Search results for: soil texture prediction

Authors: Moudi Almousa

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This research paper explores Saudi Arabian female consumers’ experiences using 3D body scanning technology and their level of acceptance of possible market applications of this technology to adopt for apparel online shopping. Data was collected for 82 women after being scanned then viewed a short video explaining three possible scenarios of 3D body scanning applications, which include size prediction, customization, and virtual try-on, before completing the survey questionnaire. Although respondents have strong positive responses towards the scanning experience, the majority were concerned about their privacy during the scanning process. The results indicated that size prediction and virtual try on had greater market application potential and a higher chance of crossing the gap based on consumer interest. The results of the study also indicated a strong positive correlation between respondents’ concern with inability to try on apparel products in online environments and their willingness to use the 3D possible market applications.

Keywords: 3D body scanning, market applications, online, apparel fit

Procedia PDF Downloads 145

Authors: H. Aripin, I. Made Joni, Edvin Priatna, Nundang Busaeri, Svilen Sabchevski

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In this investigation, manganese has been produced from medium grade manganese ore from Karangnunggal mine (West Java, Indonesia). The ores were grinded using a jar mill to pass through a 150 mesh sieve. The effects of keeping it at a temperature of 1200 °C in methane gas on the structural properties have been studied. The material’s properties have been characterized on the basis of the experimental data obtained using X-ray fluorescence (XRF), X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Fourier transform infrared (FTIR) spectroscopy. It has been found that the ore contains MnO₂ as the main constituents at about 46.80 wt.%. It can be also observed that the ore particles are agglomerated forming dense grains with different texture and morphology. The irregular-shaped grains with dark contrast, the large brighter grains, and smaller grains with bright texture and smooth surfaces are associated with the presence of manganese, calcium, and quartz, respectively. From XRD patterns, MnO₂ is reduced to hausmannite (Mn₃O₄), manganosite (MnO) and manganese carbide (Mn₇C₃). At a temperature of 1200°C the keeping time does not have any effect on the formation of crystals and the crystalline phases remain almost unchanged in the time range from 15 to 90 minutes. An increase of the keeping time up to 45 minutes during the sintering process leads to an increase of the MnO concentration, while at 90 minutes, the concentration decreases. At longer keeping times the excess reaction of the methane gas and manganese oxide in the ore causes an increase of carbon deposition. As a result, it blocks the particle surface and then hinders the reduction process of manganese oxide. From FTIR spectrum allows one to explain that the appearance of C=O stretching mode arises from absorption of atmospheric methane and manganese oxide of the ore. The intensity of this band increases with increasing the keeping time, indicating an increase of carbon deposition on the surface of manganese oxide.

Keywords: manganese, medium grade manganese ore, structural properties, keeping the temperature, carbon deposition

Procedia PDF Downloads 156

Authors: Thidathit Prachanukool, Chaiyaporn Yuksen, Welawat Tienpratarn, Sorravit Savatmongkorngul, Panvilai Tangkulpanich, Chetsadakon Jenpanitpong, Yuranan Phootothum, Malivan Phontabtim, Promphet Nuanprom

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Background: Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to developed the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods: This study was diagnostic, retrospectively cross-sectional and exploratory model at the Emergency Medicine Department in Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN)>75%, and the pain duration before presentation. The predictive model and prediction score for ruptured appendicitis was developed by multivariable logistic regression analysis. Result: During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score > 6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion: Using the Ramathibodi Welawat Ruptured Appendicitis Score. (RAMA WeRA Score) developed in this study, a score of > 6 was associated with ruptured appendicitis.

