Search results for: feature reduction

Authors: Maurice Ntahobari, Levin Kuhlmann, Mario Boley, Zhinoos Razavi Hesabi

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For machine learning based epileptic seizure prediction, it is important for the model to be implemented in small implantable or wearable devices that can be used to monitor epilepsy patients; however, current state-of-the-art methods are complex and computationally intensive. We use Shapley Additive Explanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while the performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.

Keywords: machine learning, seizure prediction, extra tree classifier, SHAP, epilepsy

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Authors: Fabiola M. Villalobos-Castaldi, Nicolás C. Kemper, Esther Rojas-Krugger, Laura G. Ramírez-Sánchez

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This paper presents the classification results about exploring the combination of a motion based approach with a local appearance method to describe the facial motion caused by the muscle contractions and expansions that are presented in facial expressions. The proposed feature extraction method take advantage of the knowledge related to which parts of the face reflects the highest deformations, so we selected 4 specific facial regions at which the appearance descriptor were applied. The most common used approaches for feature extraction are the holistic and the local strategies. In this work we present the results of using a local appearance approach estimating the correlation coefficient to the 4 corresponding landmark-localized facial templates of the expression face related to the neutral face. The results let us to probe how the proposed motion estimation scheme based on the local appearance correlation computation can simply and intuitively measure the motion parameters for some of the most relevant facial regions and how these parameters can be used to recognize facial expressions automatically.

Keywords: facial expression recognition system, feature extraction, local-appearance method, motion-based approach

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Authors: Fatima Ammari, Meriem Chenouf

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Synthesis of gold nano particles has attracted much attention since the pioneering discovery of the high catalytic activity of supported gold nano particles in the reaction of CO oxidation at low temperature. In this research field, we used Na-montmorillonite for gold nanoparticles stabilization; different loading percentage 1, 2 and 5%. The gold nano particles were obtained using chemical reduction method using NaBH4 as reductant agent. The obtained gold nano particles Au-mont stabilized in Na-montmorillonite were used as catalysts for reduction of 4-nitrophenol to aminophenol with sodium borohydride at room temperature. The UV-Vis results confirm directly the gold nano particles formation. The XRD and N2 adsorption results showed the formation of gold nano particles in the pores of montmorillonite with an average size of 5 nm obtained on samples with 2%Au-mont. The gold particles size increased with the increase of gold loading percentage. The reduction reaction of 4-nitrophenol into 4-aminophenol with NaBH4 catalyzed by Au-Na-montmorillonite catalyst exhibits remarkably a high activity; the reaction was completed within 9 min for 1Au-mont and within 3 min for 2Au-mont.

Keywords: chemical reduction, gold, montmorillonite, nano particles, 4-nitrophenol

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

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Authors: Sujata Kulkarni, Udhav Bhosle, Vijaykumar T.

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Due to the closeness between seismic signals and non-seismic signals, it is vital to detect earthquakes using conventional methods. In order to distinguish between seismic events and non-seismic events depending on their amplitude, our study processes the data that come from seismic sensors. The authors suggest a robust noise suppression technique that makes use of a bandpass filter, an IIR Wiener filter, recursive short-term average/long-term average (STA/LTA), and Carl short-term average (STA)/long-term average for event identification (LTA). The trigger ratio used in the proposed study to differentiate between seismic and non-seismic activity is determined. The proposed work focuses on significant feature extraction for machine learning-based seismic event detection. This serves as motivation for compiling a dataset of all features for the identification and forecasting of seismic signals. We place a focus on feature vector dimension reduction techniques due to the temporal complexity. The proposed notable features were experimentally tested using a machine learning model, and the results on unseen data are optimal. Finally, a presentation using a hybrid dataset (captured by different sensors) demonstrates how this model may also be employed in a real-time setting while lowering false alarm rates. The planned study is based on the examination of seismic signals obtained from both individual sensors and sensor networks (SN). A wideband seismic signal from BSVK and CUKG station sensors, respectively located near Basavakalyan, Karnataka, and the Central University of Karnataka, makes up the experimental dataset.

