Search results for: statistical techniques

Authors: Jia Zhang, Fengmei Yao, Yanjing Tan

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The accurate estimation of crop yield is of great importance for the food security. In this study, a process-based mechanism model was modified to estimate yield of C4 crop by modifying the carbon metabolic pathway in the photosynthesis sub-module of the RS-P-YEC (Remote-Sensing-Photosynthesis-Yield estimation for Crops) model. The yield was calculated by multiplying net primary productivity (NPP) and the harvest index (HI) derived from the ratio of grain to stalk yield. The modified RS-P-YEC model was used to simulate maize yield in the Northeast China Plain during the period 2002-2011. The statistical data of maize yield from study area was used to validate the simulated results at county-level. The results showed that the Pearson correlation coefficient (R) was 0.827 (P < 0.01) between the simulated yield and the statistical data, and the root mean square error (RMSE) was 712 kg/ha with a relative error (RE) of 9.3%. From 2002-2011, the yield of maize planting zone in the Northeast China Plain was increasing with smaller coefficient of variation (CV). The spatial pattern of simulated maize yield was consistent with the actual distribution in the Northeast China Plain, with an increasing trend from the northeast to the southwest. Hence the results demonstrated that the modified process-based model coupled with remote sensing data was suitable for yield prediction of maize in the Northeast China Plain at the spatial scale.

Keywords: process-based model, C4 crop, maize yield, remote sensing, Northeast China Plain

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Authors: S. B. Mayil Vealan, C. Sekar

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Bisphenol A (BIS A) and 4 Nitrophenol (4N) are the most prevalent environmental endocrine-disrupting chemicals which mimic hormones and have a direct relationship to the development and growth of animal and human reproductive systems. Moreover, intensive exposure to the compound is related to prostate and breast cancer, infertility, obesity, and diabetes. Hence, accurate and reliable determination techniques are crucial for preventing human exposure to these harmful chemicals. Lanthanum Copper Oxide (La₂Cu₂O₅) nanoparticles were synthesized and investigated through various techniques such as scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy. Cyclic voltammetry and square wave voltammetry techniques are employed to evaluate the electrochemical behavior of as-synthesized samples toward the electrochemical detection of Bisphenol A and 4-Nitrophenol. Under the optimal conditions, the oxidation current increased linearly with increasing the concentration of BIS A and 4-N in the range of 0.01 to 600 μM with a detection limit of 2.44 nM and 3.8 nM. These are the lowest limits of detection and the widest linear ranges in the literature for this determination. The method was applied to the simultaneous determination of BIS A and 4-N in real samples (food packing materials and river water) with excellent recovery values ranging from 95% to 99%. Better stability, sensitivity, selectivity and reproducibility, fast response, and ease of preparation made the sensor well-suitable for the simultaneous determination of bisphenol and 4 Nitrophenol. To the best of our knowledge, this is the first report in which La₂Cu₂O₅ nano particles were used as efficient electron mediators for the fabrication of endocrine disruptor (BIS A and 4N) chemical sensors.

Keywords: endocrine disruptors, electrochemical sensor, Food contacting materials, lanthanum cuprates, nanomaterials

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Authors: Salam Yaghi, Saleh Abu Dabous

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Government reports and published research have flagged and brought to public attention the deteriorating condition of a large percentage of bridges in Canada and the United States. With the increasing number of deteriorated bridges in the US, Canada, and around the globe, condition assessment techniques of concrete bridges are evolving. Investigation for bridges’ defects such as cracks, spalls, and delamination and their level of severity are the main objectives of condition assessment. Inspection and rehabilitation programs are being implemented to monitor and maintain deteriorated bridge infrastructure. This paper highlights the state-of-the art of current practices being performed for concrete bridge inspection. The information is gathered from the literature and through a distributed questionnaire. The current practices in concrete bridge inspection rely on the use of hummer sounding and chain dragging tests. Non-Destructive Testing (NDT) techniques are not being utilized fully in the process. Nonetheless, they are being partially utilized by the recommendation of the bridge inspector after conducting the visual inspection. Lanes are usually closed during the performance of visual inspection and bridge inspection in general.

Keywords: bridge inspection, condition assessment, questionnaire, non-destructive testing

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Authors: Shinen B., Bavor J., Dorjkhand B., Suvd B., Maitsetseg B.

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A number of remediation techniques are available for the treatment of soils and sediments contaminated by heavy metals. However, some of these techniques are expensive and environmentally disruptive. Nanomaterials are used in the environment as environmental catalysts to convert toxic substances from water, soil, and sediment into environmentally benign compounds. This study was carried out to scrutinize the feasibility of vivianite nanoparticles for remediation of soils contaminated with heavy metals. Column experiments were performed in the laboratory to examine nanoparticle sequestration of metal in soil amended with vivianite nanoparticle suspension. The effect of environmental parameters such as temperature, pH and redox potential on metal leachability and bioavailability of soil amended with nanoparticle suspension was examined and compared with non-amended soils. The vivianite was effective in reducing the leachability of metals in soils. It is suggested that vivianite nanoparticles could be applied for the remediation of contaminated sites polluted by heavy metals due to mining activities, particularly in Mongolia, where mining industries have been developing rapidly in the last decade.

