Search results for: physiological data extraction

Authors: Uzma Ayaz, Sanam Bashir, Shahid Iqbal Awan, Muhammad Ilyas, Muhammad Fareed Khan

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Drought stress directly affects growth along with the productivity of plants by altering plant water status. Sunflower (Helianthus annuus L.), an oilseed crop, is adversely affected by abiotic stresses. The present study was carried out to characterize the genetic variability for seedling and morpho-physiological parameters in different sunflower genotypes under water-stressed conditions. A total of twenty-seven genotypes, including two hybrids, eight advanced lines and seventeen accessions of sunflower (Helianthus annuus L.) were tested against drought stress at Seedling stages by Polyethylene glycol (PEG). Significant means were calculated among traits using analysis of variance (ANOVA) whereas, correlation and principal component analysis also confirmed that germination percentage, root length, shoot length, chlorophyll content, stomatal frequency are positively linked with each other hence, these traits were responsible for most of the variation among genotypes. The cluster analysis results showed that genotypes Ausun, line-3, line-2, and 17578, line-1, line-7, line-6 and 17562 as more diverse among all the genotypes. These most divergent genotypes could be utilized in the development of drought-tolerant inbreed lines which could be subsequently used in future heterosis breeding programs.

Keywords: sunflower, drought, stress, polyethylene- glycol, screening

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Authors: Ahmed Rashad Harb Riad Ismail

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The purpose of this research is to study the potential of Dynamic Pricing if deployed by mobile operators and analyse its effects from both operators and consumers side. Furthermore, to conclude, throughout the research study, the recommended conditions for successful Dynamic Pricing deployment, recommended factors identifying the type of markets where Dynamic Pricing can be effective, and proposal for a Dynamic Pricing stakeholders’ framework were presented. Currently, the mobile telecommunications industry is witnessing a dramatic growth rate in the data consumption, being fostered mainly by higher data speed technology as the 4G LTE and by the smart devices penetration rates. However, operators’ revenue from data services lags behind and is decupled from this data consumption growth. Pricing strategy is a key factor affecting this ecosystem. Since the introduction of the 4G LTE technology will increase the pace of data growth in multiples, consequently, if pricing strategies remain constant, then the revenue and usage gap will grow wider, risking the sustainability of the ecosystem. Therefore, this research study is focused on Dynamic Pricing for 4G LTE data services, researching the drivers, benefits and challenges of 4G LTE Dynamic Pricing and the feasibility of its deployment in practice from different perspectives including operators, regulators, consumers, and telecommunications equipment manufacturers point of views.

Keywords: LTE, dynamic pricing, EPC, research

Procedia PDF Downloads 323

Authors: P. Subbuthai, S. Muruganand

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Diabetic Retinopathy is one of the complicated diseases and it is caused by the changes in the blood vessels of the retina. Extraction of retina image through Fundus camera sometimes produced poor contrast and noises. Because of this noise, detection of blood vessels in the retina is very complicated. So preprocessing is needed, in this paper, a novel algorithm is implemented to remove the noisy pixel in the retina image. The proposed algorithm is Extended Median Filter and it is applied to the green channel of the retina because green channel vessels are brighter than the background. Proposed extended median filter is compared with the existing standard median filter by performance metrics such as PSNR, MSE and RMSE. Experimental results show that the proposed Extended Median Filter algorithm gives a better result than the existing standard median filter in terms of noise suppression and detail preservation.

Keywords: fundus retina image, diabetic retinopathy, median filter, microaneurysms, exudates

Procedia PDF Downloads 334

Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

Procedia PDF Downloads 683

Authors: Ahmad Badawi Saluy, Novawiguna Kemalasari

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This study aims to examine, analyze and explain the impacts of compensation, career development and employee engagement to employee’s performance partially and simultaneously (Case Study at PT Bank XYZ). The research design used is quantitative descriptive research causality involving 30 respondents. Sources of data are from primary and secondary data, primary data obtained from questionnaires distribution and secondary data obtained from journals and books. Data analysis used model test using smart application PLS 3 that consists of test outer model and inner model. The results showed that compensation, career development and employee engagement partially have a positive impact on employee performance, while they have a positive and significant impact on employee performance simultaneously. The independent variable has the greatest impact is the employee engagement.

