentor. Although maximum ethanol concentration was low in lab scale, the improvement of lignocellulosic ethanol yield is necessary for large scale production.

Keywords: Acid hydrolysis, Biodegradation, Hemicellulose, Pachysolen tannophilus, Water hyacinth.

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Authors: Sofien Chtourou, Mohamed Chtourou, Omar Hammami

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Embedded systems need to respect stringent real time constraints. Various hardware components included in such systems such as cache memories exhibit variability and therefore affect execution time. Indeed, a cache memory access from an embedded microprocessor might result in a cache hit where the data is available or a cache miss and the data need to be fetched with an additional delay from an external memory. It is therefore highly desirable to predict future memory accesses during execution in order to appropriately prefetch data without incurring delays. In this paper, we evaluate the potential of several artificial neural networks for the prediction of instruction memory addresses. Neural network have the potential to tackle the nonlinear behavior observed in memory accesses during program execution and their demonstrated numerous hardware implementation emphasize this choice over traditional forecasting techniques for their inclusion in embedded systems. However, embedded applications execute millions of instructions and therefore millions of addresses to be predicted. This very challenging problem of neural network based prediction of large time series is approached in this paper by evaluating various neural network architectures based on the recurrent neural network paradigm with pre-processing based on the Self Organizing Map (SOM) classification technique.

Keywords: Address, data set, memory, prediction, recurrentneural network.

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Authors: Mahdi Yaghobi, Mohammad Haeri

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The current methods of predictive controllers are utilized for those processes in which the rate of output variations is not high. For such processes, therefore, stability can be achieved by implementing the constrained predictive controller or applying infinite prediction horizon. When the rate of the output growth is high (e.g. for unstable nonminimum phase process) the stabilization seems to be problematic. In order to avoid this, it is suggested to change the method in the way that: first, the prediction error growth should be decreased at the early stage of the prediction horizon, and second, the rate of the error variation should be penalized. The growth of the error is decreased through adjusting its weighting coefficients in the cost function. Reduction in the error variation is possible by adding the first order derivate of the error into the cost function. By studying different examples it is shown that using these two remedies together, the closed-loop stability of unstable nonminimum phase process can be achieved.

Keywords: GPC, Stability, Varying Weighting Coefficients.

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Authors: Yu Zhao, Min Zhang, Yuanqian Wang, Qiuyu Yu

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The prediction of aesthetics of apparel is helpful for the development of a new type of apparel. This study is to build the quantitative relationship between the aesthetics and its design parameters. In particular, women’s pants have been preliminarily studied. This aforementioned relationship has been carried out by statistical analysis. The contributions of this study include the development of a more personalized apparel design mechanism and the provision of some empirical knowledge for the development of other products in the aspect of aesthetics.

Keywords: Aesthetics, crease line, cropped straight leg pants, knee width.

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Authors: H.R. Balouchi

Abstract:

To evaluate genetic variation of wheat (Triticum aestivum) affected by heat and drought stress on eight Australian wheat genotypes that are parents of Doubled Haploid (HD) mapping populations at the vegetative stage, the water stress experiment was conducted at 65% field capacity in growth room. Heat stress experiment was conducted in the research field under irrigation over summer. Result show that water stress decreased dry shoot weight and RWC but increased osmolarity and means of Fv/Fm values in all varieties except for Krichauff. Krichauff and Kukri had the maximum RWC under drought stress. Trident variety was shown maximum WUE, osmolarity (610 mM/Kg), dry mater, quantum yield and Fv/Fm 0.815 under water stress condition. However, the recovery of quantum yield was apparent between 4 to 7 days after stress in all varieties. Nevertheless, increase in water stress after that lead to strong decrease in quantum yield. There was a genetic variation for leaf pigments content among varieties under heat stress. Heat stress decreased significantly the total chlorophyll content that measured by SPAD. Krichauff had maximum value of Anthocyanin content (2.978 A/g FW), chlorophyll a+b (2.001 mg/g FW) and chlorophyll a (1.502 mg/g FW). Maximum value of chlorophyll b (0.515 mg/g FW) and Carotenoids (0.234 mg/g FW) content belonged to Kukri. The quantum yield of all varieties decreased significantly, when the weather temperature increased from 28 ÔùªC to 36 ÔùªC during the 6 days. However, the recovery of quantum yield was apparent after 8th day in all varieties. The maximum decrease and recovery in quantum yield was observed in Krichauff. Drought and heat tolerant and moderately tolerant wheat genotypes were included Trident, Krichauff, Kukri and RAC875. Molineux, Berkut and Excalibur were clustered into most sensitive and moderately sensitive genotypes. Finally, the results show that there was a significantly genetic variation among the eight varieties that were studied under heat and water stress.

