Search results for: CMOS FSK receiver

Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

Procedia PDF Downloads 161

Authors: Huan Zhang, Darius K. S Chan

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Despite a large number of studies linking emotional labor and its detrimental impact for laborer, research on how emotional labor would influence the receiver is still in its infancy. Especially under the call for “people management”, supervisors inside the organization are more inclined to display happy mood to support their employees, thus endorsing emotional labor. The present study focuses on the employees in the service industry as emotional labor recipients and investigates how they respond to their supervisors’ emotional display, given their sensitivity to emotional cues. Targeted at a sample of 250 survey data from a wide range of customer service professions, this ongoing study examines how perceived supervisory emotional labor would moderate the relationship between employees surface acting and their well-being. Our major hypotheses are that employees’ surface acting predicts well-being level, and that perceived supervisory emotional labor to moderate the surface acting—outcome links. Preliminary findings have provided some support to the hypothesized model. Specifically, supervisors who are perceived to be high in surface acting are also regarded as fake and pseudo, hence the enhancing the detrimental effect of employees’ surface acting is attenuated, resulting in lower job satisfaction, higher physical stress and burnout; whereas perceived high supervisor’s deep acting, as associated with genuine and authenticity, buffers the negative impact and leads to higher job satisfaction, lower physical stress and burnout. This study first confirms the negative impacts of the surface acting on well-being for service industry employees as laborer and then extends the emotional labor studies by considering them as recipients of supervisory emotional labor. The findings provide insights for leaders by pointing out the importance of authentic emotional expression in workplace.

Keywords: perceived supervisory emotional labor, surface acting, well-being

Procedia PDF Downloads 395

Authors: Ying Han, Yuanxiang Chen, Yongtao Huang, Jia Fu, Kaile Li, Shangjing Lin, Jianguo Yu

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In long-haul and high-speed optical transmission system, the orthogonal frequency division multiplexing (OFDM) signal suffers various linear and non-linear impairments. In recent years, researchers have proposed compensation schemes for specific impairment, and the effects are remarkable. However, different impairment compensation algorithms have caused an increase in transmission delay. With the widespread application of deep neural networks (DNN) in communication, multi-impairment compensation based on DNN will be a promising scheme. In this paper, we propose and apply DNN to compensate multi-impairment of 16-QAM coherent optical OFDM signal, thereby improving the performance of the transmission system. The trained DNN models are applied in the offline digital signal processing (DSP) module of the transmission system. The models can optimize the constellation mapping signals at the transmitter and compensate multi-impairment of the OFDM decoded signal at the receiver. Furthermore, the models reduce the peak to average power ratio (PAPR) of the transmitted OFDM signal and the bit error rate (BER) of the received signal. We verify the effectiveness of the proposed scheme for 16-QAM Coherent Optical OFDM signal and demonstrate and analyze transmission performance in different transmission scenarios. The experimental results show that the PAPR and BER of the transmission system are significantly reduced after using the trained DNN. It shows that the DNN with specific loss function and network structure can optimize the transmitted signal and learn the channel feature and compensate for multi-impairment in fiber transmission effectively.

Keywords: coherent optical OFDM, deep neural network, multi-impairment compensation, optical transmission

Procedia PDF Downloads 143

Authors: Yahaya Abdullahi, Ben Coetzee, Linda Van Den Berg

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The study aimed at establishing the global positioning system (GPS) determined singles match characteristics that act as predictors of successful and less-successful male singles badminton players’ group classification. Twenty-two (22) male single players (aged: 23.39 ± 3.92 years; body stature: 177.11 ± 3.06cm; body mass: 83.46 ± 14.59kg) who represented 10 African countries participated in the study. Players were categorised as successful and less-successful players according to the results of five championships’ of the 2014/2015 season. GPS units (MinimaxX V4.0), Polar Heart Rate Transmitter Belts and digital video cameras were used to collect match data. GPS-related variables were corrected for match duration and independent t-tests, a cluster analysis and a binary forward stepwise logistic regression were calculated. A Receiver Operating Characteristic Curve (ROC) was used to determine the validity of the group classification model. High-intensity accelerations per second were identified as the only GPS-determined variable that showed a significant difference between groups. Furthermore, only high-intensity accelerations per second (p=0.03) and low-intensity efforts per second (p=0.04) were identified as significant predictors of group classification with 76.88% of players that could be classified back into their original groups by making use of the GPS-based logistic regression formula. The ROC showed a value of 0.87. The identification of the last-mentioned GPS-related variables for the attainment of badminton performances, emphasizes the importance of using badminton drills and conditioning techniques to not only improve players’ physical fitness levels but also their abilities to accelerate at high intensities.

Keywords: badminton, global positioning system, match analysis, inertial movement analysis, intensity, effort

Procedia PDF Downloads 191

Authors: V. R. Sadasivam, M. Vipin, P. Vineeth, M. Sajith, G. Sathiskumar, R. Manikandan, N. Vijayarangan

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Global positioning system technology is what leads to such things as navigation systems, GPS tracking devices, GPS surveying and GPS mapping. All that GPS does is provide a set of coordinates which represent the location of GPS units with respect to its latitude, longitude and elevation on planet Earth. It also provides time, which is accurate. The tracking devices themselves come in different flavors. They will contain a GPS receiver, and GPS software, along with some way of transmitting the resulting coordinates. GPS in mobile tend to use radio waves to transmit their location to another GPS device. The purpose of this prototype “Mobi Navi Tour for Rescue Operation” timely communication, and lightning fast decision-making with a group of people located in different places with a common goal. Timely communication and tracking the people are a critical issue in many situations, environments. Expedited can find missing person by sending the location and other related information to them through mobile. Information must be drawn from the caller and entered into the system by the administrator or a group leader and transferred to the group leader. This system will locate the closest available person, a group of people working in an organization/company or vehicle to determine availability and their position to track them. Misinformation cannot lead to the wrong decision in the rapidly paced environment in a normal and an abnormal situation. In “Mobi Navi Tour for Rescue Operation” we use Google Cloud Messaging for android (GCM) which is a service that helps developers send data from servers to their android applications on android devices. The service provides a simple, lightweight mechanism that servers can use to tell mobile applications to contact the server directly, to fetch updated application or user data.