Keywords: Carl STA/LTA, features extraction, real time, dataset, machine learning, seismic detection

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Authors: Kamaljit Singh Sokhal, Gangacharyulu Dasoraju, Vijaya Kumar Bulasara

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Biopolymers are popular in many areas, like petrochemicals, food industry and agriculture due to their favorable properties like environment-friendly, availability, and cost. In this study, a biopolymer gum Arabic was used to find its effect on the pressure drop at various concentrations (100 ppm – 300 ppm) with various Reynolds numbers (10000 – 45000). A rheological study was also done by using the same concentrations to find the effect of the shear rate on the shear viscosity. Experiments were performed to find the effect of injection of gum Arabic directly near the boundary layer and to investigate its effect on the maximum possible drag reduction. Experiments were performed on a test section having i.d of 19.50 mm and length of 3045 mm. The polymer solution was injected from the top of the test section by using a peristaltic pump. The concentration of the polymer solution and the Reynolds number were used as parameters to get maximum possible drag reduction. Water was circulated through a centrifugal pump having a maximum 3000 rpm and the flow rate was measured by using rotameter. Results were validated by using Virk's maximum drag reduction asymptote. A maximum drag reduction of 62.15% was observed with the maximum concentration of gum Arabic, 300 ppm. The solution was circulated in the closed loop to find the effect of degradation of polymers with a number of cycles on the drag reduction percentage. It was observed that the injection of the polymer solution in the boundary layer was showing better results than premixed solutions.

Keywords: drag reduction, shear viscosity, gum arabic, injection point

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Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Fawaz S. Al-Anzi, Dia AbuZeina

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Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature extraction process, which parameterizes the speech signal to produce the corresponding feature vectors. Feature extraction process aims at approximating the linguistic content that is conveyed by the input speech signal. In speech processing field, there are several methods to extract speech features, however, Mel Frequency Cepstral Coefficients (MFCC) is the popular technique. It has been long observed that the MFCC is dominantly used in the well-known recognizers such as the Carnegie Mellon University (CMU) Sphinx and the Markov Model Toolkit (HTK). Hence, this paper focuses on the MFCC method as the standard choice to identify the different speech segments in order to obtain the language phonemes for further training and decoding steps. Due to MFCC good performance, the previous studies show that the MFCC dominates the Arabic ASR research. In this paper, we demonstrate MFCC as well as the intermediate steps that are performed to get these coefficients using the HTK toolkit.

Keywords: speech recognition, acoustic features, mel frequency, cepstral coefficients

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Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Kamel A. Elshorbagy, Mohamed A. Hussein, Rola S. Afify

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Natural gas pressure reduction is typically achieved using pressure reducing valves, where isenthalpic expansion takes place with considerable amount of wasted energy in an irreversible throttling process of the gas. Replacing gas-throttling process by an expansion process in a turbo expander (TE) converts the pressure of natural gas into mechanical energy transmitted to a loading device (i.e. an electric generator). This paper investigates the performance of a turboexpander system for power recovery at natural gas pressure reduction stations. There is a considerable temperature drop associated with the turboexpander process. Essential preheating is required, using gas fired boilers, to avoid undesirable effects of a low outlet temperature. Various system configurations were simulated by the general flow sheet simulator HYSYS and factors affecting the overall performance of the systems were investigated. Power outputs and fuel requirements were found using typical gas flow variation data. The simulation was performed for two case studies in which real input data are used. These case studies involve a domestic (commercial) and an industrial natural gas pressure reduction stations in Egypt. Economic studies of using the turboexpander system in both of the two natural gas pressure reduction stations are conducted using precise data obtained through communication with several companies working in this field. The results of economic analysis, for the two case studies, prove that using turboexpander systems in Egyptian natural gas reduction stations can be a successful project for energy conservation.

Keywords: natural gas, power recovery, reduction stations, turboexpander systems

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Authors: Sunil Mittal, Sukhpreet Singh, Hardeep Kaur

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A simple voltammetric technique for on-line analysis of arsenite [As (III)] is reported. Owing to the affinity of As (III) with thiol group of proteins and enzymes, cysteine has been employed as reducing agent. The reduction study of As(III)-cysteine complex on indium tin oxide (ITO) electrode has been explored. The experimental parameters such as scan rate, cysteine concentration, pH etc. were optimized to achieve As (III) determination. The developed method provided dynamic linear range of detection from 0.1 to 1 mM with a detection limit of 0.1 mM. The method is applicable to environmental monitoring of As (III) from highly contaminated sources such as industrial effluents, wastewater sludge etc.