Keywords: Abiotic stress, Genetic variation, Fluorescence, Wheat genotypes.

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Authors: Sirikarn Satimanont, Apanee Luengnaruemitchai, Sujitra Wongkasemjit

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Lignocellulosic materials are new targeted source to produce second generation biofuels like biobutanol. However, this process is significantly resisted by the native structure of biomass. Therefore, pretreatment process is always essential to remove hemicelluloses and lignin prior to the enzymatic hydrolysis. The goals of pretreatment are removing hemicelluloses and lignin, increasing biomass porosity, and increasing the enzyme accessibility. The main goal of this research is to study the important variables such as pretreatment temperature and time, which can give the highest total sugar yield in pretreatment step by using dilute phosphoric acid. After pretreatment, the highest total sugar yield of 13.61 g/L was obtained under an optimal condition at 140°C for 10 min of pretreatment time by using 1.75% (w/w) H3PO4 and at 15:1 liquid to solid ratio. The total sugar yield of two-stage process (pretreatment+enzymatic hydrolysis) of 27.38 g/L was obtained.

Keywords: Butanol production, Corn cobs, Phosphoric acid, Pretreatment

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Authors: Zia Amjad, Salem S. Alghamdi

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The study was conducted at the student educational farm at the College of Food and Agriculture in the Kingdom of Saudi Arabia. The aim of study was to characterize 154 Vicia faba L. accessions using agro-morphological traits based on The International Union for the Protection of New Varieties of Plants (UPOV) and The International Board for Plant Genetic Resources (IBPGR) descriptors. This research is significant as it contributes to the understanding of the genetic diversity and potential yield of V. faba in Saudi Arabia. In the study, 24 agro-morphological characters including 11 quantitative and 13 qualitative were observed for genetic variation. All the results were analyzed using multivariate analysis i.e., principal component analysis (PCA). First, six principal components (PC) had eigenvalues greater than one; accounted for 72% of available V. faba genetic diversity. However, first three components revealed more than 10% of genetic diversity each i.e., 22.36%, 15.86% and 10.89% respectively. PCA distributed the V. faba accessions into different groups based on their performance for the characters under observation. PC-1, which represented 22.36% of the genetic diversity, was positively associated with stipule spot pigmentation, intensity of streaks, pod degree of curvature and to some extent with 100 seed weight. PC-2 covered 15.86 of the genetic diversity and showed positive association for average seed weight per plant, pod length, number of seeds per plant, 100 seed weight, stipule spot pigmentation, intensity of streaks (same as in PC-1) and to some extent for pod degree of curvature and number of pods per plant. PC-3 revealed 10.89% of genetic diversity and expressed positive association for number of pods per plant and number of leaflets per plant. This study contributes to the understanding of the genetic diversity and potential yield of V. faba in the Kingdom of Saudi Arabia. By establishing a core collection of V. faba, the research provides a valuable resource for future conservation and utilization of this crop worldwide.

Keywords: Agro-morphological characterization, genetic diversity, core collection, PCA, Vicia faba L.

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Lèvy flight, situation awareness, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence.