Keywords: android, gps, tour, communication, service

Procedia PDF Downloads 396

Authors: Huseyin A. Yavasoglu, Yusuf Ziya Tengiz, Ali Göksenli

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In this study, an automatic feeding machine for different type and class of bovine animals is designed. Daily nutrition of a bovine consists of grass, corn, straw, silage, oat, wheat and different vitamins and minerals. The amount and mixture amount of each of the nutrition depends on different parameters of the bovine. These parameters are; age, sex, weight and maternity of the bovine, also outside temperature. The problem in a farm is to constitute the correct mixture and amount of nutrition for each animal. Faulty nutrition will cause an insufficient feeding of the animal concluding in an unhealthy bovine. To solve this problem, a new automatic feeding machine is designed. Travelling of the machine is performed by four tires, which is pulled by a tractor. The carrier consists of eight bins, which each of them carries a nutrition type. Capacity of each unit is 250 kg. At the bottom of each chamber is a sensor measuring the weight of the food inside. A funnel is at the bottom of each chamber by which open/close function is controlled by a valve. Each animal will carry a RFID tag including ID on its ear. A receiver on the feeding machine will read this ID and by given previous information by the operator (veterinarian), the system will detect the amount of each nutrition unit which will be given to the selected animal for feeding. In the system, each bin will open its exit gate by the help of the valve under the control of PLC (Programmable Logic Controller). The amount of each nutrition type will be controlled by measuring the open/close time. The exit canals of the bins are collected in a reservoir. To achieve a homogenous nitration, the collected feed will be mixed by a worm gear. Further the mixture will be transported by a help of a funnel to the feeding unit of the animal. The feeding process can be performed in 100 seconds. After feeding of the animal, the tractor pulls the travelling machine to the next animal. By the help of this system animals can be feeded by right amount and mixture of nutrition

Keywords: bovine, feeding, nutrition, transportation, automatic

Procedia PDF Downloads 342

Authors: Lubena Achmad, Herman Kristanto, Julian Dewantiningrum

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Background: The Bishop score is a standard method used to predict the success of induction. This examination tends to be subjective with high inter and intraobserver variability, so it was presumed to have a low predictive value in terms of the outcome of labor induction. Cervical length measurement using transvaginal ultrasound is considered to be more objective to assess the cervical length. Meanwhile, this examination is not a complicated procedure and less invasive than vaginal touché. Objective: To compare transvaginal ultrasound and Bishop score in predicting successful induction. Methods: This study was a prospective cohort study. One hundred and twenty women with singleton pregnancies undergoing induction of labor at 37 – 42 weeks and met inclusion and exclusion criteria were enrolled in this study. Cervical assessment by both transvaginal ultrasound and Bishop score were conducted prior induction. The success of labor induction was defined as an ability to achieve active phase ≤ 12 hours after induction. To figure out the best cut-off point of cervical length and Bishop score, receiver operating characteristic (ROC) curves were plotted. Logistic regression analysis was used to determine which factors best-predicted induction success. Results: This study showed significant differences in terms of age, premature rupture of the membrane, the Bishop score, cervical length and funneling as significant predictors of successful induction. Using ROC curves found that the best cut-off point for prediction of successful induction was 25.45 mm for cervical length and 3 for Bishop score. Logistic regression was performed and showed only premature rupture of membranes and cervical length ≤ 25.45 that significantly predicted the success of labor induction. By excluding premature rupture of the membrane as the indication of induction, cervical length less than 25.3 mm was a better predictor of successful induction. Conclusion: Compared to Bishop score, cervical length using transvaginal ultrasound was a better predictor of successful induction.

Keywords: Bishop Score, cervical length, induction, successful induction, transvaginal sonography

