Search results for: feature for feature match

Authors: Goodness Onwuka, Khaled Abou-El-Hossein

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Borosilicate-crown (BK7) glass has found broad application in the optic and automotive industries and the growing demands for nanometric surface finishes is becoming a necessity in such applications. Thus, it has become paramount to optimize the parameters influencing the surface roughness of this precision lens. The research was carried out on a 4-axes Nanoform 250 precision lathe machine with an ultra-high precision grinding spindle. The experiment varied the machining parameters of feed rate, wheel speed and depth of cut at three levels for different combinations using Box Behnken design of experiment and the resulting surface roughness values were measured using a Taylor Hobson Dimension XL optical profiler. Acoustic emission monitoring technique was applied at a high sampling rate to monitor the machining process while further signal processing and feature extraction methods were implemented to generate the input to a neural network algorithm. This paper highlights the training and development of a back propagation neural network prediction algorithm through careful selection of parameters and the result show a better classification accuracy when compared to a previously developed response surface model with very similar machining parameters. Hence artificial neural network algorithms provide better surface roughness prediction accuracy in the ultra-high precision grinding of BK7 glass.

Keywords: acoustic emission technique, artificial neural network, surface roughness, ultra-high precision grinding

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Authors: Hany Mustafa, Mahmoud Roushdi, Khaled Kheireldin

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Rainfall variability is an important feature of semi-arid climates. Climate change is very likely to increase the frequency, magnitude, and variability of extreme weather events such as droughts, floods, and storms. The Blue Nile Basin is facing extreme climate change-related events such as floods and droughts and its possible impacts on ecosystem, livelihood, agriculture, livestock, and biodiversity are expected. Rainfall variability is a threat to food production in the Blue Nile Basin countries. This study investigates the long-term variations and trends of seasonal and annual precipitation over the Blue Nile Basin for 102-year period (1901-2002). Six statistical trend analysis of precipitation was performed with nonparametric Mann-Kendall test and Sen's slope estimator. On the other hands, four statistical absolute homogeneity tests: Standard Normal Homogeneity Test, Buishand Range test, Pettitt test and the Von Neumann ratio test were applied to test the homogeneity of the rainfall data, using XLSTAT software, which results of p-valueless than alpha=0.05, were significant. The percentages of significant trends obtained for each parameter in the different seasons are presented. The study recommends adaptation strategies to be streamlined to relevant policies, enhancing local farmers’ adaptive capacity for facing future climate change effects.

Keywords: Blue Nile basin, climate change, Mann-Kendall test, trend analysis

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Authors: Helen Dilman, Eyal Oz, Shmuel Hayun, Nahum Frage

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The conventional approach for manufacturing silicon carbide and boron carbide reaction bonded composites is based on infiltrating a ceramic porous preform with molten silicon. The relatively high melting temperature of the silicon infiltrating medium is a drawback of the process. The present contribution is concerned with an approach that allows obtaining reaction bonded composites by pressure-less infiltration at a significantly lower (850-1000oC) temperature range. This approach was applied for the fabrication of fully dense SiC/(Si-Al) and (SiC+B4C)/(Si-Al) composites. The key feature of the approach is based on using Si alloys with low melting temperature and the Mg-vapor atmosphere, under which an adequate wetting between ceramics and liquid alloys for the infiltration process is achieved. In the first set of the experiments ceramic performs compacted from multimodal SiC powders (with the green density of about 27 vol. %) without free carbon addition were infiltrated by Si-20%Al alloy at 950oC. In the second set, 19 vol. % of a fine boron carbide powder was added to SiC powders as a source of carbon. The green density of the SiC-B4C preforms was about 23-25 vol. %. In both cases, successful infiltration was achieved and the composites were fully dense. The density of the composites was about 3g/cm3. For the SiC based composites the hardness value was 750±150HV, Young modulus-280GPa and bending strength-240±30MPa. These values for (SiC-B4C)/(Si-Al) composites (1460±200HV, 317GPa and 360±20MPa) were significantly higher due to the formation of novel ceramics phases. Microstructural characteristics of the composites and their phase composition will be discussed.