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Authors: Elena Fauquet-Alekhine-Pavlovskaya, Irina M. Nesterova

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On the territory of France fenugreek is spread since long on a line from the Gironde to the Italian border. In Belarus experimental cultivation has begun since 2004. Experiments with fenugreek variety Ovari 4 were conducted about time of sowing in order to study their growth, development, and evaluation of productivity in the North-east part of Belarus and Central part of France. Reaching full ripeness of seeds the plants of fenugreek in the Central part of France requires about 94-97 days. Average seeds yield of 2011-2012 is 1259 kg/ha. Plant height is about 36,8 cm. Plants were affected by aphid and in the high moist agro-climatic conditions by powdery mildew. In North-east part of Belarus plants need 86-93 days to full ripeness. Plants of fenugreek have steam about 59 cm. The average seeds yield of 2007-2009 was about 723 kg/ha. Plants were resistant to aphid and diseases.

Keywords: Fenugreek, time of sowing, variety, yield.

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Authors: Amin A. Jaafar, Murni M. Ahmad

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This project aims to investigate the potential of torrefaction to improve the properties of Malaysian palm kernel shell (PKS) as a solid fuel. A study towards torrefaction of PKS was performed under various temperature and residence time of 240, 260, and 280oC and 30, 60, and 90 minutes respectively. The torrefied PKS was characterized in terms of the mass yield, energy yield, elemental composition analysis, calorific value analysis, moisture and volatile matter contents, and ash and fixed carbon contents. The mass and energy yield changes in the torrefied PKS were observed to prove that the temperature has more effect compare to residence time in the torrefaction process. The C content of PKS increases while H and O contents decrease after torrefaction, which resulted in higher heating value between 5 to 16%. Meanwhile, torrefaction caused the ash and fixed carbon content of PKS to increase, and the moisture and volatile matter to decrease.

Keywords: biomass, palm kernel shell, pretreatment, solid fuel, torrefaction

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Authors: V. G. Rifert, V. V. Gorin, V. V. Sereda, V. V. Treputnev

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The analysis of heat transfer design methods in condensing inside plain tubes under existing influence of shear stress is presented in this paper. The existing discrepancy in more than 30-50% between rating heat transfer coefficients and experimental data has been noted. The analysis of existing theoretical and semi-empirical methods of heat transfer prediction is given. The influence of a precise definition concerning boundaries of phase flow (it is especially important in condensing inside horizontal tubes), shear stress (friction coefficient) and heat flux on design of heat transfer is shown. The substantiation of boundary conditions of the values of parameters, influencing accuracy of rated relationships, is given. More correct relationships for heat transfer prediction, which showed good convergence with experiments made by different authors, are substantiated in this work.

Keywords: Film condensation, heat transfer, plain tube, shear stress.

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Authors: Ratanaporn Leesing, Ratanaporn Baojungharn

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Microbial oil was produced by soil isolated oleaginous yeast YU5/2 in flask-batch fermentation. The yeast was identified by molecular genetics technique based on sequence analysis of the variable D1/D2 domain of the large subunit (26S) ribosomal DNA and it was identified as Torulaspora globosa. T. globosa YU5/2 supported maximum values of 0.520 g/L/d, 0.472 g lipid/g cells, 4.16 g/L, and 0.156 g/L/d for volumetric lipid production rate, and specific yield of lipid, lipid concentration, and specific rate of lipid production respectively, when culture was performed in nitrogen-limiting medium supplemented with 80g/L glucose. Among the carbon sources tested, maximum cell yield coefficient (YX/S, g/L), maximum specific yield of lipid (YP/X, g lipid/g cells) and volumetric lipid production rate (QP, g/L/d) were found of 0.728, 0.237, and 0.619, respectively, using sweet potato tubers hydrolysates as carbon source.

Keywords: Microbial oil, oleaginous yeast, Torulasporaglobosa YU5/2, sweet potato tubers, kinetic parameters.