Procedia PDF Downloads 325

Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

Procedia PDF Downloads 92

Authors: Revaz Chigladze

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The purpose of the research is to investigate the surfaces of Jupiter's Galilean moons, namely which moon has the most uniform surface among them, what is the difference between the front (in the direction of motion) and the back sides of each moon's surface, as well as the temporal variations of the moons. Since 1981, the E. Kharadze National Astrophysical Observatory of Georgia has been conducting polarimetric (P) and photometric (M) observations of Jupiter's Galilean moons with telescopes of different diameters (40 cm and 125 cm) and the polarimeter ASEP-78 in combination with them and the latest generation photometer with a polarimeter and modern light receiver SBIG. As it turns out from the analysis of the observed material, the parameters P and M depend on α-the phase angle of the moon (satellite), L- the orbital latitude of the moon (satellite), λ- the wavelength, and t - the period of observation, i.e., P = P (α, L, λ , t), and similarly M = M (α, L, λ. , t). Based on the analysis of the observed material, the following was studied: Jupiter's Galilean moons: dependence of the magnitude and phase angle of the degree of linear polarization for different wavelengths; Dependence of the degree of polarization and the orbital longitude; dependence between the magnitude of the degree of polarization and the wavelength; time dependence of the degree of polarization and the dependence between photometric and polarimetric characteristics (including establishing correlation). From the analysis of the obtained results, we get: The magnitude of the degree of polarization of Jupiter's Galilean moons near the opposition significantly differs from zero. Europa appears to have the most uniform surface, and Callisto the least uniform. Time variations are most characteristic of Io, which confirms the presence of volcanic activity on its surface. Based on the observed material, it can be seen that the intensity of light reflected from the front hemisphere of the first three moons: Io, Europa, and Ganymede, is less than the intensity of light reflected from the rear hemisphere, and in the case of the Callisto it is the opposite. The paper provides a convincing (natural, real) explanation of this fact.

Keywords: Galilean moons, polarization, degree of polarization, photometry, front and rear hemispheres

Procedia PDF Downloads 101

Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

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Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

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Authors: Najla Alhkalaf

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The focus of this study is answering the following question: How do e-government services in Bahrain support the productivity of SMEs? This study examines the current E-government function in enhancing SME productivity in Bahrain through analysing the efficiency of e- government by viewing its facilitators and barriers from the perspective of different stakeholders. The study aims to identify and develop best practice guidelines with the end-goal of creating a standardised channel of communication between e-government and SMEs that fulfil the requirement of SME owners, and thus achieve the prime objective of e-government. E-government services for SMEs have been offered in Bahrain since 2005. However, the current services lack the required mechanism for SMEs to fully take advantage of these services because of lagging communication between service provider and end-user. E-government employees believe that a lack of awareness and trust are the main stumbling block, whereas the SME owners believe that there is a lack of sufficiency in the content and efficiency provided through e- services. A questionnaire has been created based on a pilot study that highlighted the main indicators of e-government efficiency and SMEs productivity as well as previous studies conducted on this subject. This allowed for quantitative data to be extracted. Also interviews were conducted with SME owners and government employees from both case studies, which formed the qualitative data for this study. The findings portray that both the service provider and service receiver largely agree on the existence of most of the technical and administrative barriers. However, the data reflects a level of dissatisfaction from the SME side, which contradicts with the perceived level of satisfaction from the government employees. Therefore, the data supports the argument that assures the existence of a communication gap between stakeholders. To this effect, this research would help build channels of communication between stakeholders, and then induces a plan unlocking the potential of e-government application. The conclusions of this study will help devise an optimised E-government strategy for Bahrain.

Keywords: e-government, SME, e-services, G2B, government employees' perspective, entrepreneurs' perspective, enterprise

Procedia PDF Downloads 232

Authors: Shirin Ghatrehsamani

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Higher efficiency and lower environmental impact are the consequence of using advanced technology in farming. They also help to decrease yield variability by diminishing weather variability impact, optimizing nutrient and pest management as well as reducing competition from weeds. A better understanding of the pros and cons of applying technology and finding the main reason for preventing the utilization of the technology has a significant impact on developing technology adoption among farmers and producers in the digital agriculture era. The results from two surveys carried out in 2019 and 2021 were used to investigate whether the crop types had an impact on the willingness to utilize technology on the farms. The main focus of the questionnaire was on utilizing precision agriculture (PA) technologies among farmers in some parts of the united states. Collected data was analyzed to determine the practical application of various technologies. The survey results showed more similarities in the main reason not to use PA between the two crop types, but the present application of using technology in specialty crops is generally five times larger than in row crops. GPS receiver applications were reported similar for both types of crops. Lack of knowledge and high cost of data handling were cited as the main problems. The most significant difference was among using variable rate technology, which was 43% for specialty crops while was reported 0% for row crops. Pest scouting and mapping were commonly used for specialty crops, while they were rarely applied for row crops. Survey respondents found yield mapping, soil sampling map, and irrigation scheduling were more valuable for specialty crops than row crops in management decisions. About 50% of the respondents would like to share the PA data in both types of crops. Almost 50 % of respondents got their PA information from retailers in both categories, and as the second source, using extension agents were more common in specialty crops than row crops.

Keywords: precision agriculture, smart farming, digital agriculture, technology adoption

Procedia PDF Downloads 114

Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: Shasha Lv, Zhengcao Li

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Due to the CMOS compatibility, silicon-based field emission (FE) devices as potential electron sources have attracted much attention. The geometrical arrangement and dimensional features of aligned silicon nanowires (SiNWs) have a determining influence on the FE properties. We discuss a multistep template replication process of Ag-assisted chemical etching combined with polystyrene (PS) spheres to fabricate highly periodic and well-aligned silicon nanowires, then their diameter, aspect ratio and density were further controlled via dry oxidation and post chemical treatment. The FE properties related to proximity and aspect ratio were systematically studied. A remarkable improvement of FE propertiy was observed with the average nanowires tip interspace increasing from 80 to 820 nm. On the basis of adjusting SiNWs dimensions and morphology, addition of a secondary material whose properties complement the SiNWs could yield a combined characteristic. Three different nanoheterostructures were fabricated to control the FE performance, they are: NiSi/Si core-shell structures, ZnO/Si nanotrees, and Graphene/SiNWs. We successfully fabricated the high-quality NiSi/Si heterostructured nanowires with excellent conformality. First, nickle nanoparticles were deposited onto SiNWs, then rapid thermal annealing process were utilized to form NiSi shell. In addition, we demonstrate a new and simple method for creating 3D nanotree-like ZnO/Si nanocomposites with a spatially branched hierarchical structure. Compared with the as-prepared SiNRs and ZnO NWs, the high-density ZnO NWs on SiNRs have exhibited predominant FE characteristics, and the FE enhancement factors were attributed to band bending effect and geometrical morphology. The FE efficiency from flat sheet structure of graphene is low. We discussed an effective approach towards full control over the diameter of uniform SiNWs to adjust the protrusions of large-scale graphene sheet deposited on SiNWs. The FE performance regarding the uniformity and dimensional control of graphene protrusions supported on SiNWs was systematically clarified. Therefore, the hybrid SiNWs/graphene structures with protrusions provide a promising class of field emission cathodes.