Keywords: predictive model, risk score, ruptured appendicitis, emergency room

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Authors: Yasser R. Tawfic, Mohamed A. Eid

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Foundation differential settlement and supported structure tilting is an occasionally occurred engineering problem. This may be caused by overloading, changes in ground soil properties or unsupported nearby excavations. Engineering thinking points directly toward the logic solution for such problem by uplifting the settled side. This can be achieved with deep foundation elements such as micro-piles and macro-piles™, jacked piers and helical piers, jet grouted soil-crete columns, compaction grout columns, cement grouting or with chemical grouting, or traditional pit underpinning with concrete and mortar. Although, some of these techniques offer economic, fast and low noise solutions, many of them are quite the contrary. For tilted structures, with limited inclination, it may be much easier to cause a balancing settlement on the less-settlement side which shall be done carefully in a proper rate. This principal has been applied in Leaning Tower of Pisa stabilization with soil extraction from the ground surface. In this research, the authors attempt to introduce a new solution with a different point of view. So, micro-tunneling technique is presented in here as an intended ground deformation cause. In general, micro-tunneling is expected to induce limited ground deformations. Thus, the researchers propose to apply the technique to form small size ground unsupported holes to produce the target deformations. This shall be done in four phases: •Application of one or more micro-tunnels, regarding the existing differential settlement value, under the raised side of the tilted structure. •For each individual tunnel, the lining shall be pulled out from both sides (from jacking and receiving shafts) in slow rate. •If required, according to calculations and site records, an additional surface load can be applied on the raised foundation side. •Finally, a strengthening soil grouting shall be applied for stabilization after adjustment. A finite element based numerical model is presented to simulate the proposed construction phases for different tunneling positions and tunnels group. For each case, the surface settlements are calculated and induced plasticity points are checked. These results show the impact of the suggested procedure on the tilted structure and its feasibility. Comparing results also show the importance of the position selection and tunnels group gradual effect. Thus, a new engineering solution is presented to one of the structural and geotechnical engineering challenges.

Keywords: differential settlement, micro-tunneling, soil-structure interaction, tilted structures

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Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

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Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Noppadon Sintuboon, Benjamas Sawatdipong, Anchalee Kongsuk

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This study was conducted to investigate the engineering parameters: compressional wave: Vp, shear wave: Vs, and density: ρ related to the dynamically geotechnical properties of soils compaction in the core of earth-filled dam located in northern part of Thailand by using multichannel analysis of surface wave (MASW) techniques. The Vp, Vs, and ρ from MASW were 1,624 - 1,649 m/s, 301-323 m/s, and 1,829 kg/m3, respectively. Those parameters were calculated to Poison’s ratio: ν (0.48), shear modulus: G (1.66 x 108 - 1.92 x 108 kg/m2), Vp/Vs ratio (5.10 – 5.39) and Standard Penetration Test (SPT) showing the dynamic characteristics of soil deformation and stress resulting from dynamic loads. The results of this study will be useful in primary evaluating the current condition and foundation of the dam and can be compared to the data from the laboratory in the future.

Keywords: earth-filled dam, MASW, dynamic elastic constant, shear wave

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Authors: Shadi Fathi, Moura Mehravar, Mujib Rahman

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The railwaytransportation network in the UK is over 100 years old and is known as one of the oldest mass transit systems in the world. This aged track network requires frequent closure for maintenance. One of the main reasons for closure is inadequate drainage due to the leakage in the buried drainage pipes. The leaking water can cause localised subgrade weakness, which subsequently can lead to major ground/substructure failure.Different condition assessment methods are available to assess the railway substructure. However, the existing condition assessment methods are not able to detect any local ground weakness/damageand provide details of the damage (e.g. size and location). To tackle this issue, a hybrid back-analysis technique based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to predict the substructurelayers’ moduli and identify any soil weaknesses. At first, afinite element (FE) model of a railway track section under Falling Weight Deflection (FWD) testing was developed and validated against field trial. Then a drainage pipe and various scenarios of the local defect/ soil weakness around the buried pipe with various geometriesand physical properties were modelled. The impact of the soil local weaknesson the track surface deflection wasalso studied. The FE simulations results were used to generate a database for ANN training, and then a GA wasemployed as an optimisation tool to optimise and back-calculate layers’ moduli and soil weakness moduli (ANN’s input). The hybrid ANN-GA back-analysis technique is a computationally efficient method with no dependency on seed modulus values. The modelcan estimate substructures’ layer moduli and the presence of any localised foundation weakness.