Keywords: compensation, career development, employee engagement, employee performance

Procedia PDF Downloads 147

Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

Procedia PDF Downloads 688

Authors: Turker Yardan, Bahattin Avci, S. Sirri Bilge, Ayhan Bozkurt

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Sinapic acid (SA) is a phenylpropanoid compound with anti-inflammatory, antioxidant, and neuroprotective activities. The purpose of this study was to characterize the possible protective effect of sinapic acid on chlorpyrifos (CPF), a common organophosphorus pesticide used worldwide, induced toxicity in rats. Forty male and female rats (240-270 g) were used in this study. Each group was composed of 5 male and 5 female rats. Sinapic acid (20 mg/kg or 40 mg/kg) or vehicle (olive oil, 1 ml ⁄ rat) were given orally for 5 days. CPF (279 mg/kg) or vehicle (peanut oil, 2 ml ⁄ kg, s.c.) was administered on the sixth day, immediately after the recording of the body weight of the animals. Twenty four hours following CPF administration body weight, body temperature and locomotor activity values were recorded before decapitation of the animals. Trunk blood, brain, and liver samples were collected for biochemical examinations. Chlorpyrifos administration decreased butyrylcholinesterase activity in blood, brain, and liver, while it increased malondialdehyde (MDA) levels and advanced oxidation protein products (AOPPs) (p < 0.01 - 0.001). Additionally, CPF administration reduced the body weight, body temperature, and locomotor activity values of the animals (p < 0.01 - 0.001). All these physiological and biochemical changes induced by CPF were reduced with the 40 mg/kg dose of SA (p < 0.05 - 0.001). Our results suggest that SA administration ameliorates CPF induced toxicity in rats, possibly by supporting the antioxidant mechanism.

Keywords: antioxidant, Chlorpyrifos, poisoning, sinapic acid

Procedia PDF Downloads 173

Authors: Rhaed Khiati, Muhammad Hanif

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In the past decade, smart healthcare systems have been on an ascendant drift, especially with the evolution of hospitals and their increasing reliance on bioinformatics and software specializing in healthcare. Doctors have become reliant on technology more than ever, something that in the past would have been looked down upon, as technology has become imperative in reducing overall costs and improving the quality of patient care. With patient-doctor interactions becoming more necessary and more complicated than ever, systems must be developed while taking into account costs, patient comfort, and patient data, among other things. In this work, we proposed a smart hospital bed, which mixes the complexity and big data usage of traditional healthcare systems with the comfort found in soft beds while taking certain concerns like data confidentiality, security, and maintaining SLA agreements, etc. into account. This research work potentially provides users, namely patients and doctors, with a seamless interaction with to their respective nurses, as well as faster access to up-to-date personal data, including prescriptions and severity of the condition in contrast to the previous research in the area where there is lack of consideration of such provisions.

Keywords: big data, smart healthcare, distributed systems, bioinformatics

Procedia PDF Downloads 191

Authors: Tero Huhtala, Minna Pikkarainen, Saila Saraniemi

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Information technology has long been used as an enabler of exchange for goods and services. Services are evolving from generic to personalized, and the reverse use of customer data has been discussed in both academia and industry for the past few years. This article presents the results of an empirical case study in the area of preventive health care services. The primary data were gathered in workshops, in which future personal data-based services were conceptualized by analyzing future scenarios from a business perspective. The aim of this study is to understand business model transformation in emerging personal data ecosystems. The work was done as a case study in the context of occupational healthcare. The results have implications to theory and practice, indicating that adopting personal data management principles requires transformation of the business model, which, if successfully managed, may provide access to more resources, potential to offer better value, and additional customer channels. These advantages correlate with the broadening of the business ecosystem. Expanding the scope of this study to include more actors would improve the validity of the research. The results draw from existing literature and are based on findings from a case study and the economic properties of the healthcare industry in Finland.