Keywords: boron carbide, composites, infiltration, low temperatures, silicon carbide

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: A. D. Loureiro, L. Ventura

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Exposure to high-energy blue light has been strongly linked to the development of some eye diseases, such as age-related macular degeneration. Over the past few years, people have become more and more concerned about eye damage from blue light and how it can be prevented. We developed a prototype that allows users to self-check the blue light protection of their sunglasses and determines if the protection is adequate. Weighting functions approximating those defined in ISO 12312-1 were used to measure the luminous transmittance and blue light transmittance of sunglasses. The blue light transmittance value must be less than 1.2 times the luminous transmittance to be considered adequate. The prototype consists of a Golden Dragon Ultra White LED from OSRAM and a TCS3472 photodetector from AMS TAOS. Together, they provide four transmittance values weighted with different functions. These four transmittance values were then linearly combined to produce transmittance values with weighting functions close to those defined in ISO 12312-1 for luminous transmittance and for blue light transmittance. To evaluate our prototype, we used a VARIAN Cary 5000 spectrophotometer, a gold standard in the field, to measure the luminous transmittance and the blue light transmittance of 60 sunglasses lenses. (and Bland-Altman analysis was performed) Bland-Altman analysis was performed and showed non-significant bias and narrow 95% limits of agreement within predefined tolerances for both luminous transmittance and blue light transmittance. The results show that the prototype is a viable means of providing blue light protection information to the general public and a quick and easy way for industry and retailers to test their products. In addition, our prototype plays an important role in educating the public about a feature to look for in sunglasses before purchasing.

Keywords: blue light, sunglasses, eye protective devices, transmittance measurement, standards, ISO 12312-1

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Authors: Marco Lopes, Pedro D. Gaspar, Maria P. Simões

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This paper reviews the different concepts that have been investigated concerning the mechanization of fruit thinning as well as multiple working principles and solutions that have been developed for feature extraction of horticultural products, both in the field and industrial environments. The research should be committed towards selective methods, which inevitably need to incorporate some kinds of sensor technology. Computer vision often comes out as an obvious solution for unstructured detection problems, although leaves despite the chosen point of view frequently occlude fruits. Further research on non-traditional sensors that are capable of object differentiation is needed. Ultrasonic and Near Infrared (NIR) technologies have been investigated for applications related to horticultural produce and show a potential to satisfy this need while simultaneously providing spatial information as time of flight sensors. Light Detection and Ranging (LIDAR) technology also shows a huge potential but it implies much greater costs and the related equipment is usually much larger, making it less suitable for portable devices, which may serve a purpose on smaller unstructured orchards. Portable devices may serve a purpose on these types of orchards. In what concerns sensor methods, on-tree fruit detection, major challenge is to overcome the problem of fruits’ occlusion by leaves and branches. Hence, nontraditional sensors capable of providing some type of differentiation should be investigated.

Keywords: fruit thinning, horticultural field, portable devices, sensor technologies

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Authors: Pyi Phyo Maung

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In the study, the calculations of the permeability coefficient, values of the volume and porosity of a unit cell of a woven fabric before and after deformation based on the geometrical parameters are presented. Two types of carbon woven fabric structures were investigated: standard type, which integrated the filament, has a cross sectional shape of a cylinder and spread tow type, which has a rectangular cross sectional shape. The space antennas reflector, which distinctive feature is the presence of the surface of double curvature, is considered as the object of the research. Modeling of the kinetics of the process of impregnation of the reflector for the two types of carbon fabric’s unit cell structures was performed using software RAM-RTM. This work also investigated the influence of the grid angle between warp and welt of the unit cell on the duration of impregnation process. The results showed that decreasing the angle between warp and welt of the unit cell, the decreasing of the permeability values were occurred. Based on the results of calculation samples of the reflectors, their quality was determined. The comparisons of the theoretical and experimental results have been carried out. Comparison of the two textile structures (standard and spread tow) showed that the standard textiles with circular cross section were impregnated faster than spread tows, which have a rectangular cross section.