Keywords: bioavailability, heavy metals, nanoparticles, remediation

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Authors: Ibrahim Watad, Ibrahim Abdulhalim

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Conventional spectroscopic surface plasmon resonance (SPR) sensors are widely used, both in fundamental research and environmental monitoring as well as healthcare diagnostics. However, they still lack the low limit of detection (LOD) and there still a place for improvement. SPR conventional sensors are based on the detection of a dip in the reflectivity spectrum which is relatively wide. To improve the performance of these sensors, many techniques and methods proposed either to reduce the width of the dip or to increase the sensitivity. Together with that, profiting from the sharp jump in the phase spectrum under SPR, several works suggested the extraction of the phase of the reflected wave. However, existing phase measurement setups are in general more complicated compared to the conventional setups, require more stability and are very sensitive to external vibrations and noises. In this study, a simple polarimetric technique for phase extraction under SPR is presented, followed by a theoretical error analysis and an experimental verification. The advantages of the proposed technique upon existing techniques will be elaborated, together with conclusions regarding the best polarimetric function, and its corresponding optimal metal layer range of thicknesses to use under the conventional Kretschmann-Raether configuration.

Keywords: plasmonics, polarimetry, thin films, optical sensors

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Authors: Masoumeh Kazemi

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Depression is one of the most common mental disorders that damage mental health. In addition to mental distress, mental health damage affects other dimensions of human health, including physical and social health. According to the national study of diseases and injuries in Iran, the third health problem of the country is depression. The purpose of this study was to measure the level of depression in people referred to Karaj psychiatric treatment centers, and to investigate the relationship between depression and drug and alcohol consumption. The statistical population included 5000 people. Morgan table was used to determine the sample size. The research questions sought to identify the relationship between depression and factors such as drug and alcohol use, employment and marital status, and gender. Beck standard questionnaire was used to collect complete information. Cronbach's alpha coefficient was used to confirm the reliability of the questionnaire. To test research hypotheses, non-parametric methods of correlation coefficient, Spearman's rank, Mann-Whitney and Kruskal-Wallis tests were used. The results of using SPSS statistical software showed that there is a direct relationship between depression and drug and alcohol use. Also, the rate of depression was higher in women, widows and unemployed people. Finally, by conducting the present study, it is suggested that people use the following treatments in combination for effective recovery: 1. Cognitive Behavioral Therapy (CBT) 2. Interpersonal Therapy (IPT) 3. Treatment with appropriate medication 4. Special light therapy 5. Electric shock treatment (in acute and exceptional cases) 6. Self-help

Keywords: alcohol, depression, drug, Iran

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Authors: Mae Genevieve G. Cheung, Michael G. Cuevas, Lovely Fe L. Cuison, Elijin P. Dai, Katrina Marie S. Duron, Azalea Damaris E. Encarnacion, May T. Magtoto, Gina C. Castro

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The present study aims to evaluate the effectiveness of aqueous and ethanolic leaf extracts of Blumea balsamifera in reducing obesity on diet-induced obese Sprague-Dawley rats. Aqueous and ethanolic leaf extracts were obtained by maceration and percolation, respectively, of air-dried, grinded leaves. The test animals were given a high fat diet (HFD) for 21 days, except for one negative control group fed with a standard diet (SD). The Blumea balsamifera extracts were given at doses of 300 mg/Kg and 600 mg/Kg for BBAE and BBEE groups, and the positive control group, Orlistat, was given at 21.6 mg/Kg dose. After 24 days of treatment, the statistical difference of parameters such as Lee’s index and lipid profile of each group before and after the treatment period were determined separately using Tukey’s test of two-way Analysis of Variance (ANOVA). The statistical results showed that the600mg/kg dose of BBAE and BBEE had greatly lowered the Lee’s index among the other doses while the 300 mg/Kg dose BBEE, 600 mg/Kg BBAE, and 300 mg/kg BBAE lowered the total cholesterol level, LDL level, and VLDL and total triglyceride level respectively. The extracts, however, lowered the HDL level which was also exhibited by the standard drug, Orlistat.

Keywords: adipocytes, adipogenesis, Blumea balsamifera, Lee’s index, obesity, Sambong

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Authors: Johana Jaramillo, Robin Kalfat, Dmitriy A. Dikin

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The cement production process is one of the major sources of carbon dioxide (CO₂), a potent greenhouse gas. Indeed, as a result of its cement manufacturing process, concrete contributes approximately 8% of global greenhouse gas emissions. In addition to environmental concerns, concrete also has a low tensile and ductility strength, which can lead to cracks. Graphene nanoplatelets (GNPs) have proven to be an eco-friendly solution for improving the mechanical and durability properties of concrete. The current research investigates the effects of preparing concrete enhanced with GNPs by using different wet dispersions techniques and mixing methods on its mechanical properties. Concrete specimens were prepared with 0.00 wt%, 0.10 wt%, 0.20 wt%, 0.30 wt% and wt% GNPs. Compressive and flexural strength of concrete at age 7 days were determined. The results showed that the maximum improvement in mechanical properties was observed when GNPs content was 0.20 wt%. The compressive and flexural were improved by up to 17.5% and 8.6%, respectively. When GNP dispersions were prepared by the combination of a drill and an ultrasonic probe, mechanical properties experienced maximum improvement.