Keywords: field emission, silicon nanowires, heterostructures, controllable synthesis

Procedia PDF Downloads 273

Authors: Changaiz Khan

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Pakistan has the highest rates of maternal mortality in South Asia. From the year 2000 to 2017 the global rate of maternal mortality has decreased up to 39 %. In the context of South Asia, it has decreased by 59% since 2000s. Pakistan has also reduced the rate of maternal mortality, but there is a difference on the provincial level. According to the report of the National Institute of Population Studies (NIPS) conducted in 2020, the MMR in Balochistan has crossed the ratio of most of the South Asian countries, i.e., 298 maternal deaths per 100,000 live births. In comparison, the province of Punjab has the lowest maternal mortality rate i.e. 157 deaths (per 100,000 live births). The rate of maternal mortality is much higher in Balochistan as compared to the other provinces. This research is aimed to discuss the supply side barriers and utilization of maternal healthcare services in the District Gwadar. Likert scale survey method has been used to collect data from the Healthcare Professionals from hospitals -private and government- and the maternal healthcare receiver, that is patient. Semi-structured interviews of healthcare professionals such as doctors, nurses, and Lab technicians have also been conducted. It has been found in this research study that the hospitals in Gwadar district are lagging behind in providing modern maternal healthcare to women due to the lack of staff training, medicine supply, and Laboratories. Moreover, the system of the lady health worker is also not catering to the needs of the women in District Gwadar. It has been recommended in the study that first of all the government should fulfill the supply of the medicine in the hospital. Secondly, the government should open laboratories in the hospitals. Thirdly, the government should increase the funding of the government hospital and the allocation of lady health workers in District Gwadar, Balochistan should be increased.

Keywords: maternal mortality, neonatal, postnatal, supply barriers, patients, healthcare professionals, laboratory, medical supply, training

Procedia PDF Downloads 55

Authors: Abhaydeep Pandey, Shweta Shukla, Saptamita Goswami, Bhaswati Bandyopadhyay, Vishnampettai Ramachandran, Sudhanshu Vrati, Arup Banerjee

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Background: Long non-coding RNAs (lncRNAs) are the important regulators of gene expression and play important role in viral replication and disease progression. The role of lncRNA genes in the pathogenesis of Dengue virus-mediated pathogenesis is currently unknown. Methods: To gain additional insights, we utilized an unbiased RNA sequencing followed by in silico analysis approach to identify the differentially expressed lncRNA and genes that are associated with dengue disease progression. Further, we focused our study on lncRNAs NEAT1 (Nuclear Paraspeckle Assembly Transcript 1) as it was found to be differentially expressed in PBMC of dengue infected patients. Results: The expression of lncRNAs NEAT1, as compared to dengue infection (DI), was significantly down-regulated as the patients developed the complication. Moreover, pairwise analysis on follow up patients confirmed that suppression of NEAT1 expression was associated with rapid fall in platelet count in dengue infected patients. Severe dengue patients (DS) (n=18; platelet count < 20K) when recovered from infection showing high NEAT1 expression as it observed in healthy donors. By co-expression network analysis and subsequent validation, we revealed that coding gene; IFI27 expression was significantly up-regulated in severe dengue cases and negatively correlated with NEAT1 expression. To discriminate DI from dengue severe, receiver operating characteristic (ROC) curve was calculated. It revealed sensitivity and specificity of 100% (95%CI: 85.69 – 97.22) and area under the curve (AUC) = 0.97 for NEAT1. Conclusions: Altogether, our first observations demonstrate that monitoring NEAT1and IFI27 expression in dengue patients could be useful in understanding dengue virus-induced disease progression and may be involved in pathophysiological processes.

Keywords: dengue, lncRNA, NEAT1, transcriptome

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Authors: Babawale Akin Adeyemi, Esan Temitayo, Adeyemi Olabisi Omowumi

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The paper analyzed waste disposal system in Akure, Ondo State using GIS techniques. Specifically, the study identified the spatial distribution of collection points and existing dumpsite; evaluated the accessibility of waste collection points and their proximity to each other with the view of enhancing better performance of the waste disposal system. Data for the study were obtained from both primary and secondary sources. Primary data were obtained through the administration of questionnaire. From field survey, 35 collection points were identified in the study area. 10 questionnaires were administered around each collection point making a total of 350 questionnaires for the study. Also, co-ordinates of each collection point were captured using a hand-held Global Positioning System (GPS) receiver which was used to analyze the spatial distribution of collection points. Secondary data used include administrative map collected from Akure South Local Government Secretariat. Data collected was analyzed using the GIS analytical tools which is neighborhood function. The result revealed that collection points were found in all parts of Akure with the highest concentration around the central business district. The study also showed that 80% of the collection points enjoyed efficient waste service while the remaining 20% does not. The study further revealed that most collection points in the core of the city were in close proximity to each other. In conclusion, the paper revealed the capability of Geographic Information System (GIS) as a technique in management of waste collection and disposal technique. The application of Geographic Information System (GIS) in the evaluation of the solid waste management in Akure is highly invaluable for the state waste management board which could also be beneficial to other states in developing a modern day solid waste management system. Further study on solid waste management is also recommended especially for updating of information on both spatial and non-spatial data.