Keywords: finite element (FE) model, drainage defect, falling weight deflectometer (FWD), hybrid ANN-GA

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: B. Makoudi, R. Ghanimi, A. Bargaz, M. Mouradi, M. Farissi, A. Kabbaj, J. J. Drevon, C. Ghoulam

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Legume species are able to establish a nitrogen fixing symbiosis with soil rhizobia that allows them, when it operates normally, to ensure their necessary nitrogen nutrition. This biological process needs high phosphorus (P) supply and consequently it is limited under low phosphorus availability. To overcome this constraint, legume-rhizobia symbiosis develops many mechanisms to increase P availability in the rhizosphere and also the efficiency of P fertilizers. The objectives of our research works are to understand the physiological and biochemical mechanisms implemented by legume-rhizobia symbiosis to increase its P use efficiency (PUE) in order to select legume genotypes-rhizobia strains combination more performing for BNF under P deficiency. Our studies were carried out on two grain legume species, common bean (Phaseolus vulgaris) and faba bean (Vicia faba) tested in farmers’ fields and in experimental station fewer than two soil phosphorus levels. Under field conditions, the P deficiency caused a significant decrease of Plant and nodule biomasses in all of the tested varieties with a difference between them. This P limitation increased the contents of available P in the rhizospheric soils that was positively correlated with the increase of phosphatases activities in the nodules and the rhizospheric soil. Some legume genotypes showed a significant increase of their P use efficiency under P deficiency. The P solubilization test showed that some rhizobia strains isolated from Haouz region presented an important capacity to grow on solid and liquid media with tricalcium phosphate as the only P source and their P solubilizing activity was confirmed by the assay of the released P in the liquid medium. Also, this P solubilizing activity was correlated with medium acidification and the excretion of acid phosphatases and phytases in the medium. Thus, we concluded that medium acidification and excretion of phosphatases in the rhizosphere are the prominent reactions for legume-rhizobia symbiosis to improve its P nutrition.

Keywords: legume, phosphorus deficiency, rhizobia, rhizospheric soil

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Authors: Shaymaa Kennedy, Sam Clark, Paul Shaply

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With the upcoming construction of high-speed rail HS2 in the UK, a number of issues surrounding the construction technology and track design need to be answered. In this paper performance of subsoil subjected to dynamic loads were studied. The material of study is Mudrock backfill, a weak prevalent rock which response under indicative loading of high-speed rail line is unknown. This paper aims to investigate the use of different track types and the influence they will have on the underlying soil, in order to evaluate the behaviour of it. Ballstless track is a well-established concept in Europe, and the investigation the benefit of the form of construction due to its known savings in maintenance costs. Physical test using a triaxial cyclic loading machine was conducted to assess the expected mechanical behaviour of mudrock under a range of dynamic loads which could be generated beneath different track constructions. Some further parameters are required to frame the problem including determining the stress change with depth and cyclic response are vital to determine the residual plastic strain which is a major concern. In addition, Stress level is discussed in this paper, which are applied to recreate conditions of soil in the laboratory. Results indicate that stress levels are highly influential on the performance of soil at shallower depth and become insignificant with increasing depth.

Keywords: stress level, dynamic load, residual plastic strain, high speed railway

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Authors: Marie Madeleine Nanga Ndjang, Julie Mathilde Klang, Edwin M. Mmutlane, Derek Tantoh Ndinteh, Eugenie Kayitesi, Francois Ngoufack Zambou