Keywords: concrete, dispersion techniques, graphene nanoplatelets, mechanical properties, mixing methods

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Authors: H. Minor-Pérez, A. M. Mota-Silva, S. Ortiz-Barrios

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Substances of vegetable origin with antioxidant capacity have a high potential for application on the conservation of some foods, can prevent or reduce for example oxidation of lipids. However a food is a complex system whose wide variety of components wich can reduce or eliminate this antioxidant capacity. The antioxidant activity can be determined with the ABTS technique. The radical ABTS+ is generated from the acid 2, 2´ - Azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS). This radical is a composite color bluish-green, stable and with a spectrum of absorption into the UV-visible. The addition of antioxidants causes discoloration, value that can be reported as a percentage of inhibition of the cation radical ABTS+. The objective of this study was evaluated the effect of the combination of the pH and the essential oil of cinnamon (EOC) on inhibition of the radical ABTS+, using statistical design of central point (Design Expert) to obtain mathematical models that describe this phenomenon. Were evaluated 17 treatments with combinations of pH 5, 6 and 7 (citrate-phosphate buffer) and the concentration of essential oil of cinnamon (C): 0 µg/mL, 100 µg/mL and 200 µg/mL. The samples were analyzed using the ABTS technique. The reagent was dissolved in methanol 80% to standardized the absorbance to 0.7 +/- 0.1 at 754 nm. Then samples were mixed with reagent standardized ABTS and after 1 min and 7 min absorbance was read for each treatment at 754 nm. Was used a curve pattern with vitamin C and reported the values as inhibition (%) of radical ABTS+. The statistical analysis shows the experimental results were adjusted to a quadratic model, to the times of 1 min and 7 min. This model describes the influence of the factors investigated independently: pH and cinnamon essential oil (µg/mL) and the effect of the interaction between pH*C, as well as the square of the pH2 and C2. The model obtained was Y = 10.33684 - 3.98118*pH + 1.17031*C + 0.62745*pH2 - 3.26675*10-3*C2 - 0.013112*pH*C, where Y is the response variable. The coefficient of determination was 0.9949 for 1 min. The equation was obtained at 7 min and = - 10.89710 + 1.52341*pH + 1.32892*C + 0.47953*pH2 - 3.56605*10- *C2 - 0.034687*pH*C. The coefficient of determination was 0.9970. This means that only 1% of the total variation is not explained by the developed models. At 100 µg/mL of EOC was obtained an inhibition percentage of 80%, 84% and 97% for the pH values of 5,6 and 7 respectively, while a value of 200 µg/mL the inhibition (%) was very similar for the treatments. In these values of pH was obtained an inhibition close 97%. In conclusion the pH does not have a significant effect on the antioxidant capacity, while the concentration of EOC was decisive for the antioxidant capacity. The authors acknowledge the funding provided by the CONACYT for the project 131998.

Keywords: antioxidant activity, ABTS technique, essential oil of cinnamon, mathematical models

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Authors: Juan David Sepulveda

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This article reflects the current stage of progress in the project “Determining technological trends in energy generation”. At first it was oriented towards finding out those trends by employing such tools as the scientometrics community had proved and accepted as effective for getting reliable results. Because a documented methodological guide for this purpose could not be found, the decision was made to reorient the scope and aim of this project, changing the degree of interest in pursuing the objectives. Therefore it was decided to propose and implement a novel guide from the elements and techniques found in the available literature. This article begins by explaining the elements and considerations taken into account when implementing and applying this methodology, and the tools that led to the implementation of a software application for patent revision. Univariate analysis helped recognize the technological leaders in the field of energy, and steered the way for a multivariate analysis of this sample, which allowed for a graphical description of the techniques of mature technologies, as well as the detection of emerging technologies. This article ends with a validation of the methodology as applied to the case of fuel cells.

Keywords: energy, technology mapping, patents, univariate analysis

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Authors: Fatema Akram, Mohammad Golam Rasul, Mohammad Masud Kamal Khan, Md. Sharif Imam Ibne Amir

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A variety of routing techniques are available to develop surface runoff hydrographs from rainfall. The selection of runoff routing method is very vital as it is directly related to the type of watershed and the required degree of accuracy. There are different modelling softwares available to explore the rainfall-runoff process in urban areas. XPSTORM, a link-node based, integrated storm-water modelling software, has been used in this study for developing surface runoff hydrograph for a Golf course area located in Rockhampton in Central Queensland in Australia. Four commonly used methods, namely SWMM runoff, Kinematic wave, Laurenson, and Time-Area are employed to generate runoff hydrograph for design storm of this study area. In runoff mode of XPSTORM, the rainfall, infiltration, evaporation and depression storage for sub-catchments were simulated and the runoff from the sub-catchment to collection node was calculated. The simulation results are presented, discussed and compared. The total surface runoff generated by SWMM runoff, Kinematic wave and Time-Area methods are found to be reasonably close, which indicates any of these methods can be used for developing runoff hydrograph of the study area. Laurenson method produces a comparatively less amount of surface runoff, however, it creates highest peak of surface runoff among all which may be suitable for hilly region. Although the Laurenson hydrograph technique is widely acceptable surface runoff routing technique in Queensland (Australia), extensive investigation is recommended with detailed topographic and hydrologic data in order to assess its suitability for use in the case study area.