Keywords: assessment, geospatial, system, waste disposal

Procedia PDF Downloads 239

Authors: Yanhua Zhao, Yu Gou, Shu Feng, Dongdong Li, Chuanmin Tao

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The natural history of HBV infection could experience immune tolerant (IT), immune clearance (IC), HBeAg-negative inactive/quienscent carrier (ENQ), and HBeAg-negative hepatitis (ENH). As current biomarkers for discriminating these four phases have some weaknesses, additional serological indicators are needed. Hepatits B core-related antigen (HBcrAg) encoded with precore/core gene contains denatured HBeAg, HBV core antigen (HBcAg) and a 22KDa precore protein (p22cr), which was demonstrated to have a close association with natural history of hepatitis B infection, but no specific cutoff values and diagnostic parameters to evaluate the diagnostic efficacy. This study aimed to clarify the distribution of HBcrAg levels and evaluate its diagnostic performance during the natural history of infection from a Western Chinese perspective. 294 samples collected from treatment-naïve chronic hepatitis B (CHB) patients in different phases (IT=64; IC=72; ENQ=100, and ENH=58). We detected the HBcrAg values and analyzed the relationship between HBcrAg and HBV DNA. HBsAg and other clinical parameters were quantitatively tested. HBcrAg levels of four phases were 9.30 log U/mL, 8.80 log U/mL, 3.00 log U/mL, and 5.10 logU/mL, respectively (p < 0.0001). Receiver operating characteristic curve analysis demonstrated that the area under curves (AUCs) of HBcrAg and quantitative HBsAg at cutoff values of 9.25 log U/mL and 4.355 log IU/mL for distinguishing IT from IC phases were 0.704 and 0.694, with sensitivity 76.39% and 59.72%, specificity 53.13% and 79.69%, respectively. AUCs of HBcrAg and quantitative HBsAg at cutoff values of 4.15 log U/mlmL and 2.395 log IU/mlmL for discriminating between ENQ and ENH phases were 0.931 and 0.653, with sensitivity 87.93% and 84%, specificity 91.38% and 39%, respectively. Therefore, HBcrAg levels varied significantly among four natural phases of HBV infection. It had higher predictive performance than quantitative HBsAg for distinguishing between ENQ-patients and ENH-patients and similar performance with HBsAg for the discrimination between IT and IC phases, which indicated that HBcrAg could be a potential serological marker for CHB.

Keywords: chronic hepatitis B, hepatitis B core-related antigen, hepatitis B surface antigens, hepatitis B virus

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Authors: Lixin Tian, Wei Xue

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Many jobs in society go underground, such as mine mining, tunnel construction and subways, which are vital to the development of society. Once accidents occur in these places, the interruption of traditional wired communication is not conducive to the development of rescue work. In order to realize the positioning, early warning and command functions of underground personnel and improve rescue efficiency, it is necessary to develop and design an emergency ground communication system. It is easy to be subjected to narrowband interference when performing conventional underground communication. Spreading communication can be used for this problem. However, general spread spectrum methods such as direct spread communication are inefficient, so it is proposed to use parallel combined spread spectrum (PCSS) communication to improve efficiency. The PCSS communication not only has the anti-interference ability and the good concealment of the traditional spread spectrum system, but also has a relatively high frequency band utilization rate and a strong information transmission capability. So, this technology has been widely used in practice. This paper presents a PCSS communication model-multiple detection parallel combined spread spectrum (MDPCSS) communication system. In this paper, the principle of MDPCSS communication system is described, that is, the sequence at the transmitting end is processed in blocks and cyclically shifted to facilitate multiple detection at the receiving end. The block diagrams of the transmitter and receiver of the MDPCSS communication system are introduced. At the same time, the calculation formula of the system bit error rate (BER) is introduced, and the simulation and analysis of the BER of the system are completed. By comparing with the common parallel PCSS communication, we can draw a conclusion that it is indeed possible to reduce the BER and improve the system performance. Furthermore, the influence of different pseudo-code lengths selected on the system BER is simulated and analyzed, and the conclusion is that the larger the pseudo-code length is, the smaller the system error rate is.