Keywords: vacuum assistant resin infusion, impregnation time, shear angle, reflector and modeling

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Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Raju Murugan, Pankaj S. Kolhe

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The present study is mainly focusing on the numerical simulation of Flow Blurring (FB) twin fluid injection concept was proposed by Ganan-Calvo, which involves back flow atomization based on global bifurcation of liquid and gas streams, thus creating two-phase flow near the injector exit. The interesting feature of FB injector spray is an insignificant effect of variation in atomizing air to liquid ratio (ALR) on a spray cone angle. Besides, FB injectors produce a nearly uniform spatial distribution of mean droplet diameter and are least susceptible to variation in thermo-physical properties of fuels, making it a perfect candidate for fuel flexible combustor development. The FB injector working principle has been realized through experimental flow visualization techniques only. The present study explores potential of ANSYS Fluent based Large Eddy Simulation(LES) with volume of fluid (VOF) method to investigate two-phase flow just upstream of injector dump plane and spray quality immediate downstream of injector dump plane. Note that, water and air represent liquid and gas phase in all simulations and ALR is varied by changing the air mass flow rate alone. Preliminary results capture two phase flow just upstream of injector dump plane and qualitative agreement is observed with the available experimental literature.

Keywords: flow blurring twin fluid atomization, large eddy simulation, volume of fluid, air to liquid ratio

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Authors: Meaghan Bowman

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Microblogging has become increasingly popular for commenting on current events, spreading gossip, and encouraging individualism--which favors its low-context communication channel. These social media (SM) platforms allow users to express opinions while interacting with a wide range of populations. Hashtags allow immediate identification of like-minded individuals worldwide on a vast array of topics. The output of the analytic tool, Linguistic Inquiry and Word Count (LIWC)--a program that associates psychological meaning with the frequency of use of specific words--may suggest the nature of individuals’ internal states and general sentiments. When applied to groupings of SM posts unified by a hashtag, such information can be helpful to community leaders during periods in which the forming of public opinion happens in parallel with the unfolding of political, economic, or social events. This is especially true when outcomes stand to impact the well-being of the group. Here, we applied the online tools, Google Translate and the University of Texas’s LIWC, to a 90-posting sample from a corpus of Colombian Spanish microblogs. On translated disjoint sets, identified by hashtag as being authored by advocates of voting “No,” advocates voting “Yes,” and entities refraining from hashtag use, we observed the value of LIWC’s Tone feature as distinguishing among the categories and the word “peace,” as carrying particular significance, due to its frequency of use in the data.

Keywords: Colombia peace referendum, FARC, hashtags, linguistics, microblogging, social media

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Authors: Rui Liu, Klaus Greve

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The navigation information is a kind of dynamic geographic information, and the navigation information system is a kind of special geographic information system. At present, there are many researches on the application of centralized management and cross-integration application of basic geographic information. However, the idea of information integration and sharing is not deeply applied into the research of navigation information service. And the imperfection of navigation target coordination and navigation information sharing mechanism under certain navigation tasks has greatly affected the reliability and scientificity of navigation service such as path planning. Considering this, the project intends to study the multi-source information fusion and multi-objective united navigation information interaction mechanism: first of all, investigate the actual needs of navigation users in different areas, and establish the preliminary navigation information classification and importance level model; and then analyze the characteristics of the remote sensing and GIS vector data, and design the fusion algorithm from the aspect of improving the positioning accuracy and extracting the navigation environment data. At last, the project intends to analyze the feature of navigation information of the land, sea and air navigation targets, and design the united navigation data standard and navigation information sharing model under certain navigation tasks, and establish a test navigation system for united navigation simulation experiment. The aim of this study is to explore the theory of united navigation service and optimize the navigation information service model, which will lay the theory and technology foundation for the united navigation of land, sea and air targets.

Keywords: information fusion, united navigation, dynamic path planning, navigation information visualization

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Authors: Paua Ketan, Bhagate Rajkumar, Adiga Ganesh, M. Kiran

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Low gear noise is an important sound quality feature in modern passenger cars. Annoying gear noise from the gearbox is influenced by the gear design, gearbox shaft layout, manufacturing deviations in the components, assembly errors and the mounting arrangement of the complete gearbox. Geometrical deviations in the form of profile and lead errors are often present on the flanks of the inspected gears. Ghost frequencies of a gear are very challenging to identify in standard gear measurement and analysis process due to small wavelengths involved. In this paper, gear whine noise occurring at non-integral multiples of gear mesh frequency of passenger car gearbox is investigated and the root cause is identified using the displacement deviation analysis (DDA) method. DDA method is applied to identify ghost frequency excitations on the flanks of gears arising out of generation grinding. Frequency identified through DDA correlated with the frequency of vibration and noise on the end-of-line machine as well as vehicle level measurements. With the application of DDA method along with standard lead profile measurement, gears with ghost frequency geometry deviations were identified on the production line to eliminate defective parts and thereby eliminate ghost frequency noise from a vehicle. Further, displacement deviation analysis can be used in conjunction with the manufacturing process simulation to arrive at suitable countermeasures for arresting the ghost frequency.