Keywords: ARI, design storm, IFD, rainfall temporal pattern, routing techniques, surface runoff, XPSTORM

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Authors: Muhammad Aown Sammar Raza, Salman Ahmad, Muhammad Farrukh Saleem, Muhammad Saqlain Zaheer, Rashid Iqbal, Imran Haider, Muhammad Usman Aslam, Muhammad Adnan Nazar

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One of the main negative effects of climate change is the increasing scarcity of water worldwide, especially for irrigation purpose. In order to ensure food security with less available water, there is a need to adopt easy and economic techniques. Two of the effective techniques are; use of ground covers and partial root zone drying (PRD). A field experiment was arranged to find out the most suitable mulch for PRD irrigation system in wheat. The experiment was comprised of two irrigation methods (I0 = irrigation on both sides of roots and I1= irrigation to only one side of the root as alternate irrigation) and four ground covers (M0= open ground without any cover, M1= black plastic cover, M2= wheat straw cover and M4= cotton sticks cover). More plant height, spike length, number of spikelets and number of grains were found in full irrigation treatment. While water use efficiency and grain nutrient (NPK) contents were more in PRD irrigation. All soil covers suppress the weeds and significantly influenced the yield attributes, final yield as well as the grain nutrient contents. However black plastic cover performed the best. It was concluded that joint use of both techniques was more effective for water conservation and increasing grain yield than their sole application and combination of PRD with black plastic mulch performed the best than other ground covers combination used in the experiment.

Keywords: ground covers, partial root zone drying, grain yield, quality traits, WUE, weed control efficiency

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Authors: Khalid A. Al-Afandy, El-Sayyed El-Rabaie, Osama Salah, Ahmed El-Mhalaway

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This paper presents a high secure data hiding technique using image cropping and Least Significant Bit (LSB) steganography. The predefined certain secret coordinate crops will be extracted from the cover image. The secret text message will be divided into sections. These sections quantity is equal the image crops quantity. Each section from the secret text message will embed into an image crop with a secret sequence using LSB technique. The embedding is done using the cover image color channels. Stego image is given by reassembling the image and the stego crops. The results of the technique will be compared to the other state of art techniques. Evaluation is based on visualization to detect any degradation of stego image, the difficulty of extracting the embedded data by any unauthorized viewer, Peak Signal-to-Noise Ratio of stego image (PSNR), and the embedding algorithm CPU time. Experimental results ensure that the proposed technique is more secure compared with the other traditional techniques.

Keywords: steganography, stego, LSB, crop

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Authors: Milan Joshi, Harsh Agrawal, Pallaw Mishra, Sanand Sule

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Predicting electricity usage is a crucial aspect of organizing and controlling sustainable energy systems. The task of forecasting electricity load is intricate and requires a lot of effort due to the combined impact of social, economic, technical, environmental, and cultural factors on power consumption in communities. As a result, it is important to create strong models that can handle the significant non-linear and complex nature of the task. The objective of this study is to create and compare three machine learning techniques for predicting electricity load for both the day ahead and intraday, taking into account various factors such as meteorological data and social events including holidays and festivals. The proposed methods include a LightGBM, FBProphet, combination of FBProphet and LightGBM for day ahead and Motifs( Stumpy) based on Mueens algorithm for similarity search for intraday. We utilize these techniques to predict electricity usage during normal days and social events in the Himachal Region. We then assess their performance by measuring the MSE, RMSE, and MAPE values. The outcomes demonstrate that the combination of FBProphet and LightGBM method is the most accurate for day ahead and Motifs for intraday forecasting of electricity usage, surpassing other models in terms of MAPE, RMSE, and MSE. Moreover, the FBProphet - LightGBM approach proves to be highly effective in forecasting electricity load during social events, exhibiting precise day ahead predictions. In summary, our proposed electricity forecasting techniques display excellent performance in predicting electricity usage during normal days and special events in the Himachal Region.

Keywords: feature engineering, FBProphet, LightGBM, MASS, Motifs, MAPE

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

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Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

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Authors: Elmahdi Elgharbaoui, Tamou Nasser, Ahmed Essadki

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In the era of sustainable development, photovoltaic (PV) technology has shown significant potential as a renewable energy source. Photovoltaic generators (GPV) have a non-linear current-voltage characteristic, with a maximum power point (MPP) characterized by an optimal voltage, and depends on environmental factors such as temperature and irradiation. To extract each time the maximum power available at the terminals of the GPV and transfer it to the load, an adaptation stage is used, consisting of a boost chopper controlled by a maximum power point tracking technique (MPPT) through a stage of pulse width modulation (PWM). Our choice has focused on three techniques which are: the perturbation and observation method (P&O), the incremental conductance method (InCond) and the last is that of control using the fuzzy logic. The implementation and simulation of the system (photovoltaic generator, chopper boost, PWM and MPPT techniques) are then performed in the Matlab/Simulink environment.