Keywords: arsenite, cysteine, linear sweep voltammetry, reduction

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Authors: Şehri Çınar, Aydın Akın

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This study was conducted Razakı grape variety (Vitis vinifera L.) and its vine which was aged 19 was grown on 5 BB rootstock in a vegetation period of 2014 in Afyon province in Turkey. In this research, it was investigated whether the applications of Control (C), 1/3 Cluster Tip Reduction (1/3 CTR), Shoot Tip Reduction (STR), 1/3 CTR + STR, Boric Acid (BA), 1/3 CTR + BA, STR + BA, 1/3 CTR + STR + BA on yield and yield components of Razakı grape variety. The results were obtained as the highest fresh grape yield (7.74 kg/vine) with C application, as the highest cluster weight (244.62 g) with STR application, as the highest 100 berry weight (504.08 g) with C application, as the highest maturity index (36.89) with BA application, as the highest must yield (695.00 ml) with BA and (695.00 ml) with 1/3 CTR + STR + BA applications, as the highest intensity of L* color (46.93) with STR and (46.10) with 1/3 CTR + STR + BA applications, as the highest intensity of a* color (-5.37) with 1/3 CTR + STR and (-5.01) with STR, as the highest intensity of b* color (12.59) with STR application. The shoot tip reduction to increase cluster weight and boric acid application to increase maturity index of Razakı grape variety can be recommended.

Keywords: razakı, 1/3 cluster tip reduction, shoot tip reduction, boric acid, yield and yield components

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: K. L. Pan, M. B. Chang

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Nitrogen oxides (NOₓ) is regarded as one of the most important air pollutants. It not only causes adverse environmental effects but also harms human lungs and respiratory system. As a post-combustion treatment, selective catalytic reduction (SCR) possess the highest NO removal efficiency ( ≥ 85%), which is considered as the most effective technique for removing NO from gas streams. However, injection of reducing agent such as NH₃ is requested, and it is costly and may cause secondary pollution. Reduction of NO with carbon monoxide (CO) as reducing agent has been previously investigated. In this process, the key step involves the NO adsorption and dissociation. Also, the high performance mainly relies on the amounts of oxygen vacancy on catalyst surface and redox ability of catalyst, because oxygen vacancy can activate the N-O bond to promote its dissociation. Additionally, perfect redox ability can promote the adsorption of NO and oxidation of CO. Typically, noble metals such as iridium (Ir), platinum (Pt), and palladium (Pd) are used as catalyst for the reduction of NO with CO; however, high cost has limited their applications. Recently, transition metal oxides have been investigated for the reduction of NO with CO, especially CuₓOy, CoₓOy, Fe₂O₃, and MnOₓ are considered as effective catalysts. However, deactivation is inevitable as oxygen (O₂) exists in the gas streams because active sites (oxygen vacancies) of catalyst are occupied by O₂. In this study, Cu-Ce-Fe-Co is prepared and supported on activated carbon by impregnation method to form 10% wt. Cu-Ce-Fe-Co/activated carbon catalyst. Generally, addition of activated carbon on catalyst can bring several advantages: (1) NO can be effectively adsorbed by interaction between catalyst and activated carbon, resulting in the improvement of NO removal, (2) direct NO decomposition may be achieved over carbon associated with catalyst, and (3) reduction of NO could be enhanced by a reducing agent over carbon-supported catalyst. Therefore, 10% wt. Cu-Ce-Fe-Co/activated carbon may have better performance for reduction of NO with CO. Experimental results indicate that NO conversion achieved with 10% wt. Cu-Ce-Fe-Co/activated carbon reaches 83% at 150°C with 300 ppm NO and 10,000 ppm CO. As temperature is further increased to 200°C, 100% NO conversion could be achieved, implying that 10% wt. Cu-Ce-Fe-Co/activated carbon prepared has good activity for the reduction of NO with CO. In order to investigate the effect of O₂ on reduction of NO with CO, 1-5% O₂ are introduced into the system. The results indicate that NO conversions still maintain at ≥ 90% with 1-5% O₂ conditions at 200°C. It is worth noting that effect of O₂ on reduction of NO with CO could be significantly improved as carbon is used as support. It is inferred that carbon support can react with O₂ to produce CO₂ as O₂ exists in the gas streams. Overall, 10% wt. Cu-Ce-Fe-Co/activated carbon is demonstrated with good potential for reduction of NO with CO, and possible mechanisms will be elucidated in this paper.