Keywords: displacement deviation analysis, gear whine, ghost frequency, sound quality

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Authors: O. Sunantha, S. Zhenfeng, S. Phattraporn, A. Zeeshan

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The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. While it is recognized that SOC predictors vary significantly when derived from remote sensing data and environmental variables, identifying the specific parameters most suitable for accurately estimating SOC in diverse agricultural areas remains a challenge. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors' impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation.

Keywords: factors influencing SOC estimation, remote sensing data, environmental variables, machine learning

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Authors: Basyir Yaman, Fades Br. Gultom

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The picture and pattern of Islamic education in the Prophet's period in Mecca and Medina is the history of the past that we need to bring back. The Basic Education Institute called Kuttab. Kuttab or Maktab comes from the word kataba which means to write. The popular Kuttab in the Prophet’s period aims to resolve the illiteracy in the Arab community. In Indonesia, this Institution has 25 branches; one of them is located in Semarang (i.e. Kuttab Al-Fatih). Kuttab Al-Fatih as a non-formal institution of Islamic education is reserved for children aged 5-12 years. The independently designed curriculum is a distinctive feature that distinguishes between Kuttab Al-Fatih curriculum and the formal institutional curriculum in Indonesia. The curriculum includes the faith and the Qur’an. Kuttab Al-Fatih has been licensed as a Community Activity Learning Center under the direct supervision and guidance of the National Education Department. Here, we focus to describe the implementation of curriculum Kuttab Al-Fatih Semarang (i.e. faith and al-Qur’an). After that, we determine the relevance between the implementation of the Kuttab Al-Fatih education system with the formal education system in Indonesia. This research uses literature review and field research qualitative methods. We obtained the data from the head of Kuttab Al-Fatih Semarang, vice curriculum, faith coordinator, al-Qur’an coordinator, as well as the guardians of learners and the learners. The result of this research is the relevance of education system in Kuttab Al-Fatih Semarang about education system in Indonesia. Kuttab Al-Fatih Semarang emphasizes character building through a curriculum designed in such a way and combines thematic learning models in modules.

Keywords: Islamic education system, implementation of curriculum, Kuttab Al-Fatih Semarang, formal education system, Indonesia

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Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

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Authors: Prakash, Vikash Kumar, Vinay Bansal, L. N. Das

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Dual biometrics is a subpart of multimodal biometrics, which refers to the use of a variety of modalities to identify and authenticate persons rather than just one. We limit the risks of mistakes by mixing several modals, and hackers have a tiny possibility of collecting information. Our goal is to collect the precise characteristics of iris and palmprint, produce a fusion of both methodologies, and ensure that authentication is only successful when the biometrics match a particular user. After combining different modalities, we created an effective strategy with a mean DI and EER of 2.41 and 5.21, respectively. A biometric system has been proposed.

Keywords: multimodal, fusion, palmprint, Iris, EER, DI

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Authors: Marju Ben Sayed, Shigeko Haruyama

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Among all kinds of natural disaster, the flood is a common feature in rapidly urbanizing Dhaka city. In this research, assessment of flood risk of Dhaka metropolitan area has been investigated by using an integrated approach of GIS, remote sensing and socio-economic data. The purpose of the study is to measure the flooding risk concerning with the flood protection embankment in big flooding events (1988, 1998 and 2004) and urbanization of Dhaka metropolitan zone. In this research, we considered the Dhaka city into two parts; East Dhaka (outside the flood protection embankment) and West Dhaka (inside the flood protection embankment). Using statistical data, we explored the socio-economic status of the study area population by comparing the density of population, land price and income level. We have drawn the cross section profile of the flood protection embankment into three different points for realizing the flooding risk in the study area, especially in the big flooding year (1988, 1998 and 2004). According to the physical condition of the study area, the land use/land cover map has been classified into five classes. Comparing with each land cover unit, historical weather station data and the socio-economic data, the flooding risk has been evaluated. Moreover, we compared between DEM data and each land cover units to find out the relationship with flood. It is expected that, this study could contribute to effective flood forecasting, relief and emergency management for a future flood event in Dhaka city.