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Gluten intolerance and the unavailability of wheat flour in some parts of the world have led to the development of gluten-free bread. However, gluten-free bread generally results in a low specific volume, and to remedy this, the use of hydrocolloids and bases has proved to be very successful. Thus, the present study aims to determine the optimal proportions of gum extract of Triumffetapentendraand sodium bicarbonate in breadmaking of a composite flour based on sour cassava starch, peanut, and cowpea flour. To achieve this, a BoxBenkhendesign was used, the variable being the amount of extract gums, the amount of bicarbonate, and the amount of water. The responses evaluated were the specific volume and texture properties (Hardness, Cohesiveness, Consistency, Elasticity, and Masticability). The specific volume was done according to standard methods of AACC and the textural properties by a texture analyzer. It appears from this analysis that the specific volume is positively influenced by the incorporation of extract gums, bicarbonate, and water. The hardness, consistency, and plasticity increased with the incorporation rate of extract gums but reduced with the incorporation rate of bicarbonate and water. On the other hand, Cohesion and elasticity increased with the incorporation rate of bicarbonate and water but reduced with the incorporation of extract gum. The optimate proportions of extract gum, bicarbonate, and water are 0.28;1.99, and 112.5, respectively. This results in a specific volume of 1.51; a hardness of 38.51; a cohesiveness of 0.88; a consistency of 32.86; an elasticity of 5.57, and amasticability of 162.35. Thus, this analysis suggests that gum extracts and sodium bicarbonate can be used to improve the quality of gluten-free bread.

Keywords: box benkhen design, bread-making, gums, textures properties, specific volume

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Authors: Robespierre Chavez, Eden Bojorquez, Alfredo Reyes-Salazar

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This study compares the seismic response of regular and vertically irregular steel frames determined by nonlinear time history analysis and by using several sets of earthquake records, which are divided in two categories: The first category having 20 stiff-soil ground motion records obtained from the NGA database, and the second category having 30 soft-soil ground motions recorded in the Lake Zone of Mexico City and exhibiting a dominant period (Ts) of two seconds. The steel frames in both format regular and irregular were designed according to the Mexico City Seismic Design Provisions (MCSDP). The effects of irregularity throught the height on the maximum interstory drifts are estimated.

Keywords: irregular steel frames, maximum interstory drifts, seismic response, seismic records

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Authors: Vijay S. Katta, Vinod Kushwah, Rudraksh Tiwari, Mulayam Singh Gaur, Priti Dimri, Ashok Kumar Sharma

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The formation process of seismic activities because of abrupt slip on faults, tectonic plate moments due to accumulated stress in the Earth’s crust. The prediction of seismic activity is a very challenging task. We have studied the changes in surface latent heat temperatures which are observed prior to significant earthquakes have been investigated and could be considered for short term earthquake prediction. We analyzed the surface latent heat temperature (SLHT) variation for inland earthquakes occurred in Chamba, Himachal Pradesh (32.5 N, 76.1E, M-4.5, depth-5km) nearby the main boundary fault region, the data of SLHT have been taken from National Center for Environmental Prediction (NCEP). In this analysis, we have calculated daily variations with surface latent heat temperature (0C) in the range area 1⁰x1⁰ (~120/KM²) with the pixel covering epicenter of earthquake at the center for a three months period prior to and after the seismic activities. The mean value during that period has been considered in order to take account of the seasonal effect. The monthly mean has been subtracted from daily value to study anomalous behavior (∆SLHT) of SLHT during the earthquakes. The results found that the SLHTs adjacent the epicenters all are anomalous high value 3-5 days before the seismic activities. The abundant surface water and groundwater in the epicenter and its adjacent region can provide the necessary condition for the change of SLHT. To further confirm the reliability of SLHT anomaly, it is necessary to explore its physical mechanism in depth by more earthquakes cases.

Keywords: surface latent heat temperature, satellite data, earthquake, magnetic storm

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Authors: M. Heydari Vini

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There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Nastaran Moosavi, Mohammad Mokhtari

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Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.

Keywords: density, p-impedance, s-impedance, post-stack seismic inversion, pre-stack seismic inversion

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Authors: Hameed Ullah, Shujaul Mulk Khan, Zahid Ullah, Zeeshan Ahmad Sadia Jahangir, Abdullah, Amin Ur Rahman, Muhammad Suliman, Dost Muhammad