Keywords: nitrogen oxides (NOₓ), carbon monoxide (CO), reduction of NO with CO, carbon material, catalysis

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: Nidal F. Shilbayeh, Remah W. Al-Khatib, Sameer A. Nooh

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Arabic offline handwriting recognition systems are considered as one of the most challenging topics. Arabic Handwritten Numeral Strings are used to automate systems that deal with numbers such as postal code, banking account numbers and numbers on car plates. Segmentation of connected numerals is the main bottleneck in the handwritten numeral recognition system.  This is in turn can increase the speed and efficiency of the recognition system. In this paper, we proposed algorithms for automatic segmentation and feature extraction of Arabic handwritten numeral strings based on Watershed approach. The algorithms have been designed and implemented to achieve the main goal of segmenting and extracting the string of numeral digits written by hand especially in a courtesy amount of bank checks. The segmentation algorithm partitions the string into multiple regions that can be associated with the properties of one or more criteria. The numeral extraction algorithm extracts the numeral string digits into separated individual digit. Both algorithms for segmentation and feature extraction have been tested successfully and efficiently for all types of numerals.

Keywords: handwritten numerals, segmentation, courtesy amount, feature extraction, numeral recognition

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Authors: M. A. Hasnat, M. Amirul Islam, M. A. Rashed, Jamil. Safwan, M. Mahabubul Alam

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Symmetric (Cu–Pt|Nafion|Pt–Cu) and asymmetric(Pt|Nafion|Pt–Cu) assemblies were fabricated to study the nitrate reduction processes at the cathode. The electrocatalytic nitrate reduction reactions were performed in these assemblies in order to investigate the prerequisite for the enhanced catalytic activity, electrochemical cell durability as well as preferable product selectivity resulting from the reduction of nitrate at the cathode. It has been observed for the symmetric assembly that Cu particles were oxidized on the anode surface under an applied potential and the resulting copper ions migrated to the cathode surface through the Nafion membrane, which deposited as copper oxide on the cathode surface. The formation of this copper oxide covering layer on the Pt–Cu cathode surface is attributed as the reason for the deactivation of the cathode that governed the reduced nitrate reduction along with increasing nitrite selectivity. These problems were addressed and resolved with the asymmetric design of the electrocatalytic reactor, where enhanced hydrogen evolution activates the surface by eroding the CuO over layer as well as speeding up the slow rate determining hydrogenation reactions.

Keywords: membrane, nitrate, electrocatalysis, voltammetry, electrolysis

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Mehrnaz Mostafavi

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The current research aims to explore a method for creating nano-ring structures through chemical reduction. By employing a direct reduction process at a controlled, slow pace, and concurrently introducing specific reduction agents, the goal is to fabricate these unique nano-ring formations. The deliberate slow reduction of nanoparticles within this process helps prevent spatial hindrances caused by the reduction agents. The timing of the reduction of metal atoms, facilitated by these agents, emerges as a crucial factor influencing the creation of nano-ring structures. In investigation involves a chemical approach utilizing bovine serum albumin and human serum albumin as organic reducing agents to produce gold nano-rings. The controlled reduction of metal atoms at a slow pace and under specific pH conditions plays a pivotal role in the successful fabrication of these nanostructures. Optical spectroscopic analyses revealed distinctive plasmonic behavior in both visible and infrared spectra, owing to the collective movement of electrons along the inner and outer walls of the gold nano-rings. Importantly, these ring-shaped nanoparticles exhibit customizable plasmon resonances in the near-infrared spectrum, a characteristic absent in solid particles of similar sizes. This unique attribute makes the generated samples valuable for applications in Nanomedicine and Nanobiotechnology, leveraging the distinct optical properties of these nanostructures.

Keywords: nano-ring structure, nano-particles, reductant agents, plasmon resonace

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Authors: S. Jursová, P. Pustějovská, S. Brožová, J. Bilík

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The paper deals with possibilities of interpretation of iron ore reducibility tests. It presents a mathematical model developed at Centre ENET, VŠB–Technical University of Ostrava, Czech Republic for an evaluation of metallurgical material of blast furnace feedstock such as iron ore, sinter or pellets. According to the data from the test, the model predicts its usage in blast furnace technology and its effects on production parameters of shaft aggregate. At the beginning, the paper sums up the general concept and experience in mathematical modelling of iron ore reduction. It presents basic equation for the calculation and the main parts of the developed model. In the experimental part, there is an example of usage of the mathematical model. The paper describes the usage of data for some predictive calculation. There are presented material, method of carried test of iron ore reducibility. Then there are graphically interpreted effects of used material on carbon consumption, rate of direct reduction and the whole reduction process.