Keywords: ecosystem, business model, personal data, preventive healthcare

Procedia PDF Downloads 244

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Kyung-Soon Choi, Ji-Young Hwang, Young-Hee Pyo

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Vinegars have been used as an alternative remedy for treating gout, but the scientific basis remains to be elucidated. In this study, acetic acid fermentation was applied for the first time to Monascus-fermented soybeans to examine its effect on the bioactive components together with the xanthine oxidase inhibitory (XOI) activity of the soy vinegar. The content of total phenols (0.47~0.97 mg gallic acid equivalents/mL) and flavonoids (0.18~0.39 mg quercetin equivallents/mL) were spectrophotometrically determined, and the content of organic acid (10.22~59.76 mg/mL) and isoflavones (6.79~7.46 mg/mL) were determined using HPLC-UV. The analytical method for ubiquinones (0.079~0.276 μg/mL) employed saponification before solvent extraction and quantification using LC-MS. Soy vinegar also showed significant XOI (95.3%) after 20 days of acetic acid fermentation at 30 °C. The results suggest that soy vinegar has potential as a novel medicinal food.

Keywords: acetic acid fermentation, bioactive component, soy vinegar, xanthine oxidase inhibitory activity

Procedia PDF Downloads 377

Authors: Syed Nauman Bin Asghar Bukhari, Mohtashim Mansoor, Mohammad Nouman

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Engine test-Bed system is a fundamental tool to measure dynamic parameters, economic performance, and reliability of an aircraft Engine, and its automation and accuracy directly influences the precision of acquired and analysed data. In this paper, we present the design of digital Data Acquisition (DAQ) system for a vintage aircraft engine test bed that lacks the capability of displaying all the analyzed parameters at one convenient location (one panel-one screen). Recording such measurements in the vintage test bed is not only time consuming but also prone to human errors. Digitizing such measurement system requires a Data Acquisition (DAQ) system capable of recording these parameters and displaying them on one screen-one panel monitor. The challenge in designing upgrade to the vintage systems arises with a need to build and integrate digital measurement system from scratch with a minimal budget and modifications to the existing vintage system. The proposed design not only displays all the key performance / maintenance parameters of the gas turbine engines for operator as well as quality inspector on separate screens but also records the data for further processing / archiving.

Keywords: Gas turbine engine, engine test cell, data acquisition, instrumentation

Procedia PDF Downloads 119

Authors: Xingguo Feng, Hui Zhou, Kaifeng Zhang, Zhao Jiang, Hanjun Hu, Jun Zheng, Hong Hao

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TiN and TiC films have been prepared on Ti6Al4V alloy substrates by plasma-based ion implantation. The effect of N+C and Ti+N hybrid ion implantation at 50 kV, and Ti+C hybrid ion implantation at 20 kV, 35 kV and 50 kV extraction voltages on mechanical properties at a dose of 2×10¹⁷ ions / cm² was studied. The chemical states and microstructures of the implanted samples were investigated using X-ray photoelectron (XPS), and X-ray diffraction (XRD), together with the mechanical and tribological properties of the samples were characterized using nano-indentation and ball-on-disk tribometer. It was found that the modified layer by Ti+C implanted at 50 kV was composed of mainly TiC and Ti-O bond and the layer of Ti+N implanted at 50 kV was observed to be TiN and Ti-O bond. Hardness tests have shown that the hardness values for N+C, Ti+N, and Ti+C hybrid ion implantation samples were much higher than the un-implanted ones. The results of wear tests showed that both Ti+C and Ti+N ion implanted samples had much better wear resistance compared un-implanted sample. The wear rate of Ti+C implanted at 50 kV sample was 6.7×10⁻⁵mm³ / N.m, which was decreased over one order than unimplanted samples.

Keywords: plasma ion implantation, x-ray photoelectron (XPS), hardness, wear

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Authors: Boussoualim Nacira, Aklouf Youcef

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Software as a Service (SaaS) is a software delivery paradigm in which the product is not installed on-premise, but it is available on Internet and Web. The customers do not pay to possess the software itself but rather to use it. This concept of pay per use is very attractive. Hence, we see increasing number of organizations adopting SaaS. However, each customer is unique, which leads to a very large variation in the requirements off the software. As several suppliers propose SaaS products, the choice of this latter becomes a major issue. When multiple criteria are involved in decision making, we talk about a problem of «Multi-Criteria Decision-Making» (MCDM). Therefore, this paper presents a method to help customers to choose a better SaaS product satisfying most of their conditions and alternatives. Also, we know that a good method of adaptive selection should be based on the correct definition of the different parameters of choice. This is why we started by extraction and analysis the various parameters involved in the process of the selection of a SaaS application.