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The interrelationship between vegetation and abiotic variables inside an ecosystem is one of the main jobs of plant scientists. This study was designed to investigate the vegetation structure and species diversity along with the environmental variables in the Yakhtangay hill district Shangla of the Himalayan Mountain series Pakistan by using multivariate statistical analysis. Quadrat’s method was used and a total of 171 Quadrats were laid down 57 for Tree, Shrubs and Herbs, respectively, to analyze the phytosociological attributes of the vegetation. The vegetation of the selected area was classified into different Life and leaf-forms according to Raunkiaer classification, while PCORD software version 5 was used to classify the vegetation into different plants communities by Two-way indicator species Analysis (TWINSPAN). The CANOCCO version 4.5 was used for DCA and CCA analysis to find out variation directories of vegetation with different environmental variables. A total of 114 plants species belonging to 45 different families was investigated inside the area. The Rosaceae (12 species) was the dominant family followed by Poaceae (10 species) and then Asteraceae (7 species). Monocots were more dominant than Dicots and Angiosperms were more dominant than Gymnosperms. Among the life forms the Hemicryptophytes and Nanophanerophytes were dominant, followed by Therophytes, while among the leaf forms Microphylls were dominant, followed by Leptophylls. It is concluded that among the edaphic factors such as soil pH, the concentration of soil organic matter, Calcium Carbonates concentration in soil, soil EC, soil TDS, and physiographic factors such as Altitude and slope are affecting the structure of vegetation, species composition and species diversity at the significant level with p-value ≤0.05. The Vegetation of the selected area was classified into four major plants communities and the indicator species for each community was recorded. Classification of plants into 4 different communities based upon edaphic gradients favors the individualistic hypothesis. Indicator Species Analysis (ISA) shows the indicators of the study area are mostly indicators to the Himalayan or moist temperate ecosystem, furthermore, these indicators could be considered for micro-habitat conservation and respective ecosystem management plans.

Keywords: species richness, edaphic gradients, canonical correspondence analysis (CCA), TWCA

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Authors: Sang Heon Han, Daejun Chang, Won Yang

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NOx reduction characteristics of syngas fuel were numerically investigated for the 2MW pilot scale heavy oil furnace of KITECH (Korea Institute of Industrial Technology). The secondary fuel and syngas was fed into the furnace with two purposes- partial replacement of main fuel and reburning of NOx. Some portion of syngas was fed into the flame zone to partially replace the heavy oil, while the other portion was fed into the furnace downstream to reduce NOx generation. The numerical prediction was verified by comparing it with the experimental results. Syngas of KITECH’s experiment, assumed to be produced from biomass, had very low calorific value and contained 3% hydrocarbon. This study investigated the precise behavior of NOx generation and NOx reduction as well as thermo-fluidic characteristics inside the furnace, which was unavailable with experiment. In addition to 3% hydrocarbon syngas, 5%, and 7% hydrocarbon syngas were numerically tested as reburning fuels to analyze the effect of hydrocarbon proportion to NOx reduction. The prediction showed that the 3% hydrocarbon syngas is as much effective as 7% hydrocarbon syngas in reducing NOx.

Keywords: syngas, reburning, heavy oil, furnace

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Authors: Md. Kausar Alam, Ramin Motamed

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The damaging effects of liquefaction-induced ground movements have been frequently observed in past earthquakes, such as the 2010-2011 Canterbury Earthquake Sequence (CES) in New Zealand and the 2011 Tohoku earthquake in Japan. To reduce the consequences of soil liquefaction at shallow depths, various ground improvement techniques have been utilized in engineering practice, among which this research is focused on experimentally evaluating the press-in sheet piling technique. The press-in sheet pile technique eliminates the vibration, hammering, and noise pollution associated with dynamic sheet pile installation methods. Unfortunately, there are limited experimental studies on the press-in sheet piling technique for liquefaction mitigation using 1g shake table tests in which all the controlling mechanisms of liquefaction-induced foundation settlement, including sand ejecta, can be realistically reproduced. In this study, a series of moderate scale 1g shake table experiments were conducted at the University of Nevada, Reno, to evaluate the performance of this technique in liquefiable soil layers. First, a 1/5 size model was developed based on a recent UC San Diego shaking table experiment. The scaled model has a density of 50% for the top crust, 40% for the intermediate liquefiable layer, and 85% for the bottom dense layer. Second, a shallow foundation is seated atop an unsaturated sandy soil crust. Third, in a series of tests, a sheet pile with variable embedment depth is inserted into the liquefiable soil using the press-in technique surrounding the shallow foundations. The scaled models are subjected to harmonic input motions with amplitude and dominant frequency properly scaled based on the large-scale shake table test. This study assesses the performance of the press-in sheet piling technique in terms of reductions in the foundation movements (settlement and tilt) and generated excess pore water pressures. In addition, this paper discusses the cost-effectiveness and carbon footprint features of the studied mitigation measures.