Keywords: land use, land cover change, socio-economic, Dhaka city, GIS, flood

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Authors: M. N. Khajavi, S. Nasiri, E. Farokhi, M. R. Bavir

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Alcohol fuels are renewable, have low pollution and have high octane number; therefore, they are important as fuel in internal combustion engines. Percentage detection of these alcoholic fuels with gasoline is a complicated, time consuming, and expensive process. Nowadays, these processes are done in equipped laboratories, based on international standards. The aim of this research is to determine percentage detection of different fuels based on vibration analysis of engine block signals. By doing, so considerable saving in time and cost can be achieved. Five different fuels consisted of pure gasoline (G) as base fuel and combination of this fuel with different percent of ethanol and methanol are prepared. For example, volumetric combination of pure gasoline with 10 percent ethanol is called E10. By this convention, we made M10 (10% methanol plus 90% pure gasoline), E30 (30% ethanol plus 70% pure gasoline), and M30 (30% Methanol plus 70% pure gasoline) were prepared. To simulate real working condition for this experiment, the vehicle was mounted on a chassis dynamometer and run under 1900 rpm and 30 KW load. To measure the engine block vibration, a three axis accelerometer was mounted between cylinder 2 and 3. After acquisition of vibration signal, eight time feature of these signals were used as inputs to an Adaptive Neuro Fuzzy Inference System (ANFIS). The designed ANFIS was trained for classifying these five different fuels. The results show suitable classification ability of the designed ANFIS network with 96.3 percent of correct classification.

Keywords: internal combustion engine, vibration signal, fuel composition, classification, ANFIS

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Authors: Bahram Khan, Anderson Rocha Ramos, Rui R. Paulo, Fernando J. Velez

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With the growing demand for a new blend of applications, the users dependency on the internet is increasing day by day. Mobile internet users are giving more attention to their own experiences, especially in terms of communication reliability, high data rates and service stability on move. This increase in the demand is causing saturation of existing radio frequency bands. To address these challenges, researchers are investigating the best approaches, Carrier Aggregation (CA) is one of the newest innovations, which seems to fulfill the demands of the future spectrum, also CA is one the most important feature for Long Term Evolution - Advanced (LTE-Advanced). For this purpose to get the upcoming International Mobile Telecommunication Advanced (IMT-Advanced) mobile requirements (1 Gb/s peak data rate), the CA scheme is presented by 3GPP, which would sustain a high data rate using widespread frequency bandwidth up to 100 MHz. Technical issues such as aggregation structure, its implementations, deployment scenarios, control signal techniques, and challenges for CA technique in LTE-Advanced, with consideration of backward compatibility, are highlighted in this paper. Also, performance evaluation in macro-cellular scenarios through a simulation approach is presented, which shows the benefits of applying CA, low-complexity multi-band schedulers in service quality, system capacity enhancement and concluded that enhanced multi-band scheduler is less complex than the general multi-band scheduler, which performs better for a cell radius longer than 1800 m (and a PLR threshold of 2%).

Keywords: component carrier, carrier aggregation, LTE-advanced, scheduling

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Authors: Ahmad Ghanem, Mohamad Yasin, Mona Abdel Rehim, Fabrice Gouanve, Eliane Espuche

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Biodegradable polymers are of great interest, especially for biomedical and packaging applications. Current research efforts are focused on the development of biopolymers with the purpose of reducing the plastic pollution induced by the widely used in biodegradable polyolefins. The main challenge is focused on the elaboration of biopolymers having properties competitive to those of polyolefins. On the other hand, graphene oxide (GO), a graphene derivative, is characterized by the presence of several functional groups on the surface such as carboxylic, hydroxyl and epoxide. This feature enables modification of GO surface with different modifiers to obtain versatile surface properties and overcome the problem of graphene sheets aggregations during inclusion in a polymer matrix. In this context, poly (butylene succinate) (PBS) as promising biopolyester is modified through blending with different ratios of functionalized (GO) to improve its barrier properties. Modification of GO has been carried out using different hyperbranched polymeric structures in order to increase miscibility of the nanosheets in the hosting polymeric matrix. Films have been prepared from the modified PBS and their mechanical, thermal and gas barrier properties were investigated. The results reveal enhancement in the thermal and mechanical properties beside observed improvement of the barrier properties for the films prepared from the modified PBS. This improvement is related to the strong dependence on tortuosity effects of dispersion, exfoliation levels of fillers into the polymer matrix and interactions between the fillers and the polymer matrix.