Keywords: cloud computing, business operation, Multi-Criteria Decision-Making (MCDM), Software as a Service (SaaS)

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Authors: M.H. Keshek, M.N. Omar, A.H. Amer

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Eggplant (Solanum melongena L.) is considered one of the most important crops in summer season, and it is grown in most cultivated area in Egypt. Eggplant has a very limited shelf life for freshness and physiological changes occur after harvest. Nowadays, microwave drying offers an alternative way to drying agricultural products. microwave drying is not only faster but also requiring less energy consumption than conventional drying. The main objective of this research was to evaluate using the microwave oven in Eggplant drying, to determine the optimum drying time of higher drying efficiency and lower energy consumption. The eggplants slices, having a thickness of about 5, 10, 15, and 20 mm, with diameter 50±2 mm was dried using microwave oven (KOR-9G2B) using three different levels were 450, 630, and 810 Watt (50%, 70%, and 90% of 900 Watt). The results show that, the initial moisture content of the eggplant slices was around 93 % wet basis (13.28 g water/g dry matter). The results indicated that, the moisture transfer within the sample was more rapidly during higher microwave power heating (810 watt) and lower thickness (5 mm) of the eggplant slices. In addition, the results show that, the drying efficiency increases by increasing slices thickness at power levels 450, 630 and 810 Watt. The higher drying efficiency was 83.13% occurred when drying the eggplant slices 20 mm thickness in microwave oven at power 630 Watt. the higher total energy consumption per dry kilogram was 1.275 (kWh/ dry kg) occurred at used microwave 810 Watt for drying eggplant slices 5 mm thickness, and the lower total energy consumption per dry kilogram was 0.55 (kWh/ dry kg) occurred at used microwave 810 Watt for drying eggplant slices 20 mm thickness.

Keywords: microwave drying, eggplant, drying rate, drying efficiency, energy consumption

Procedia PDF Downloads 148

Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

Procedia PDF Downloads 300

Authors: Piedad Lucia Bolivar Goez, Daniel Ignacio Garzon Luna, Maria Camila Balcero Angel, Sara Carolina Martinez Roman, Daniela Natalia Polo Rivas, Sandra Liliana Rocha Guitierrez

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This article provides a critical analysis on the conception of disorders of sexual development (DSDs) within the bioethics framework. By means of analytical thought, the objective is to approach topics such as the rediscovery of the body, the reinvention of sexuality and link them to the liability that health personnel have to inform people about the options they have to decide over their health and body. The medicalization of sexed bodies in both psychosocial and anatomo-morpho-physiological dimensions from a legal standpoint were analyzed. Its also explored the gender stereotypes established by society and the role of laws in guaranteeing the right of autonomy that takes on greater relevance in DSD. Through this analysis, it was concluded that despite intersexuality having been analyzed by Colombia’s Constitutional Court, that it is stated as a fair entity, the stigmatization by society has not allowed these individuals to belong to an egalitarian context in which everyone has the same opportunities of access to the goods and services that they need. This leads individuals to hide their identity and expression of genre in order to be accepted in a set of contexts. Thus creating a vulnerability that the health system must be able to identify and in which it is necessary to intervene at a biopsychosocial level, in order to guarantee the protection of the individual within an unquestionable frame of equality and solidarity.