Keywords: blast furnace technology, iron ore reduction, mathematical model, prediction of iron ore reduction

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Authors: Ogunwole Cecilia Oluwakemi, Timothy Ayomitunde Aderemi

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Provision of sufficient food and elimination of abject poverty have usually been the conventional benefits of agriculture in any society. Meanwhile, despite the fact that Nigeria is an agrarian society, food insecurity and poverty have become the issues of concern among both scholars and policymakers in the recent times. Against this backdrop, this study examined the nexus among agriculture, food security, and poverty reduction in Nigeria from 1990 to 2019 within the framework of the Cointegration and Granger Causality approach. Data was collected from the Central Bank of Nigeria Statistical Bulletin and the World Development Indicators, respectively. The following are the major results that emanated from the study. A long run equilibrium relationship exists among agricultural value added, food production index, and GDP per capita in Nigeria. Similarly, there is a unidirectional causality which flows from food production index to poverty reduction in Nigeria. In the same vein, one way causality flows from poverty reduction to agricultural value added in Nigeria. Consequently, this study makes the following recommendation for the policymakers in Nigeria, and other African countries by extension, that agricultural value added and food production are the important variables that cannot be undermined when poverty reduction occupies the central focus of the policymakers. Therefore, any time these policymakers want to reduce poverty, policies that drive agricultural value added and food production should be embarked upon. Therefore, this study will contribute to the literature by establishing the type of linkage that exists between agriculture, food security, and poverty reduction in Nigeria.

Keywords: agriculture, value added, food production, GDP per capita, Nigeria

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Lai Ving Kam

Abstract:

Many markets are maturing, consumer buying powers are weakening and customer preferences change rapidly. To survive, many adopt fast paced continuous cost reduction and competitive pricing to remain relevance. Marketers desire to push for more sales to increase revenues have intensified competitions at time cannibalize the product and market. The amazing technologies changes have created both hope and despair to the industries. The pressure to constantly reduce cost, on the one hand, create and market new products in cheaper prices and shorter life cycles, on the other has become a continuous endeavour. The twin trends appear irreconcilable. Can core strategic vision provides and adapts new directions in continuous cost reduction? This study investigates core strategic vision able to meet this need, for firms to survive and stay profitable. Under current uncertainty market, are firms falling back on their core strategic visions to take them out of the unfavourable positions?

Keywords: core strategy vision, continuous cost reduction, fashionable products industry, competitive pricing

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Authors: Gen Sakoda, Hideki Takayasu, Misako Takayasu

Abstract:

Waste reduction is a fundamental problem for sustainability. Methods for waste reduction with point-of-sales (POS) data are proposed, utilizing the knowledge of a recent econophysics study on a statistical property of POS data. Concretely, the non-stationary time series analysis method based on the Particle Filter is developed, which considers abnormal fluctuation scaling known as Taylor's law. This method is extended for handling incomplete sales data because of stock-outs by introducing maximum likelihood estimation for censored data. The way for optimal stock determination with pricing the cost of waste reduction is also proposed. This study focuses on the examination of the methods for large sales numbers where Taylor's law is obvious. Numerical analysis using aggregated POS data shows the effectiveness of the methods to reduce food waste maintaining a high profit for large sales numbers. Moreover, the way of pricing the cost of waste reduction reveals that a small profit loss realizes substantial waste reduction, especially in the case that the proportionality constant  of Taylor’s law is small. Specifically, around 1% profit loss realizes half disposal at =0.12, which is the actual  value of processed food items used in this research. The methods provide practical and effective solutions for waste reduction keeping a high profit, especially with large sales numbers.

Keywords: food waste reduction, particle filter, point-of-sales, sustainable development goals, Taylor's law, time series analysis

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Authors: Fatima Ammari, Meriem Chenouf

Abstract:

Synthesis of gold nanoparticles (AuNPs) has attracted much attention since the pioneering discovery of the high catalytic activity of supported gold nanoparticles in the reaction of CO oxidation at low temperature. In this research field, we used montmorillonite pre-acidified under gentle conditions for AuNPs stabilization; using different loading percentage 1, 2 and 5%. The gold nanoparticles were obtained using chemical reduction method using NaBH4 as reductant agent. The obtained gold nanoparticles stabilized in acid-activated montmorillonite were used as catalysts for reduction of 4-nitrophenol to aminophenol with sodium borohydride at room temperature The UV-Vis results confirm directly the gold nanaoparticles formation. The XRD N2 adsorption and MET results showed the formation of gold nanoparticles in the pores of preacidified montmorillonite with an average size of 5.7nm. The reduction reaction of 4-nitrophenol into 4-aminophenol with NaBH4 catalyzed by Au°-montmorillonite catalyst exhibits remarkably a high activity; the reaction was completed within 4.5min.