Keywords: excess pore water pressure, foundation settlement, press-in sheet pile, soil liquefaction

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Nayara Torres B. Acioli, José Fernando T. Jucá

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The uncontrolled emission of gases in urban residues' embankment located near urban areas is a social and environmental problem, common in Brazilian cities. Several environmental impacts in the local and global scope may be generated by atmospheric air contamination by the biogas resulted from the decomposition of solid urban materials. In Brazil, the cities of small size figure mostly with 90% of all cities, with the population smaller than 50,000 inhabitants, according to the 2011 IBGE' census, most of the landfill covering layer is composed of clayey, pure soil. The embankments undertaken with pure soil may reach up to 60% of retention of methane, for the other 40% it may be dispersed into the atmosphere. In face of this figures the oxidative covering layer is granted some space of study, envisaging to reduce this perceptual available in the atmosphere, releasing, in spite of methane, carbonic gas which is almost 20 times as less polluting than Methane. This paper exposes the results of studies on the characteristics of the soil used for the oxidative coverage layer of the experimental embankment of Solid Urban Residues (SUR), built in Muribeca-PE, Brazil, supported of the Group of Solid Residues (GSR), located at Federal University of Pernambuco, through laboratory vacuum experiments (determining the characteristics curve), granularity, and permeability, that in soil with saturation over 85% offers dramatic drops in the test of permeability to the air, by little increments of water, based in the existing Brazilian norm for this procedure. The suction was studied, as in the other tests, from the division of prospection of an oxidative coverage layer of 60cm, in the upper half (0.1 m to 0.3 m) and lower half (0.4 m to 0.6 m). Therefore, the consequences to be presented from the lixiviation of the fine materials after 5 years of finalization of the embankment, what made its permeability increase. Concerning its humidity, it is most retained in the upper part, that comprises the compound, with a difference in the order of 8 percent the superior half to inferior half, retaining the least suction from the surface. These results reveal the efficiency of the oxidative coverage layer in retaining the rain water, it has a lower cost when compared to the other types of layer, offering larger availability of this layer as an alternative for a solution for the appropriate disposal of residues.

Keywords: oxidative coverage layer, permeability, suction, saturation

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Authors: Roike Iwan Montolalu, Henny Adeleida Dien, Feny Mentang, Kristhina P. Rahael, Tomy Moga, Ayub Meko, Siegfried Berhimpon

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In the last 20 years, packaging materials derived from petrochemicals polymers were widely used as packaging materials. This due to various advantages such as flexible, strong, transparent, and the price is relatively cheap. However, the plastic polymer also has various disadvantages, such as the transmission monomer contamination into the material to be packed, and waste is non-biodegradable. Edible film (EF) is an up to date materials, generated after the biodegradable packaging materials. The advantages of the EF materials, is the materials can be eat together with food, and the materials can be applied as a coating materials for a widely kind of foods especially snack foods. The aims of this research are to produce and to analyze the characteristics of smoked EF made from carrageenan, myofibril and collagen of Black Marlin (Makaira indica) industrial waste. Smoked EF made with an addition of 0.8 % smoke liquid. Three biopolymers i.e. carrageenan, myofibril, and collagen were used as treatments, and homogenate for 1 hours at speed of 1500 rpm. The analysis carried out on the pH and physical properties i.e. thickness, solubility, tensile strength, % elongation, and water vapor transmission rate (WVTR), as well as on the sensory characteristics of texture i.e. wateriness, firmness, elasticity, hardness, and juiciness of the coated products. The result shown that the higher the concentration the higher the thickness of EF, where as for myofibril proteins appeared higher than carrageenan and collagen. Both of collagen and myofibril shown that concentration of 6% was most soluble, while for carrageenan were in concentration of 2 to 2.5%. For tensile strength, carrageenan was significantly higher than myofibril and collagen; while for elongation, collagen film more elastic than carragenan and myofibril protein. Water vapor transmission rate, shown that myofibril protein film lower than carrageenan and collagen film. From sensory assessment of texture, carrageenan has a high elasticity and juiciness, while collagen and myofibril have a high in firmness and hardness.