Keywords: disorders of sex development, gender identity, sexuality, transgender persons

Procedia PDF Downloads 185

Authors: Riya Kartikee, Uday Shankar

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Background –.Happiness refers to a range of the balance of positive and pleasant emotions of joy, pride, contentment, gratitude, and living with ethics. Happiness is an experience combined with a sense that one’s life is good, meaningful, and worth a while, but in the context of orphans who have lost their birthgivers, their parents who play an important role in bringing necessities and comfort to them, but many terms of the above phases are missing in the life of orphan So, stress increases because of lack of love, attention, sympathy, care, they experience many kind of trauma and also in some cases their lives get worst as they face some physiological abuse, sexual abuse, they are forced to have stress at a not only mentally but physically also in the context of Patna, Bihar where many people are below poverty line, lack of resources is a normal condition for the Orphanages.AIM- The present study was intended to study the level of Happiness among the orphans of Patna District, also it was attempted to find the role of happiness in their lives as an individual.Method- The sample of 70 Orphans in the age group of 12 to 18 years were taken from the orphanages of Patna district-Apnaghar, Rainbow homes, etc. Purposive sampling was used in the study, There has been one research tool used in the study, which is Happiness scale by Dr.R.L Bhardwaj and Dr.Poonam R Das. Results- Results have revealed that Orphans have possessed a very low level of happiness and unhappiness was related due to their living conditions in the orphanage.Conclusion-It can be stated that the Level of happiness is an important missing determinant in the lives of orphans.

Keywords: happiness, orphans, patna, orphanage

Procedia PDF Downloads 162

Authors: Aminah Muchdar, Nuraeni, Eddy

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The aims of the research are: (1) to verify the cropsyst plant model of experimental data in the field of soybean plants and (2) to predict planting time and potential yield soybean plant with the use of cropsyst model. This research is divided into several stages: (1) first calibration stage which conducted in the field from June until September 2015.(2) application models stage, where the data obtained from calibration in the field will be included in cropsyst models. The required data models are climate data, ground data/soil data,also crop genetic data. The relationship between the obtained result in field with simulation cropsyst model indicated by Efficiency Index (EF) which the value is 0,939.That is showing that cropsyst model is well used. From the calculation result RRMSE which the value is 1,922%.That is showing that comparative fault prediction results from simulation with result obtained in the field is 1,92%. The conclusion has obtained that the prediction of soybean planting time cropsyst based models that have been made valid for use. and the appropriate planting time for planting soybeans mainly on rain-fed land is at the end of the rainy season, in which the above study first planting time (June 2, 2015) which gives the highest production, because at that time there was still some rain. Tanggamus varieties more resistant to slow planting time cause the percentage decrease in the yield of each decade is lower than the average of all varieties.

Keywords: soybean, Cropsyst, calibration, efficiency Index, RRMSE

Procedia PDF Downloads 173

Authors: Corbel Camille, Le Cerf Flora, Capriz Françoise, Vaillant-Ciszewicz Anne-Julie, Breaud Jean, Guerin Olivier, Corveleyn Xavier

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Background: The medical environment can generate stressful and anxiety-provoking situations for patients, particularly during painful care procedures for the older population. These stressful environments have deleterious effects on the quality of care and can even put the patient at risk and set the care team up for failure. The search for a solution is, therefore, imperative. The development of new technologies, such as virtual reality (VR), seems to be an answer to this problem. Objectives: The objective of this study is to compare the effects of virtual reality on pain and anxiety when caring for older hospitalized people with the effects of usual care. More precisely, different individual factors (age, cognitive level, individual preferences, etc.) and different virtual reality universes (personalized or non-personalized) are studied to understand the role of these factors in reducing pain and anxiety during care procedures. The aim of this study is to improve the quality of life of patients and caregivers in their work environment. Method: This mono-centered, randomized, controlled study was conducted from September 2023 to September 2024 on 120 participants recruited from the geriatric departments of the Cimiez Hospital, Nice, France. Participants are randomized into three groups: a control group, a personalized VR group and a non-personalized VR group. Each participant is followed during a painful care session. Data are collected before, during and after the care, using measures of pain (Algoplus and numerical scale) and anxiety (Hospital anxiety scale and numerical scale). Physiological assessments with an oximeter are also performed to collect both heart and respiratory rate measurements. The implementation of the care will be assessed among healthcare providers to evaluate its effects on the difficulty and fatigue associated with the care. Additionally, a questionnaire (System Usability Scale) will be administered at the conclusion of the study to determine the willingness of healthcare providers to integrate VR into their daily care practices. Result: The preliminary results indicate significant effects on anxiety (p=.001) and pain (p=<.001) following the VR intervention during care, as compared to the control group. Conclusion: The preliminary results suggest that VRI appears to be a suitable and effective method for reducing anxiety and pain among older hospitalized individuals compared with standard care. Finally, the experiences of healthcare professionals involved will also be considered to assess the impact of these interventions on working conditions and patient support.