Keywords: cyclic shift, multiple detection, parallel combined spread spectrum, PN code

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Authors: Shangqing Song, Bin Xu, Yajun Cheng, Zhong Wang

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miRNAs derived from exosomes exist in a body fluid such as urine were regarded as potential biomarkers for various human cancers diagnosis and prognosis, as mature miRNAs can be steadily preserved by exosomes. However, its potential value in clear cell renal cell carcinoma (ccRCC) diagnosis and prognosis remains unclear. In the present study, differentially expressed miRNAs from urinal exosomes were identified by next-generation sequencing (NGS) technology. The 16 differentially expressed miRNAs were identified between ccRCC patients and healthy donors. To explore the specific diagnosis biomarker of ccRCC, we validated these urinary exosomes from 70 early-stage renal cancer patients, 30 healthy people and other urinary system cancers, including 30 early-stage prostate cancer patients and 30 early-stage bladder cancer patients by qRT-PCR. The results showed that urinary exosome miR-30c-5p could be stably amplified and meanwhile the expression of miR-30c-5p has no significant difference between other urinary system cancers and healthy control, however, expression level of miR-30c-5p in urinary exosomal of ccRCC patients was lower than healthy people and receiver operation characterization (ROC) curve showed that the area under the curve (AUC) values was 0.8192 (95% confidence interval was 0.7388-0.8996, P= 0.0000). In addition, up-regulating miR-30c-5p expression could inhibit renal cell carcinoma cells growth. Lastly, HSP5A was found as a direct target gene of miR-30c-5p. HSP5A depletion reversed the promoting effect of ccRCC growth casued by miR-30c-5p inhibitor, respectively. In conclusion, this study demonstrated that urinary exosomal miR-30c-5p is readily accessible as diagnosis biomarker of early-stage ccRCC, and miR-30c-5p might modulate the expression of HSPA5, which correlated with the progression of ccRCC.

Keywords: clear cell renal cell carcinoma, exosome, HSP5A, miR-30c-5p

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Authors: ZhenlinJu, Christopher P. Vellano, RehanAkbani, Yiling Lu, Gordon B. Mills

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The epithelial–mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal characteristics, such as profound disruption of cell-cell junctions, loss of apical-basolateral polarity, and extensive reorganization of the actin cytoskeleton to induce cell motility and invasion. A hallmark of EMT is its capacity to promote metastasis, which is due in part to activation of several transcription factors and subsequent downregulation of E-cadherin. Unfortunately, current approaches have yet to uncover robust protein marker sets that can classify tumors as possessing strong EMT signatures. In this study, we utilize reverse phase protein array (RPPA) data and consensus clustering methods to successfully classify a subset of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) tumors into an EMT protein signaling group (EMT group). The overall survival (OS) of patients in the EMT group is significantly worse than those in the other Hormone and PI3K/AKT signaling groups. In addition to a shrinkage and selection method for linear regression (LASSO), we applied training/test set and Monte Carlo resampling approaches to identify a set of protein markers that predicts the EMT status of CESC tumors. We fit a logistic model to these protein markers and developed a classifier, which was fixed in the training set and validated in the testing set. The classifier robustly predicted the EMT status of the testing set with an area under the curve (AUC) of 0.975 by Receiver Operating Characteristic (ROC) analysis. This method not only identifies a core set of proteins underlying an EMT signature in cervical cancer patients, but also provides a tool to examine protein predictors that drive molecular subtypes in other diseases.

Keywords: consensus clustering, TCGA CESC, Silhouette, Monte Carlo LASSO

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Authors: Tae-Hoon Kim, Kwon-Ha Yoon, Hong Young Jun, Ki-Jong Kim, Young Hwan Lee, Myeung Su Lee, Keum Ha Choi, Ki Jung Yun, Eun Young Cho, Yong-Yeon Jeong, Chung-Hwan Jun

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Purpose: To assess the changes of hepatic metabolite for differentiation between non-alcoholic steatohepatitis (NASH) and simple steatosis on proton magnetic resonance spectroscopy (1H-MRS) in both humans and animal model. Methods: The local institutional review board approved this study and subjects gave written informed consent. 1H-MRS measurements were performed on a localized voxel of the liver using a point-resolved spectroscopy (PRESS) sequence and hepatic metabolites of alanine (Ala), lactate/triglyceride (Lac/TG), and TG were analyzed in NASH, simple steatosis and control groups. The group difference was tested with the ANOVA and Tukey’s post-hoc tests, and diagnostic accuracy was tested by calculating the area under the receiver operating characteristics (ROC) curve. The associations between metabolic concentration and pathologic grades or non-alcoholic fatty liver disease(NAFLD) activity scores were assessed by the Pearson’s correlation. Results: Patient with NASH showed the elevated Ala(p<0.001), Lac/TG(p < 0.001), TG(p < 0.05) concentration when compared with patients who had simple steatosis and healthy controls. The NASH patients were higher levels in Ala(mean±SEM, 52.5±8.3 vs 2.0±0.9; p < 0.001), Lac/TG(824.0±168.2 vs 394.1±89.8; p < 0.05) than simple steatosis. The area under the ROC curve to distinguish NASH from simple steatosis was 1.00 (95% confidence interval; 1.00, 1.00) with Ala and 0.782 (95% confidence interval; 0.61, 0.96) with Lac/TG. The Ala and Lac/TG levels were well correlated with steatosis grade, lobular inflammation, and NAFLD activity scores. The metabolic changes in human were reproducible to a mice model induced by streptozotocin injection and a high-fat diet. Conclusion: 1H-MRS would be useful for differentiation of patients with NASH and simple hepatic steatosis.