Keywords: photovoltaic generator, technique MPPT, boost chopper, PWM, fuzzy logic, P&O, InCond

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Rupali Bhatia, Deepthi Nair, Geetika Khanna, Seema Singhal

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Introduction: Genital tract tuberculosis is a chronic disease (caused by reactivation of organisms from systemic distribution of Mycobacterium tuberculosis) that often presents with low grade symptoms and non-specific complaints. Patients with genital tuberculosis are usually young women seeking workup and treatment for infertility. Infertility is the commonest presentation due to involvement of the fallopian tubes, endometrium and ovarian damage with poor ovarian volume and reserve. The diagnosis of genital tuberculosis is difficult because of the fact that it is a silent invader of genital tract. Since tissue cannot be obtained from fallopian tubes, the diagnosis is made by isolation of bacilli from endometrial tissue obtained by endometrial biopsy curettage and/or aspiration. Problems are associated with sampling technique as well as diagnostic modality due to lack of adequate sample volumes and the segregation of the sample for various diagnostic tests resulting in non-uniform distribution of microorganisms. Moreover, lack of an efficient sampling technique universally applicable for all specific diagnostic tests contributes to the diagnostic challenges. Endometrial sampling plays a key role in accurate diagnosis of female genital tuberculosis. It may be done by 2 methods viz. endometrial curettage and endometrial aspiration. Both endometrial curettage and aspirate have their own limitations as curettage picks up strip of the endometrium from one of the walls of the uterine cavity including tubal osteal areas whereas aspirate obtains total tissue with exfoliated cells present in the secretory fluid of the endometrial cavity. Further, sparse and uneven distribution of the bacilli remains a major factor contributing to the limitations of the techniques. The sample that is obtained by either technique is subjected to histopathological examination, AFB staining, culture and PCR. Aim: Comparison of the sampling techniques viz. endometrial biopsy curettage and endometrial aspiration using different laboratory methods of histopathology, cytology, microbiology and molecular biology. Method: In a hospital based observational study, 75 Indian females suspected of genital tuberculosis were selected on the basis of inclusion criteria. The women underwent endometrial tissue sampling using Novaks biopsy curette and Karmans cannula. One part of the specimen obtained was sent in formalin solution for histopathological testing and another part was sent in normal saline for acid fast bacilli smear, culture and polymerase chain reaction. The results so obtained were correlated using coefficient of correlation and chi square test. Result: Concordance of results showed moderate agreement between both the sampling techniques. Among HPE, AFB and PCR, maximum sensitivity was observed for PCR, though the specificity was not as high as other techniques. Conclusion: Statistically no significant difference was observed between the results obtained by the two sampling techniques. Therefore, one may use either EA or EB to obtain endometrial samples and avoid multiple sampling as both the techniques are equally efficient in diagnosing genital tuberculosis by HPE, AFB, culture or PCR.

Keywords: acid fast bacilli (AFB), histopatholgy examination (HPE), polymerase chain reaction (PCR), endometrial biopsy curettage

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Authors: Chijindu Okpalaoka, Charles Chinedu Onuselogu

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COVID-19, humanity's coronavirus disease caused by SARS-CoV-2, was first detected in late 2019 in Wuhan, China. COVID-19 lacked an established conventional pharmaceutical therapy, and as a result, the outbreak quickly became an epidemic affecting the entire World. Only a qPCR assay is reliable for diagnosing COVID-19. Clustered, regularly interspaced short palindromic repeats (CRISPR) technology is being researched for speedy and specific identification of COVID-19, among other therapeutic techniques. Apart from its therapeutic capabilities, the CRISPR technique is being evaluated to develop antiviral therapies; nevertheless, no CRISPR-based medication has been approved for human use to date. Prophylactic antiviral CRISPR in living being cells, a Cas 13-based approach against coronavirus, has been developed. While this method can be evolved into a treatment approach, it may face substantial obstacles in human clinical trials for licensure. This study discussed the potential applications of CRISPR-based techniques for developing a speedy and accurate feasible treatment alternative for the COVID-19 virus.