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Authors: E. A. Mlybari, M. S. Elbisy, A. H. Alshahri, O. M. Albarakati

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Sea level rise threatens to increase the impact of future  storms and hurricanes on coastal communities. Accurate sea level  change prediction and supplement is an important task in determining  constructions and human activities in coastal and oceanic areas. In  this study, support vector machines (SVM) is proposed to predict  daily tidal levels along the Jeddah Coast, Saudi Arabia. The optimal  parameter values of kernel function are determined using a genetic  algorithm. The SVM results are compared with the field data and  with back propagation (BP). Among the models, the SVM is superior  to BPNN and has better generalization performance.

 

Keywords: Tides, Prediction, Support Vector Machines, Genetic Algorithm, Back-Propagation Neural Network, Risk, Hazards.

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Authors: Kamaldeep Kaur, Arvinder Kaur, Ruchika Malhotra

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The aim of this paper is to rank the impact of Object Oriented(OO) metrics in fault prediction modeling using Artificial Neural Networks(ANNs). Past studies on empirical validation of object oriented metrics as fault predictors using ANNs have focused on the predictive quality of neural networks versus standard statistical techniques. In this empirical study we turn our attention to the capability of ANNs in ranking the impact of these explanatory metrics on fault proneness. In ANNs data analysis approach, there is no clear method of ranking the impact of individual metrics. Five ANN based techniques are studied which rank object oriented metrics in predicting fault proneness of classes. These techniques are i) overall connection weights method ii) Garson-s method iii) The partial derivatives methods iv) The Input Perturb method v) the classical stepwise methods. We develop and evaluate different prediction models based on the ranking of the metrics by the individual techniques. The models based on overall connection weights and partial derivatives methods have been found to be most accurate.

Keywords: Artificial Neural Networks (ANNS), Backpropagation, Fault Prediction Modeling.

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Authors: M. Hajzler, J. Klimesova, T. Streda, K. Vejrazka, V. Marecek, T. Cholastova

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The most planted cover crops in the Czech Republic are mustard (Sinapis alba) and phacelia (Phacelia tanacetifolia Benth.). A field trial was executed to evaluate root system size (RSS) in eight varieties of mustard and five varieties of phacelia on two locations, in three BBCH phases and in two years. The relationship between RSS and aboveground biomass was inquired. The root system was assessed by measuring its electric capacity. Aboveground mass and root samples to be evaluated by means of a digital image analysis were recovered in the BBCH phase 70. The yield of aboveground biomass of mustard was always statistically significantly higher than that of phacelia. Mustard showed a statistically significant negative correlation between root length density (RLD) within 10 cm and aboveground biomass weight (r = - 0.46*). Phacelia featured a statistically significant correlation between aboveground biomass production and nitrate nitrogen content in soil (r=0.782**).

Keywords: Aboveground Biomass, Cover crop, Nitrogen content, Root system size

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Authors: Alexandre M. Brighenti

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One of the most damaging species in sunflower crops in Brazil is the hairy beggarticks (Bidens pilosa L.). The large number of seeds, the various vegetative cycles during the year, the staggered germination and the scarcity of selective and effective herbicides to control this weed in sunflower are some of attributes that hinder the effectiveness in controlling hairy beggarticks populations. The experiment was carried out with the objectives of evaluating the control of hairy beggarticks plants in sunflower crops, and to assess sunflower tolerance to residual herbicides. The treatments were as follows: S-metolachlor (1,200 and 2,400 g ai ha^-1), flumioxazin (60 and 120 g ai ha^-1), sulfentrazone (150 and 300 g ai ha^-1) and two controls (weedy and weed-free check). Phytotoxicity on sunflower plants, percentage of control and density of hairy beggarticks plants, sunflower stand and plant height, head diameter, oil content and sunflower yield were evaluated. The herbicides flumioxazin and sulfentrazone were the most efficient in hairy beggarticks control. S-metolachlor provided acceptable control levels. S-metolachlor (1,200 g ha^-1), flumioxazin (60 g ha^-1) and sulfentrazone (150 g ha^-1) were the most selective doses for sunflower crop.