Keywords: non-alcoholic fatty liver disease, non-alcoholic steatohepatitis, 1H MR spectroscopy, hepatic metabolites

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Sathyaseelan Subramaniam, Jacqueline Bober, Jennifer Chao, Shahriar Zehtabchi

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Background: Emergency physicians frequently evaluate skin and soft tissue infections in order to differentiate abscess from cellulitis. This helps determine which patients will benefit from incision and drainage. Our objective was to determine the operating characteristics of point-of-care ultrasound (POCUS) compared to clinical examination in identifying abscesses in emergency department (ED) patients with features of skin and soft tissue infections. Methods: We performed a comprehensive search in the following databases: Medline, Web of Science, EMBASE, CINAHL and Cochrane Library. Trials were included if they compared the operating characteristics of POCUS with clinical examination in identifying skin and soft tissue abscesses. Trials that included patients with oropharyngeal abscesses or that requiring abscess drainage in the operating room were excluded. The presence of an abscess was determined by pus drainage. No pus seen on incision or resolution of symptoms without pus drainage at follow up, determined the absence of an abscess. Quality of included trials was assessed using GRADE criteria. Operating characteristics of POCUS are reported as sensitivity, specificity, positive likelihood (LR+) and negative likelihood (LR-) ratios and the respective 95% confidence intervals (CI). Summary measures were calculated by generating a hierarchical summary receiver operating characteristic model (HSROC). Results: Out of 3203 references identified, 5 observational studies with 615 patients in aggregate were included (2 adults and 3 pediatrics). We rated the quality of 3 trials as low and 2 as very low. The operating characteristics of POCUS and clinical examination in identifying soft tissue abscesses are presented in the table. The HSROC for POCUS revealed a sensitivity of 96% (95% CI = 89-98%), specificity of 79% (95% CI = 71-86), LR+ of 4.6 (95% CI = 3.2-6.8), and LR- of 0.06 (95% CI = 0.02-0.2). Conclusion: Existing evidence indicates that POCUS is useful in identifying abscesses in ED patients with skin or soft tissue infections.

Keywords: abscess, point-of-care ultrasound, pocus, skin and soft tissue infection

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Authors: Havva Nur Tarakci, Bahar Urhan Torun

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The transformation that is seen in every area of life by technology, especially internet technology changes the structure of political communications too. Internet, which is at the top of new communication technologies, affects political communications with its structure in a way that no traditional communication tools ever have and enables interaction and the channel between receiver and sender, and it becomes one of the most effective tools preferred among the political communication applications. This state as a result of technological convergence makes Internet an unobtainable place for political communication campaigns. Political communications, which means every kind of communication strategies that political parties called 'actors of political communications' use with the aim of messaging their opinions and party programmes to their present and potential voters who are a target group for them, is a type of communication that is frequently used also among social media tools at the present day. The electorate consisting of different structures is informed, directed, and managed by social media tools. Political parties easily reach their electorate by these tools without any limitations of both time and place and also are able to take the opinions and reactions of their electorate by the element of interaction that is a feature of social media. In this context, Facebook, which is a place that political parties use in social media at most, is a communication network including in our daily life since 2004. As it is one of the most popular social networks today, it is among the most-visited websites in the global scale. In this way, the research is based on the question, “How do the political parties use Facebook at the campaigns, which they conduct during the election periods, for informing their voters?” and it aims at clarifying the Facebook using practices of the political parties. In direction of this objective the official Facebook accounts of the four political parties (JDP–AKParti, PDP–BDP, RPP-CHP, NMP-MHP), which reach their voters by social media besides other communication tools, are treated, and a frame for the politics of Turkey is formed. The time of examination is constricted with totally two weeks, one week before the mayoral elections and one week after the mayoral elections, when it is supposed that the political parties use their Facebook accounts in full swing. As a research method, the method of content analysis is preferred, and the texts and the visual elements that are gotten are interpreted based on this analysis.

Keywords: Facebook, political communications, social media, electrorate

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Authors: Ariba Siddiqui, Amber Khan

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The need for bio-implantable devices in the field of medical sciences has been increasing day by day; however, the charging of these devices is a major issue. Batteries, a very common method of powering the implants, have a limited lifetime and bulky nature. Therefore, as a replacement of batteries, acoustic power transfer (APT) technology is being accepted as the most suitable technique to wirelessly power the medical implants in the present scenario. The basic model of APT consists of piezoelectric transducers that work on the principle of converse piezoelectric effect at the transmitting end and direct piezoelectric effect at the receiving end. This paper provides mechanistic insight into the parameters affecting the design and efficient working of acoustic power transfer systems. The optimum design considerations have been presented that will help to compress the size of the device and augment the intensity of the pressure wave. A COMSOL model of the PZT (Lead Zirconate Titanate) transducer was developed. The model was simulated and analyzed on a frequency spectrum. The simulation results displayed that the efficiency of these devices is strongly dependent on the frequency of operation, and a wrong choice of the operating frequency leads to the high absorption of acoustic field inside the tissue (medium), poor power strength, and heavy transducers, which in effect influence the overall configuration of the acoustic systems. Considering all the tradeoffs, the simulations were performed again by determining an optimum frequency (900 kHz) that resulted in the reduction of the transducer's thickness to 1.96 mm and augmented the power strength with an intensity of 432 W/m². Thus, the results obtained after the second simulation contribute to lesser attenuation, lightweight systems, high power intensity, and also comply with safety limits provided by the U.S Food and Drug Administration (FDA). It was also found that the chosen operating frequency enhances the directivity of the acoustic wave at the receiver side.