Keywords: edible film, collagen, myofibril, carrageenan

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Authors: Magdalena Rost-Roszkowska, Izabela Poprawa, Alina Chachulska-Zymelka, Lukasz Chajec, Grazyna Wilczek, Piotr Wilczek, Malgorzata Lesniewska

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Lithobius forficatus, commonly known as the brown centipede, is a widespread European species, which lives in the upper layers of soil, under stones, litter, rocks, and leaves. As the soil organism, it is exposed to numerous stressors such as xenobiotics, including heavy metals, temperature, starvation, pathogens, etc. Heavy metals are treated as the environmental pollutants of the soil because of their toxic effects on plants, animals and human being. One of the heavy metals which is xenobiotic and can be taken up by plants or animals from the soil is cadmium. The digestive system of centipedes is composed of three distinct regions: fore-, mid- and hindgut. The salivary glands of centipedes are the organs which belong to the anterior region of the digestive system and take part in the synthesis, accumulation, and secretion of many substances. The middle region having contact with the food masses is treated as one of the barriers which protect the organism against any stressors which originate from the external environment, e.g., toxic metals. As the material for our studies, we chose two organs of the digestive system in brown centipede, the organs which take part in homeostasis maintenance: the salivary glands and the midgut. The main purpose of the project was to investigate the relationship between the percentage of depolarized mitochondria, mitophagy and ATP level in cells of mentioned above organs. The animals were divided into experimental groups: K – the control group, the animals cultured in a laboratory conditions in a horticultural soil and fed with Acheta domesticus larvae; Cd1 – the animals cultured in a horticultural soil supplemented with 80 mg/kg (dry weight) of CdCl2, fed with A. domesticus larvae maintained in tap water, 12 days – short-term exposure; Cd2 – the animals cultured in a horticultural soil supplemented with 80 mg/kg (dry weight) of CdCl2, fed with A. domesticus larvae maintained in tap water, 45 days – long-term exposure. The studies were conducted using transmission electron microscopy (TEM), flow cytometry and confocal microscopy. Quantitative analysis revealed that regardless of the organ, a progressive increase in the percentage of cells with depolarized mitochondria was registered, but only in the salivary glands. These were statistically significant changes from the control. In both organs, there were no differences in the level of the analyzed parameter depending on the duration of exposure of individuals to cadmium. Changes in the ultrastructure of mitochondria have been observed. With the extension of the body's exposure time to metal, an increase in the ADP/ATP index was recorded. However, changes statistically significant to the control were demonstrated in the intestine and salivary glands. The size of this intestinal index and salivary glands in the Cd2 group was about thirty and twenty times higher, respectively than in control. Acknowledgment: The study has been financed by the National Science Centre, Poland, grant no 2017/25/B/NZ4/00420.

Keywords: cadmium, digestive system, ultrastructure, centipede

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Authors: Obafemi Akinwotu, Aylin Tas, Tony Taylor, Bukola Onarinde