Keywords: Flumioxazin, Helianthus annuus, S-metolachlor, sulfentrazone, weeds.

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Authors: T.A. Adegbola, IEA Aghachi, E.R. Sadiku

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To investigates the effect of fiberglass clamping process improvement on drape simulation prediction. This has great effect on the mould and the fiber during manufacturing process. This also, improves the fiber strain, the quality of the fiber orientation in the area of folding and wrinkles formation during the press-forming process. Drape simulation software tool was used to digitalize the process, noting the formation problems on the contour sensitive part. This was compared with the real life clamping processes using single and double frame set-ups to observe the effects. Also, restrains are introduced by using clips, and the G-clamps with predetermine revolution to; restrain the fabric deformation during the forming process.The incorporation of clamping and fabric restrain deformation improved on the prediction of the simulation tool. Therefore, for effective forming process, incorporation of clamping process into the drape simulation process will assist in the development of fiberglass application in manufacturing process.

Keywords: clamping, fiberglass, drape simulation, pressforming.

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Authors: Sulata Maharana, B Biswas, Adity Ganguly, Ashok Kumar

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In this paper, the requirement for Coke quality prediction, its role in Blast furnaces, and the model output is explained. By applying method of Artificial Neural Networking (ANN) using back propagation (BP) algorithm, prediction model has been developed to predict CSR. Important blast furnace functions such as permeability, heat exchanging, melting, and reducing capacity are mostly connected to coke quality. Coke quality is further dependent upon coal characterization and coke making process parameters. The ANN model developed is a useful tool for process experts to adjust the control parameters in case of coke quality deviations. The model also makes it possible to predict CSR for new coal blends which are yet to be used in Coke Plant. Input data to the model was structured into 3 modules, for tenure of past 2 years and the incremental models thus developed assists in identifying the group causing the deviation of CSR.

Keywords: Artificial Neural Networks, backpropagation, CokeStrength after Reaction, Multilayer Perceptron.

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Authors: Revanth Sai Kosaraju, Michael Ramscar, Melody Dye

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Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.

Keywords: Abstractness, child psychology, language acquisition, prediction and error.

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Authors: Adilah Shariff, Nur Syairah Mohamad Aziz, Norsyahidah Md Saleh, Nur Syuhada Izzati Ruzali

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The first objective of this study is to investigate the suitability of coconut frond (CF) and coconut husk (CH) as feedstocks using a laboratory-scale slow pyrolysis experimental setup. The second objective is to investigate the effect of pyrolysis temperature on the biochar yield. The properties of CF and CH feedstocks were compared. The properties of the CF and CH feedstocks were investigated using proximate and elemental analysis, lignocellulosic determination, and also thermogravimetric analysis (TGA). The CF and CH feedstocks were pyrolysed at 300, 400, 500, 600 and 700 °C for 2 hours at 10 °C/min heating rate. The proximate analysis showed that CF feedstock has 89.96 mf wt% volatile matter, 4.67 mf wt% ash content and 5.37 mf wt% fixed carbon. The lignocelluloses analysis showed that CF feedstock contained 21.46% lignin, 39.05% cellulose and 22.49% hemicelluloses. The CH feedstock contained 84.13 mf wt% volatile matter, 0.33 mf wt% ash content, 15.54 mf wt% fixed carbon, 28.22% lignin, 33.61% cellulose and 22.03% hemicelluloses. Carbon and oxygen are the major component of the CF and CH feedstock compositions. Both of CF and CH feedstocks contained very low percentage of sulfur, 0.77% and 0.33%, respectively. TGA analysis indicated that coconut wastes are easily degraded. It may be due to their high volatile content. Between the temperature ranges of 300 and 800 °C, the TGA curves showed that the weight percentage of CF feedstock is lower than CH feedstock by 0.62%-5.88%. From the D TGA curves, most of the weight loss occurred between 210 and 400 °C for both feedstocks. The maximum weight loss for both CF and CH are 0.0074 wt%/min and 0.0061 wt%/min, respectively, which occurred at 324.5 °C. The yield percentage of both CF and CH biochars decreased significantly as the pyrolysis temperature was increased. For CF biochar, the yield decreased from 49.40 wt% to 28.12 wt% as the temperature increased from 300 to 700 °C. The yield for CH biochars also decreased from 52.18 wt% to 28.72 wt%. The findings of this study indicated that both CF and CH are suitable feedstock for slow pyrolysis of biochar.