Keywords: anxiety, care, pain, older adults, virtual reality

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Authors: Marjan Moradi Fard, Hossein Rassi, Masoud Houshmand

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Aim: Breast cancer is one of the most common cancers affecting the morbidity and mortality of Iranian women. This disease is a result of collective alterations of oncogenes and tumor suppressor genes. Studies have produced conflicting results concerning the role of p53 codon 72 polymorphism (G>C) and miR-146a rs2910164 polymorphism (G>C) on the risk of several cancers; therefore, a research was performed to estimate the association between the p53 codon 72 polymorphism and miR-146a rs2910164 polymorphism in breast cancer. Methods and Materials: A total of 45 archival breast cancer samples from khatam hospital and 40 healthy samples were collected. Verification of each cancer reported in a relative was sought through the pathology reports of the hospital records. Then, DNA extracted from all samples by standard methods and p53 codon 72 polymorphism genotypes and miR-146a rs2910164 polymorphism genotypes were analyzed using multiplex PCR. The tubules, mitotic activity, necrosis, polymorphism and grade of breast cancer were staged by Nottingham histological grading and immunohistochemical staining of the sections from the paraffin wax embedded tissues for the expression of ER, PR and p53 was carried out using a standard method. Finally, data analysis was performed using the 7 version of the Epi Info(TM) 2012 software and test chi-square(x2) for trend. Results: Successful DNA extraction was assessed by PCR amplification of b-actin gene (99 bp). According to the results, p53 GG genotype and miR-146a rs2910164 CC genotype was significantly associated with increased risk of breast cancer in the study population. In this study, we established that tumors of p53 GG genotype and miR-146a rs2910164 CC genotype exhibited higher mitotic activity, higher polymorphism, lower necrosis, lower tubules, higher ER- and PR-negatives and lower TP53-positives than the other genotypes. Conclusion: The present study provided preliminary evidence that a p53 GG genotype may effect breast cancer risk in the study population, interacting synergistically with miR-146a rs2910164 CC genotype. Our results demonstrate that the testing of p53 codon 72 polymorphism genotypes and miR-146a rs2910164 polymorphism genotypes in combination with clinical parameters can serve as major risk factors in the early identification of breast cancers.

Keywords: breast cancer, p53 codon 72 polymorphism, miR-146a rs2910164 polymorphism, genotypes

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Authors: Mehmet Ali Topçu, Aydın Ruşen

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Çinkur is the only plant in Turkey that produces zinc from primary ore containing zinc carbonate from its establishment until 1997. After this year, zinc concentrate coming from Iran was used in this plant. Therefore, there are two different leach residues namely Turkish leach residue (TLR) and Iranian leach residue (ILR), in Çinkur stock piles. This paper describes zinc recovery by sulphuric acid (H2SO4) treatment for each leach residue and includes comparison of blended of TLR and ILR. Before leach experiments; chemical, mineralogical and thermal analysis of three different leach residues was carried out by using atomic absorption spectrometry (AAS), X-Ray diffraction (XRD) and differential thermal analysis (DTA), respectively. Leaching experiments were conducted at optimum conditions; 100 oC, 150 g/L H2SO4 and 2 hours. In the experiments, stirring rate was kept constant at 600 r/min which ensures complete mixing in leaching solution. Results show that zinc recovery for Iranian LR was higher than Turkish LR due to having different chemical composition from each other.

Keywords: hydrometallurgy, leaching, metal extraction, metal recovery

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Authors: Adrià Arbués-Sangüesa, Coloma Ballester, Gloria Haro

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Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances.