Keywords: COVID-19, CRISPR technique, Cas13, SARS-CoV-2, prophylactic antiviral

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Authors: Orkide Donma, Mustafa M. Donma

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Morbid obesity is a health threatening condition particularly in children. Generally, it leads to the development of metabolic syndrome (MetS) characterized by central obesity, elevated fasting blood glucose (FBG), triglyceride (TRG), blood pressure values and suppressed high density lipoprotein cholesterol (HDL-C) levels. However, some ambiguities exist during the diagnosis of MetS in children below 10 years of age. Therefore, clinicians are in the need of some surrogate markers for the laboratory assessment of pediatric MetS. In this study, the aim is to develop an index, which will be more helpful during the evaluation of further risks detected in morbid obese (MO) children. A total of 235 children with normal body mass index (N-BMI), with varying degrees of obesity; overweight (OW), obese (OB), MO as well as MetS participated in this study. The study was approved by the Institutional Ethical Committee. Informed consent forms were obtained from the parents of the children. Obesity states of the children were classified using BMI percentiles adjusted for age and sex. For the purpose, tabulated data prepared by WHO were used. MetS criteria were defined. Systolic and diastolic blood pressure values were measured. Parameters related to glucose and lipid metabolisms were determined. FBG, insulin (INS), HDL-C, TRG concentrations were determined. Diagnostic Obesity Notation Model Assessment Laboratory (DONMA_LAB) Index [ln TRG/HDL-C*INS] was introduced. Commonly used insulin resistance (IR) indices such as Homeostatic Model Assessment for IR (HOMA-IR) as well as ratios such as TRG/HDL-C, TRG/HDL-C*INS, HDL-C/TRG*INS, TRG/HDL-C*INS/FBG, log, and ln versions of these ratios were calculated. Results were interpreted using statistical package program (SPSS Version 16.0) for Windows. The data were evaluated using appropriate statistical tests. The degree for statistical significance was defined as 0.05. 35 N, 20 OW, 47 OB, 97 MO children and 36 with MetS were investigated. Mean ± SD values of TRG/HDL-C were 1.27 ± 0.69, 1.86 ± 1.08, 2.15 ± 1.22, 2.48 ± 2.35 and 4.61 ± 3.92 for N, OW, OB, MO and MetS children, respectively. Corresponding values for the DONMA_LAB index were 2.17 ± 1.07, 3.01 ± 0.94, 3.41 ± 0.93, 3.43 ± 1.08 and 4.32 ± 1.00. TRG/HDL-C ratio significantly differed between N and MetS groups. On the other hand, DONMA_LAB index exhibited statistically significant differences between N and all the other groups except the OW group. This index was capable of discriminating MO children from those with MetS. Statistically significant elevations were detected in MO children with MetS (p < 0.05). Multiple parameters are commonly used during the assessment of MetS. Upon evaluation of the values obtained for N, OW, OB, MO groups and for MO children with MetS, the [ln TRG/HDL-C*INS] value was unique in discriminating children with MetS.

Keywords: children, index, laboratory, metabolic syndrome, obesity

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Praveena Sinha

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Context: Diabetes is a global public health burden, particularly affecting postmenopausal women. Insulin resistance (IR) is prevalent in this population, and it is associated with an increased risk of developing type 2 diabetes. Yoga therapy is gaining attention as a complementary intervention for diabetes due to its potential to address stress psychophysiology. This study focuses on the efficacy of a 12-week yoga practice in attenuating insulin resistance in early postmenopausal women. Research Aim: The aim of this research is to investigate the effect of a 3-month long yoga practice on insulin resistance in early postmenopausal women. Methodology: The study conducted a prospective longitudinal design with 67 women within five years of menopause. Participants were divided into two groups based on their willingness to join yoga. The Yoga group (n = 37) received routine gynecological management along with an integrated yoga module, while the Non-Yoga group (n = 30) received only routine management. Insulin resistance was measured using the homeostasis model assessment of insulin resistance (HOMA-IR) method before and after the intervention. Statistical analysis was performed using GraphPad Prism Version 5 software, with statistical significance set at P < 0.05. Findings: The results indicate a significant decrease in serum fasting insulin levels and HOMA-IR measurements in the Yoga group, although the decrease did not reach statistical significance. In contrast, the Non-Yoga group showed a significant rise in serum fasting insulin levels and HOMA-IR measurements after 3 months, suggesting a detrimental effect on insulin resistance in these postmenopausal women. Theoretical Importance: This study provides evidence that a 12-week yoga practice can attenuate the increase in insulin resistance in early postmenopausal women. It highlights the potential of yoga as a preventive measure against the early onset of insulin resistance and the development of type 2 diabetes mellitus. Regular yoga practice can be a valuable tool in addressing hormonal imbalances associated with early postmenopause, leading to a decrease in morbidity and mortality related to insulin resistance and type 2 diabetes mellitus in this population. Data Collection and Analysis Procedures: Data collection involved measuring serum fasting insulin levels and calculating HOMA-IR. Statistical analysis was performed using GraphPad Prism Version 5 software, and mean values with standard error of the mean were reported. The significance level was set at P < 0.05. Question Addressed: The study aimed to address whether a 3-month long yoga practice could attenuate insulin resistance in early postmenopausal women. Conclusion: The research findings support the efficacy of a 12-week yoga practice in attenuating insulin resistance in early postmenopausal women. Regular yoga practice has the potential to prevent the early onset of insulin resistance and the development of type 2 diabetes mellitus in this population. By addressing the hormonal imbalances associated with early post menopause, yoga could significantly decrease morbidity and mortality related to insulin resistance and type 2 diabetes mellitus in these subjects.