Keywords: Biochar, biomass, coconut wastes, slow pyrolysis.

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Authors: Prasit Wonglersak, Prakarnkiat Youngkong, Ittipon Cheowanish

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This paper aims to improve a fine lapping process of hard disk drive (HDD) lapping machines by removing materials from each slider together with controlling the strip height (SH) variation to minimum value. The standard deviation is the key parameter to evaluate the strip height variation, hence it is minimized. In this paper, a design of experiment (DOE) with factorial analysis by twoway analysis of variance (ANOVA) is adopted to obtain a statistically information. The statistics results reveal that initial stripe height patterns affect the final SH variation. Therefore, initial SH classification using a radial basis function neural network is implemented to achieve the proportional gain prediction.

Keywords: Stripe height variation, Two-way analysis ofvariance (ANOVA), Radial basis function neural network, Proportional gain prediction.

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Authors: E. Muzenda

Abstract:

Group contribution methods such as the UNIFAC are of major interest to researchers and engineers involved synthesis, feasibility studies, design and optimization of separation processes as well as other applications of industrial use. Reliable knowledge of the phase equilibrium behavior is crucial for the prediction of the fate of the chemical in the environment and other applications. The objective of this study was to predict the solubility of selected volatile organic compounds (VOCs) in glycol polymers and biodiesel. Measurements can be expensive and time consuming, hence the need for thermodynamic models. The results obtained in this study for the infinite dilution activity coefficients compare very well those published in literature obtained through measurements. It is suggested that in preliminary design or feasibility studies of absorption systems for the abatement of volatile organic compounds, prediction procedures should be implemented while accurate fluid phase equilibrium data should be obtained from experiment.

Keywords: Volatile organic compounds, Prediction, Phaseequilibrium, Environmental, Infinite dilution.

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Authors: J. Menčík, B. Culek, Jr., L. Beran, J. Mareš

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Large metal and concrete structures suffer by various kinds of deterioration, and accurate prediction of the remaining life is important. This paper informs about two methods for its assessment. One method, suitable for steel bridges and other constructions exposed to fatigue, monitors the loads and damage accumulation using information systems for the operation and the finite element model of the construction. In addition to the operation load, the dead weight of the construction and thermal stresses can be included into the model. The second method is suitable for concrete bridges and other structures, which suffer by carbonatation and other degradation processes, driven by diffusion. The diffusion constant, important for the prediction of future development, can be determined from the depth-profile of pH, obtained by pH measurement at various depths. Comparison with measurements on real objects illustrates the suitability of both methods.

Keywords: Bridges, carbonatation, concrete, diagnostics, fatigue, life prediction, monitoring, railway, simulation, structures.