Keywords: basketball, deep learning, feature extraction, single-camera, tracking

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Kaaryn Cater

Abstract:

People vary in how they perceive, process, and react to internal, external, social, and emotional environmental factors; some are more sensitive than others. Compassionate people have a highly reactive nervous system and are more impacted by positive and negative environmental conditions (Differential Susceptibility). Further, some sensitive individuals are disproportionately able to benefit from positive and supportive environments without necessarily suffering negative impacts in less supportive environments (Vantage Sensitivity). Environmental sensitivity is underpinned by physiological, genetic, and personality/temperamental factors, and the phenotypic expression of high sensitivity is Sensory Processing Sensitivity. The hallmarks of Sensory Processing Sensitivity are deep cognitive processing, emotional reactivity, high levels of empathy, noticing environmental subtleties, a tendency to observe new and novel situations, and a propensity to become overwhelmed when over-stimulated. Several educational advantages associated with high sensitivity include creativity, enhanced memory, divergent thinking, giftedness, and metacognitive monitoring. High sensitivity can also lead to some educational challenges, particularly managing multiple conflicting demands and negotiating low sensory thresholds. A mixed methods study was undertaken. In the first quantitative study, participants completed the Perceived Success in Study Survey (PSISS) and the Highly Sensitive Person Scale (HSPS-12). Inclusion criteria were current or previous postsecondary education experience. The survey was presented on social media, and snowball recruitment was employed (n=365). The Excel spreadsheets were uploaded to the statistical package for the social sciences (SPSS)26, and descriptive statistics found normal distribution. T-tests and analysis of variance (ANOVA) calculations found no difference in the responses of demographic groups, and Principal Components Analysis and the posthoc Tukey calculations identified positive associations between high sensitivity and three of the five PSISS factors. Further ANOVA calculations found positive associations between the PSISS and two of the three sensitivity subscales. This study included a response field to register interest in further research. Respondents who scored in the 70th percentile on the HSPS-12 were invited to participate in a semi-structured interview. Thirteen interviews were conducted remotely (12 female). Reflexive inductive thematic analysis was employed to analyse data, and a descriptive approach was employed to present data reflective of participant experience. The results of this study found that compassionate students prioritize work-life balance; employ a range of practical metacognitive study and self-care strategies; value independent learning; connect with learning that is meaningful; and are bothered by aspects of the physical learning environment, including lighting, noise, and indoor environmental pollutants. There is a dearth of research investigating sensitivity in the educational context, and these studies highlight the need to promote widespread education sector awareness of environmental sensitivity, and the need to include sensitivity in sector and institutional diversity and inclusion initiatives.

Keywords: differential susceptibility, highly sensitive person, learning, neurosensitivity, sensory processing sensitivity, vantage sensitivity

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Authors: Salina Budin, Nor Liawati Abu Othman, Shaira Ismail

Abstract:

This study attempts to determine kinesthetic, verbal and visual intelligences among mechanical engineering undergraduate students and explores any probable relation with students’ learning styles and academic performance. The questionnaire used in this study is based on Howard Gardner’s multiple intelligences theory comprising of five elements of learning style; environmental, sociological, emotional, physiological and psychological. Questionnaires are distributed amongst undergraduates in the Faculty of Mechanical Engineering. Additional questions on students’ perception of learning styles and their academic performance are included in the questionnaire. The results show that one third of the students are strongly dominant in the kinesthetic intelligent (33%), followed by a combination of kinesthetic and visual intelligences (29%) and 21% are strongly dominant in all three types of intelligences. There is a statistically significant correlation between kinesthetic, verbal and visual intelligences and students learning styles and academic performances. The ANOVA analysis supports that there is a significant relationship between academic performances and level of kinesthetic, verbal and visual intelligences. In addition, it has also proven a remarkable relationship between academic performances and kinesthetic, verbal and visual learning styles amongst the male and female students. Thus, it can be concluded that, academic achievements can be enhanced by understanding as well as capitalizing the students’ types of intelligences and learning styles.