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The study was conducted to explore the methods and tools to improve texture and preserve the total phenolic and antioxidant compounds of dysphagia foods produced from carrot-based puree with decolourised Chlorella algae. Textural properties (Texture profile analysis [TPA]; the International Dysphagia Diet Standardization Initiative, particle size test [PST]) and rheological properties (viscosity and viscoelastic properties) of carrot puree defrosted by different treatments (microwave, steamer, oven), were characterised using hydrocolloids (guar gum, k. carrageenan, and xanthan gum), and the results were compared to a level 4 commercial sample. DPPH (2,2-diphenyl-1-picrylhydrazyl) antiradical scavenging radicals and total phenolic contents were employed to evaluate the total phenolics, and radical scavenging properties of defrosted carrot puree sonicated carrot puree (20 Hz, 30 min, 60 oC), and vacuum-dried carrot powder with the addition of algae. Results show that the viscosity, viscoelasticity test, TPA, and PST of the commercial sample were comparable to those of guar gum and xanthan gum containing puree, suggesting that they could be used as dysphagia diets. There was no noticeable decolourisation of the Chlorella pigment. Additionally, the use of the microwave, stemmer, and oven for defrosting treatment had an impact on the textural characteristics of the moulded samples upon cooling and also contributed to the reduction in the total phenolic and antioxidant properties of the samples. Sonication treatments of algae exposure reduced the cloudiness of the green pigment and lightened the colour of the samples containing algae, and they also reduced the drying time from 2.5 to 1.5 hours during the preliminary work. The low-temperature vacuum- and freeze-dried samples increased the concentration of the powder and resulted in an increase in the total phenolic content of the dry samples. The dried products may therefore have the potential to become more nutrient-dense to benefit the health of individuals with dysphagia.

Keywords: dysphagia, elderly, hydrocolloids, carrot puree

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Authors: Zicheng Wang

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Offshore wind energy provides a huge potential for the expansion of renewable energies to the coastal countries. High demands are required concerning the shape and type of foundations for offshore wind turbines (OWTs) to find an economically, technically and environmentally-friendly optimal solution. A promising foundation concept is the hybrid foundation system, which consists of a steel plate attached to the outer side of a hollow steel pipe pile. In this study, the bearing behavior of a large diameter foundation is analyzed using a 3-dimensional finite element (FE) model. Non-linear plastic soil behavior is considered. The results of the numerical simulations are compared to highlight the priority of the hybrid foundation to the conventional monopile foundation.

Keywords: hybrid foundation system, mechanical parameters, plastic soil behaviors, numerical simulations

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Nittaya Nokham, Nattaphon Kamon, Pipatpong Pimkhot, Pedcharada Yusuk

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Testing efficacy of a bio-product (bp) to reduce amount of arsenic was carried out in soil which were used for cultivation of organic vegetables, at Watchan Royal Project Development Center, Kulayaniwattana district, Chiang Mai. The test consists of 6 treatments e.g. Tr.1) Control: To underlie the planting pits (pp)with compost; Tr.2) Using bp: To underlie thepp with compost mixed with (+) bp at 100 g/pit; Tr.3) Using bp: To underlie the pp with compost + bp at 100 g/pit and to spray the vegetables with bp at 2 l/20 l of water, once a week; Tr.4) Using bp: To spread the compost bp on the planting area at 3 kg/1 m2 ; Tr.5) Using bp: To spread the compost + bp on the planting area at 3 kg/1 m2and to spray vegetables with bp at 2 l/20 l of water; Tr.6) Using bp: To spray vegetables with bp at 2 l/20 l of water. Result showed that after first trial of pointed cabbage cultivation, only Tr.6 had a small reduction of arsenic; while the others had higher amount of the metal. After second trial of growing red oak leaf, Tr.6 had more reduction of arsenic while Tr.5 and Tr.3 had less reduction compared to Tr.6 but more reduction than the others. In the third trial of growing mustard, very small reduction could be found on Tr.6 and Tr.5 but more reduction in Tr.3. For the fourth (last) trial with cos romaine lettuce: Tr.6, Tr.5 showed most reduction of arsenic to about half of the original amount. So, it can be concluded that this bio-product can help reducing arsenic when using this product by spraying the bp to vegetables at concentration of 2 l/20 l of water once week (Tr.6), or using the bio-product mixed with compost to spread on the planting area at 3 kg/1 m2 together with spraying the product (Tr.5). The results obtained from continuous planting 4 kinds of vegetables at the same area. The amount of arsenic found in roots and stem is very small in the 4 vegetables.

Keywords: organic vegetables, bio-product, arsenic, soil

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