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Authors: Mohamed M. A. Abdalla, M. F Mohamed, A. M. A. Rashwan

Abstract:

A field experiment was conducted in the Agricultural Research Station, at El-Ghoraieb, Assiut to study dry seed yield performance of two locally adapted cultivars (‘Azmerly’ and ‘Cream 7’) and two line introductions (IT81D-1032 and IT82D-812) of common cowpea (Vigna unguiculata (L.) Walp) grown at three different within-row spaces (20, 30 and 40 cm) and two planting dates in the summer (April 15th and 30th) and in the fall season (Aug. 12th and 27th) of two successive seasons. The data showed that total dry-seed yield produced by plants grown at 20 cm was greater than at 30 cm in all cvs/lines in both years. Increases in 1000-seed weight were detected in cv ‘Azmerly’ and line IT82D-812 when they were grown at 30 cm as compared with 20 cm in the summer season. However, in the fall season such increases were found in all cvs/lines. Planting at 40 cm produced seeds of greater weight than planting at 30 cm for all cvs/lines in the fall season and also in cv. Cream 7 and line IT82D-812 in the summer season, while all cvs/lines in the fall Planting on April 15th in the summer and also planting on Aug. 12th in the fall had plants which showed increases in 1000-seed weight and total dry-seed yield. The greatest 1000-seed weight was found in the line IT81D-1032 in the summer season and in the line IT82D-812 in the fall season. The sum up results revealed that ‘Azmerly’ produced greater dry-seed yield than ‘Cream 7’ and both of them were superior to the line IT82D-812 and IT81D-1032 in the summer season. In the fall, however, the line IT82D-812 produced greater dry-seed yield than the other cultivars/lines.

Keywords: Cowpea, Assiut, fall, planting dates, El-Ghoraieb.

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

Abstract:

Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity and aflatoxinogenic capacity of the strains, topography, soil and climate parameters of the fig orchards are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high-performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques i.e., dimensionality reduction on the original dataset (Principal Component Analysis), metric learning (Mahalanobis Metric for Clustering) and K-nearest Neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson Correlation Coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Yong Zhao, Jian He, Cheng Zhang

Abstract:

Cardiovascular disease resulting from hypertension poses a significant threat to human health, and early detection of hypertension can potentially save numerous lives. Traditional methods for detecting hypertension require specialized equipment and are often incapable of capturing continuous blood pressure fluctuations. To address this issue, this study starts by analyzing the principle of heart rate variability (HRV) and introduces the utilization of sliding window and power spectral density (PSD) techniques to analyze both temporal and frequency domain features of HRV. Subsequently, a hypertension prediction network that relies on HRV is proposed, combining Resnet, attention mechanisms, and a multi-layer perceptron. The network leverages a modified ResNet18 to extract frequency domain features, while employing an attention mechanism to integrate temporal domain features, thus enabling auxiliary hypertension prediction through the multi-layer perceptron. The proposed network is trained and tested using the publicly available SHAREE dataset from PhysioNet. The results demonstrate that the network achieves a high prediction accuracy of 92.06% for hypertension, surpassing traditional models such as K Near Neighbor (KNN), Bayes, Logistic regression, and traditional Convolutional Neural Network (CNN).

Keywords: Feature extraction, heart rate variability, hypertension, residual networks.

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Authors: P. Bhramara, V. D. Rao, K. V. Sharma , T. K. K. Reddy

Abstract:

In designing of condensers, the prediction of pressure drop is as important as the prediction of heat transfer coefficient. Modeling of two phase flow, particularly liquid – vapor flow under diabatic conditions inside a horizontal tube using CFD analysis is difficult with the available two phase models in FLUENT due to continuously changing flow patterns. In the present analysis, CFD analysis of two phase flow of refrigerants inside a horizontal tube of inner diameter, 0.0085 m and 1.2 m length is carried out using homogeneous model under adiabatic conditions. The refrigerants considered are R22, R134a and R407C. The analysis is performed at different saturation temperatures and at different flow rates to evaluate the local frictional pressure drop. Using Homogeneous model, average properties are obtained for each of the refrigerants that is considered as single phase pseudo fluid. The so obtained pressure drop data is compared with the separated flow models available in literature.

Keywords: Adiabatic conditions, CFD analysis, Homogeneousmodel and Liquid – Vapor flow.

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