Keywords: kinesthetic intelligent, verbal intelligent, visual intelligent, learning style, academic performances

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

Abstract:

In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: Kaiyi Zhang, Wenlong Li, Haicheng Li, Yifei Luo, Zheng Li, Xiaoshi Wang, Xiaodong Chen

Abstract:

Plant wearable sensors facilitate the real-time monitoring of plant physiological status. In situ monitoring of the plant chlorophyll content over days could provide valuable information on the photosynthetic capacity, nitrogen content, and general plant health. However, it cannot be achieved by current chlorophyll measuring methods. Here, a miniaturized and plant-wearable chlorophyll meter was developed for rapid, non-destructive, in situ, and long-term chlorophyll monitoring. This reflectance-based chlorophyll sensor with 1.5 mm thickness and 0.2 g weight (1000 times lighter than the commercial chlorophyll meter), includes a light emitting diode (LED) and two symmetric photodetectors (PDs) on a flexible substrate and is patched onto the leaf upper epidermis with a conformal light guiding layer. A chlorophyll content index (CCI) calculated based on this sensor shows a better linear relationship with the leaf chlorophyll content (r² > 0.9) than the traditional chlorophyll meter. This meter can wirelessly communicate with a smartphone to monitor the leaf chlorophyll change under various stresses and indicate the unhealthy status of plants for long-term application of plants under various stresses earlier than chlorophyll meter and naked-eye observation. This wearable chlorophyll sensing patch is promising in smart and precision agriculture.

Keywords: plant wearable sensors, reflectance-based measurements, chlorophyll content monitoring, smart agriculture

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Authors: Qiuxiao Chen, Yan Hou, Ning Wu

Abstract:

As there are deficiencies of the classic Douglas-Peucker Algorithm (DPA), such as high risks of deleting key nodes by mistake, high complexity, time consumption and relatively slow execution speed, a new Deletion-Cost Based Compression Algorithm (DCA) for linear vector data was proposed. For each curve — the basic element of linear vector data, all the deletion costs of its middle nodes were calculated, and the minimum deletion cost was compared with the pre-defined threshold. If the former was greater than or equal to the latter, all remaining nodes were reserved and the curve’s compression process was finished. Otherwise, the node with the minimal deletion cost was deleted, its two neighbors' deletion costs were updated, and the same loop on the compressed curve was repeated till the termination. By several comparative experiments using different types of linear vector data, the comparison between DPA and DCA was performed from the aspects of compression quality and computing efficiency. Experiment results showed that DCA outperformed DPA in compression accuracy and execution efficiency as well.

Keywords: Douglas-Peucker algorithm, linear vector data, compression, deletion cost

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Authors: Janusz Bobulski, Mariusz Kubanek

Abstract:

The main goal of the research is to develop a multimedia container structure containing three types of images: RGB, lidar and infrared, properly calibrated to each other. An additional goal is to develop program libraries for creating and saving this type of file and for restoring it. It will also be necessary to develop a method of data synchronization from lidar and RGB cameras as well as infrared. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. Autonomous cars are increasingly breaking into our consciousness. No one seems to have any doubts that self-driving cars are the future of motoring. Manufacturers promise that moving the first of them to showrooms is the prospect of the next few years. Many experts believe that creating a network of communicating autonomous cars will be able to completely eliminate accidents. However, to make this possible, it is necessary to develop effective methods of detection of objects around the moving vehicle. In bad weather conditions, this task is difficult on the basis of the RGB(red, green, blue) image. Therefore, in such situations, you should be supported by information from other sources, such as lidar or infrared cameras. The problem is the different data formats that individual types of devices return. In addition to these differences, there is a problem with the synchronization of these data and the formatting of this data. The goal of the project is to develop a file structure that could be containing a different type of data. This type of file is calling a multimedia container. A multimedia container is a container that contains many data streams, which allows you to store complete multimedia material in one file. Among the data streams located in such a container should be indicated streams of images, films, sounds, subtitles, as well as additional information, i.e., metadata. This type of file could be used in autonomous vehicles, which would certainly facilitate data processing by the intelligent autonomous vehicle management system. As shown by preliminary studies, the use of combining RGB and InfraRed images with Lidar data allows for easier data analysis. Thanks to this application, it will be possible to display the distance to the object in a color photo. Such information can be very useful for drivers and for systems in autonomous cars.

Keywords: an autonomous car, image processing, lidar, obstacle detection

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