Keywords: acoustic power, bio-implantable, COMSOL, Lead Zirconate Titanate, piezoelectric, transducer

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Authors: Said Benaissa, David Plets, Emmeric Tanghe, Jens Trogh, Luc Martens, Leen Vandaele, Annelies Van Nuffel, Frank A. M. Tuyttens, Bart Sonck, Wout Joseph

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The herd monitoring and managing - in particular the detection of ‘attention animals’ that require care, treatment or assistance is crucial for effective reproduction status, health, and overall well-being of dairy cows. In large sized farms, traditional methods based on direct observation or analysis of video recordings become labour-intensive and time-consuming. Thus, automatic monitoring systems using sensors have become increasingly important to continuously and accurately track the health status of dairy cows. Wireless sensor networks (WSNs) and internet-of-things (IoT) can be effectively used in health tracking of dairy cows to facilitate herd management and enhance the cow welfare. Since on-cow measuring devices are energy-constrained, a proper characterization of the off-body wireless channel between the on-cow sensor nodes and the back-end base station is required for a power-optimized deployment of these networks in barns. The aim of this study was to characterize the off-body wireless channel in indoor (barns) environment at 868 MHz using LoRa nodes. LoRa is an emerging wireless technology mainly targeted at WSNs and IoT networks. Both large scale fading (i.e., path loss) and temporal fading were investigated. The obtained path loss values as a function of the transmitter-receiver separation were well fitted by a lognormal path loss model. The path loss showed an additional increase of 4 dB when the wireless node was actually worn by the cow. The temporal fading due to movement of other cows was well described by Rician distributions with a K-factor of 8.5 dB. Based on this characterization, network planning and energy consumption optimization of the on-body wireless nodes could be performed, which enables the deployment of reliable dairy cow monitoring systems.

Keywords: channel, channel modelling, cow monitoring, dairy cows, health monitoring, IoT, LoRa, off-body propagation, PLF, propagation

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Authors: Lan Luo, Jingjing Huang, Huafang Li

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Introduction: Distressing low sexual desire is common in China, while the lack of reliable and valid instruments to evaluate symptoms of hypoactive sexual desire disorder (HSDD) impedes related research and clinical services. Aim: This study aimed to validate the reliability and validity of the Female Sexual Function Index (FSFI) and the Female Sexual Distress Scale-Desire/Arousal/Orgasm (FSDS-DAO) in Chinese female HSDD patients. Methods: We administered FSFI and FSDS-DAO in a convenient sample of Chinese adult women. Participants were diagnosed by a psychiatrist according to the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, Text Revision (DSM-IV-TR). Results: We had a valid analysis sample of 279 Chinese women, of which 107 were HSDD patients. The Cronbach's α of FSFI and FSDS-DAO were 0.947 and 0.956, respectively, and the intraclass correlation coefficients of which were 0.86 and 0.89, respectively (the interval was 13-15 days). The correlation coefficient between the Revised Adult Attachment Scale (RAAS) and FSFI (or FSDS-DAO) did not exceed 0.4; the area under the receiver operating characteristic (ROC) curve was 0. 83 when combined FSFI-d (the desire domain of FSFI) and FSDS-DAO to diagnose HSDD, which was significantly different from that of using these scales individually. FSFI-d of less than 2.7 (1.2-6) and FSDS-DAO of no less than 15 (0-60) (Sensitivity 65%, Specificity 83%), or FSFI-d of no more than 3.0 (1.2-6) and FSDS-DAO of no less than 14 (0-60) (Sensitivity 74%, Specificity 77%) can be used as cutoff scores in clinical research or outpatient screening. Clinical implications: FSFI (including FSFI-d) and FSDS-DAO are suitable for the screening and evaluation of Chinese female HSDD patients of childbearing age. Strengths and limitations: Strengths include a thorough validation of FSFI and FSDS-DAO and the exploration of the cutoff score combing FSFI-d and FSDS-DAO. Limitations include a small convenience sample and the requirement of being sexually active for HSDD patients. Conclusion: FSFI (including FSFI-d) and FSDS-DAO have good internal consistency, test-retest reliability, construct validity, and criterion validity in Chinese female HSDD patients of childbearing age.

Keywords: sexual desire, sexual distress, hypoactive sexual desire disorder, scale

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Authors: Bahram Besharti, Hossein Navid, Hadi Karimi, Hossein Behfar, Iraj Eskandari

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Multiple or miss sowing by seed drills is a common problem on the farm. This problem causes overuse of seeds, wasting energy, rising crop treatment cost and reducing crop yield in harvesting. To be informed of mentioned faults and monitoring the performance of seed drills during sowing, developing a seed sensor for detecting seed mass flow rate and monitoring in a delivery tube is essential. In this research, an infrared seed sensor was developed to estimate seed mass flow rate in seed drills. The developed sensor comprised of a pair of spaced apart circuits one acting as an IR transmitter and the other acting as an IR receiver. Optical coverage in the sensing section was obtained by setting IR LEDs and photo-diodes directly on opposite sides. Passing seeds made interruption in radiation beams to the photo-diode which caused output voltages to change. The voltage difference of sensing units summed by a microcontroller and were converted to an analog value by DAC chip. The sensor was tested by using a roller seed metering device with three types of seeds consist of chickpea, wheat, and alfalfa (representing large, medium and fine seed, respectively). The results revealed a good fitting between voltage received from seed sensor and mass flow of seeds in the delivery tube. A linear trend line was set for three seeds collected data as a model of the mass flow of seeds. A final mass flow model was developed for various size seeds based on receiving voltages from the seed sensor, thousand seed weight and equivalent diameter of seeds. The developed infrared seed sensor, besides monitoring mass flow of seeds in field operations, can be used for the assessment of mechanical planter seed metering unit performance in the laboratory and provide an easy calibrating method for seed drills before planting in the field.

Keywords: seed flow, infrared, seed sensor, seed drills

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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