Keywords: post menopause, insulin resistance, HOMA-IR, yoga, type 2 diabetes mellitus

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Authors: Setareh Yousife

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Introduction: Studies suggest that Extramarital Infidelity (EMI) variants, such as sexual and emotional infidelities are increasing in marriage relationships. To our knowledge, less is known about what therapies and mental-hygiene factors can prevent more effective this behavior and address it. Spiritual and cognitive-behavioural health have proven to reduce marital conflict, Increase marital satisfaction and commitment. Objective: This study aims to discuss the effectiveness of spiritual counseling combined with Cognitive-behavioural techniques in addressing Extramarital Infidelity. Method: Descriptive, analytical, and intervention articles indexed in SID, Noormags, Scopus, Iranmedex, Web of Science and PubMed databases, and Google Scholar were searched. We focused on Studies in which Women with extramarital relationships, including heterosexual married couples-only studies and spirituality/religion and CBT as coping techniques used as EMI therapy. Finally, the full text of all eligible articles was prepared and discussed in this review. Results: 25 publications were identified, and their textual analysis facilitated through four thematic approaches: The nature of EMI in Women, the meaning of spirituality in the context of mental health and human behavior as well as psychotherapy; Spirituality integrated into Cognitive-Behavioral approach, The role of Spirituality as a deterrent to EMI. Conclusions: The integration of the findings discussed herein suggests that the application of cognitive and behavioral skills in addressing these kinds of destructive family-based relationships is inevitable. As treatments based on religion/spirituality or cognition/behavior do not seem adequately effective in dealing with EMI, the combination of these approaches may lead to higher efficacy in fewer sessions and a shorter time.

Keywords: spirituality, religion, cognitive behavioral therapy, extramarital relation, infidelity

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Authors: Rose Mini Agoes Salim, Shahnaz Safitri

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The aim of this study is to test the effectiveness of teaching kindness in preschool children by using several techniques. Kindness is a physical act or emotional support aimed to build or maintain relationships with others. Kindness is known to be essential in the development of moral reasoning to distinguish between the good and bad things. In this study, kindness is operationalized as several acts including helping friends, comforting sad friends, inviting friends to play, protecting others, sharing, saying hello, saying thank you, encouraging others, and apologizing. It is mentioned that kindness is crucial to be developed in preschool children because this is the time the children begin to interact with their social environment through play. Furthermore, preschool children's cognitive development makes them begin to represent the world with words, which then allows them to interact with others. On the other hand, preschool children egocentric thinking makes them still need to learn to consider another person's perspective. In relation to social interaction, preschool children need to be stimulated and assisted by adult to be able to pay attention to other and act with kindness toward them. On teaching kindness to children, the quality of interaction between children and their significant others is the key factor. It is known that preschool children learn about kindness by imitating adults on their two way interaction. Specifically, this study examines two types of teaching techniques that can be done by parents as a way to teach kindness, namely the picture-storybook reading and hand-puppet storytelling. These techniques were examined because both activities are easy to do and both also provide a model of behavior for the child based on the character in the story. To specifically examine those techniques effectiveness in teaching kindness, two studies were conducted. Study I involves 31 children aged 5-6 years old with picture-storybook reading technique, where the intervention is done by reading 8 picture books for 8 days. In study II, hand-puppet storytelling technique is examined to 32 children aged 3-5 years old. The treatments effectiveness are measured using an instrument in the form of nine colored cards that describe the behavior of kindness. Data analysis using Wilcoxon Signed-rank test shows a significant difference on the average score of kindness (p < 0.05) before and after the intervention has been held. For daily observation, a ‘kindness tree’ and observation sheets are used which are filled out by the teacher. Two weeks after interventions, an improvement on all kindness behaviors measured is intact. The same result is also gained from both ‘kindness tree’ and observational sheets.

Keywords: kindness, moral teaching, storytelling, hand puppet

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Authors: Khunsa Fatima, Umar K. Khattak, Allah Bakhsh Kausar

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Satellite images interpretation and analysis assist geologists by providing valuable information about geology and minerals of an area to be surveyed. A test site in Fatejang of district Attock has been studied using Landsat ETM+ and ASTER satellite images for lithological mapping. Five different supervised image classification techniques namely maximum likelihood, parallelepiped, minimum distance to mean, mahalanobis distance and spectral angle mapper have been performed on both satellite data images to find out the suitable classification technique for lithological mapping in the study area. Results of these five image classification techniques were compared with the geological map produced by Geological Survey of Pakistan. The result of maximum likelihood classification technique applied on ASTER satellite image has the highest correlation of 0.66 with the geological map. Field observations and XRD spectra of field samples also verified the results. A lithological map was then prepared based on the maximum likelihood classification of ASTER satellite image.