Keywords: gold, acid-activated montmorillonite, nanoparticles, 4-nitrophenol

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Authors: Fuqiang Wang, Jun Liu, Liyan Cai

Abstract:

This paper investigates the coordination of the conflicting two-stage low carbon supply chain consisting of upstream and downstream manufacturers. The conflict means that the upstream manufacturer takes action for carbon emissions reduction under carbon trading mechanism while the downstream manufacturer’s production cost rises. It assumes for the Stackelberg game that the upstream manufacturer plays as a leader and the downstream manufacturer does as a follower. Four kinds of the situation of decentralized decision making, centralized decision-making, the production cost sharing contract and the carbon emissions reduction revenue sharing contract under decentralized decision making are considered. The backward induction approach is adopted to solve the game. The results show that the more intense the conflict is, the lower the efficiency of carbon emissions reduction and the higher the retail price is. The optimal investment of the decentralized supply chain under the two contracts is unchanged and still lower than that of the centralized supply chain. Both the production cost sharing contract and the carbon emissions reduction revenue sharing contract cannot coordinate the supply chain, because that the sharing cost or carbon emissions reduction sharing revenue will transfer through the wholesale price mechanism. As a result, it requires more complicated contract forms to coordinate such a supply chain.

Keywords: cap-and-trade mechanism, carbon emissions reduction, conflict, supply chain coordination

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Authors: Yaşar Önal, Aydın Akın

Abstract:

This study was conducted Ismailoglu grape type (Vitis vinifera L.) and its vine which was aged 15 was grown on its own root in a vegetation period of 2013 in Nevşehir province in Turkey. In this research, it was investigated whether the applications of Control (C), 1/3 cluster tip reduction (1/3 CTR), shoot tip reduction (STR), 1/3 CTR + STR, TKI-HUMAS (TKI-HM) (Soil) (S), TKI-HM (Foliar) (F), TKI-HM (S + F), 1/3 CTR + TKI-HM (S), 1/3 CTR + TKI-HM (F), 1/3 CTR + TKI-HM (S+F), STR + TKI-HM (S), STR + TKI-HM (F), STR + TKI-HM (S + F), 1/3 CTR + STR+TKI-HM (S), 1/3 CTR + STR + TKI-HM (F), 1/3 CTR + STR + TKI-HM (S + F) on yield and yield components of Ismailoglu grape type. The results were obtained as the highest fresh grape yield (16.15 kg/vine) with TKI-HM (S), as the highest cluster weight (652.39 g) with 1/3 CTR + STR, as the highest 100 berry weight (419.07 g) with 1/3 CTR + STR + TKI-HM (F), as the highest maturity index (44.06) with 1/3 CTR, as the highest must yield (810.00 ml) with STR + TKI-HM (F), as the highest intensity of L* color (42.04) with TKI-HM (S + F), as the highest intensity of a* color (2.60) with 1/3 CTR + TKI-HM (S), as the highest intensity of b* color (7.16) with 1/3 CTR + TKI-HM (S) applications. To increase the fresh grape yield of Ismailoglu grape type can be recommended TKI-HM (S) application.

Keywords: 1/3 cluster tip reduction, shoot tip reduction, TKI-Humas application, yield and yield components

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Authors: Jiahao Cui

Abstract:

In the past centuries, due to climate change and intense whaling, the global whale population has dramatically declined. Among the various whale species, the fin whale experienced the most drastic drop in number due to its popularity in whaling. Under this background, identifying fin whale calls could be immensely beneficial to the preservation of the species. This paper uses feature extraction to process the input audio signal, then a network based on AlexNet and three networks based on the ResNet model was constructed to classify fin whale calls. A mixture of the DOSITS database and the Watkins database was used during training. The results demonstrate that a modified ResNet network has the best performance considering precision and network complexity.

Keywords: convolutional neural network, ResNet, AlexNet, fin whale preservation, feature extraction

Procedia PDF Downloads 88