Keywords: ASTER, Landsat-ETM+, satellite, image classification

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Authors: Marzena Trojanczyk, Mariusz Jaworski, Ewa Dmoch Gajzlerska

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Purpose: The purpose of this paper is to assess the relationship between the level of quality of life and willingness to take treatment in patients with eating disorders as anorexia, bulimia and compulsive bingeing. Material and methods: The subjects consisted of 99 women with eating disorders: anorexia, n = 33; bulimia, n = 35; compulsive overeating, n = 31 and 35 women in the control group. The study used an original questionnaire to assess the overall quality of life, as well as selected areas of the physical, mental, social and spiritual satisfaction. The subjects were also asked about the level of motivation for treatment, and the importance of the disease in the lives of patients. Statistical analyses were performed using the statistical program SPSS 18.0. Results: Women with eating disorders in particular groups did not differ with respect to each other in the aspect of overall quality of life, satisfaction with the development of the spiritual, social functioning and mental health. The severity level of the disease in the lives of patients showed a negative correlation with social functioning in women with anorexia nervosa. In the case of patients with compulsive bingeing a positive relationship between the level of importance of the disease and the satisfaction of spiritual development is reported. Conclusions: Concerning the inferior quality of life, there is no relationship between a willingness to take treatment and the importance of the disease in the lives of patients with anorexia, bulimia and compulsive bingeing.

Keywords: anorexia, bulimia, compulsive overeating, quality of life

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Authors: A. A. Adekunle, A. M. Omoare, W. O. Oyediran

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This study was carried out to assess rural women’s skill acquisition in the processing of locust bean in Ipokia Local Government Area of Ogun State, Nigeria. Simple random sampling technique was used to select 90 women locust bean processors for this study. Data were analyzed with descriptive statistics and Pearson Product Moment Correlation. The result showed that the mean age of respondents was 40.72 years. Most (70.00%) of the respondents were married. The mean processing experience was 8.63 years. 93.30% of the respondents relied on information from fellow locust beans processors and friends. All (100%) the respondents did not acquire improved processing skill through trainings and workshops. It can be concluded that the rural women’s skill acquisition on modernized processing techniques was generally low. It is hereby recommend that the rural women processors should be trained by extension service providers through series of workshops and seminars on improved processing techniques.

Keywords: locust bean, processing, skill acquisition, rural women

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Authors: Michael A. Kraus, Navid Pourmoghaddam, Martin Botz, Jens Schneider, Geralt Siebert

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Fragmentation analysis of tempered glass gives insight into the quality of the tempering process and defines a certain degree of safety as well. Different standard such as the European EN 12150-1 or the American ASTM C 1048/CPSC 16 CFR 1201 define a minimum number of fragments required for soda-lime safety glass on the basis of fragmentation test results for classification. This work presents an approach for the glass breakage pattern prediction using a Voronoi Tesselation over Random Fields. The random Voronoi tessellation is trained with and validated against data from several breakage patterns. The fragments in observation areas of 50 mm x 50 mm were used for training and validation. All glass specimen used in this study were commercially available soda-lime glasses at three different thicknesses levels of 4 mm, 8 mm and 12 mm. The results of this work form a Bayesian framework for the training and prediction of breakage patterns of tempered soda-lime glass using a Voronoi Random Field Tesselation. Uncertainties occurring in this process can be well quantified, and several statistical measures of the pattern can be preservation with this method. Within this work it was found, that different Random Fields as basis for the Voronoi Tesselation lead to differently well fitted statistical properties of the glass breakage patterns. As the methodology is derived and kept general, the framework could be also applied to other random tesselations and crack pattern modelling purposes.

Keywords: glass breakage predicition, Voronoi Random Field Tessellation, fragmentation analysis, Bayesian parameter identification

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Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

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Authors: Sana El Adlani, Yassir Ait Ben Kaddour, Abdelhafid Benksim, Abderraouf Soummani, Mohamed Cherkaoui

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Background: Infertility in all countries of the word is on an increase, it’s why the World Health Organization included an investigation into young people's fertility. In this sense, it’s important to increase efforts to improve the knowledge about fertility for the young population. The aim of this study is to describe the difference between knowledge and attitude of medical and non-medical Moroccan young people. Materials and Methods: 100 medical Moroccan students (group 1) participated in the study, between 18 and 30 years, by a simple random sampling method, during 2020 and using a previously validated questionnaire. The answers were confronted to the result of our same study among 355 non-medical Moroccan young people (group 2) in 2019. Statistical analyses were performed using Statistical Package for the Social Sciences (version 10). Result: Medical students had a significantly higher level of knowledge about infertility than non-medical young people. However, both groups were aware of the impact of lifestyle on infertility. The knowledge state of the first group about infertility management was higher than the second group. Moreover, all non-medical Moroccan young people believed that it is easier to conceive if the couples had already their first baby, whereas, among medical students, only 53% had confirmed this belief. The results showed that 65% of medical students had proposed to try fertility treatments more than one time if treatment fails. Besides, the first advice of the second group was polygamy and adoption. Conclusion: Following the result of our study, the investigation of young people is the measure to optimize reproductive health. So, it’s crucial that the government increase efforts to improve the knowledge about infertility not only for medical universities but for all scholar programs.

Keywords: attitude, infertility, knowledge, medical, non-medical, young people

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