Keywords: gas barrier properties, graphene oxide, food packaging, transport properties

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Authors: Mahya Naghipoor

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Purpose: Non-small cell lung cancer (NSCLC) is the most common lung cancer type. Epidermal growth factor receptor (EGFR) mutation is the main reason which causes NSCLC. Computed tomography (CT) is used for diagnosis and prognosis of lung cancers because of low price and little invasion. Semantic analyses of qualitative CT features are based on visual evaluation by radiologist. However, the naked eye ability may not assess all image features. On the other hand, radiomics provides the opportunity of quantitative analyses for CT images features. The aim of this review study was comparing accuracy of semantic and radiomics features in prognosis of EGFR mutation in NSCLC. Methods: For this purpose, the keywords including: non-small cell lung cancer, epidermal growth factor receptor mutation, semantic, radiomics, feature, receiver operating characteristics curve (ROC) and area under curve (AUC) were searched in PubMed and Google Scholar. Totally 29 papers were reviewed and the AUC of ROC analyses for semantic and radiomics features were compared. Results: The results showed that the reported AUC amounts for semantic features (ground glass opacity, shape, margins, lesion density and presence or absence of air bronchogram, emphysema and pleural effusion) were %41-%79. For radiomics features (kurtosis, skewness, entropy, texture, standard deviation (SD) and wavelet) the AUC values were found %50-%86. Conclusions: In conclusion, the accuracy of radiomics analysis is a little higher than semantic in prognosis of EGFR mutation in NSCLC.

Keywords: lung cancer, radiomics, computer tomography, mutation

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Authors: Itay Harlap

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Over the past decade, LGBT figures have become increasingly visible on Israeli television in its various channels and genres. In recent years, however, the representation of gays on Israeli television has undergone an interesting shift, whereby many television texts feature gay people as fathers. These texts, mostly news items and documentaries, usually present gay parenthood as a positive phenomenon. The question in paper is whether LGBT parenting (in reality and as representation) fated to be part of the homonormativity that characterizes the LGBT community in Israel, or can it be an alternative to the hegemonic discourse? This paper embraces a dialectical position and explores the tension between mainstream and radical, or homonormativity and queer politics in the specific Israeli Jewish context through a textual and discursive reading of a selection of television programs that revolve principally around gay parenting in Israel. The first part of this lecture addresses the cultural and social context that generated these representations, dealing with three key Israeli areas: The fertility cult, the evolution of the LGBT community, and the evolution of local television. The second part offers a queer reading of these ‘positive’ representations (mainly in special reports on the news and programs labeled as ‘documentaries’ by broadcasters) and highlight the possible price of the ‘bear hug’ given by Israeli media to gay parents. The last part focuses on a single case study, the TV serial drama Ima Veabaz, and suggests that this drama exposes the performative aspect of parenting and the connection between ethnicity and fertility, and offers an alternative to normative displays of gay parenting.

Keywords: fatherhood, heteronormativity, Israel, queer theory, television

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Authors: Assia Haif, Djelloul Achouri, Zineddine Soualili

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Introduction: Laparoscopic treatment of liver echinococcosis cyst has become popular. In parallel, the diagnostic approach of cystic liver lesions is based on the number of lesions and their distribution. The etiologies of cystic masses in children are different, and the role of imaging in their characterization and pre-therapeutic evaluation is essential. The main differential diagnoses of hepatic hydatid cysts can be discovered intraoperatively by minimally invasive surgery. Methods: The clinical data contained seven patients with hepatic cystic who underwent laparoscopic surgery in the Department of Pediatric Surgery, SETIF, Algeria, from 2015 to 2022. Results: Of reported seven patients, five are male, and the remaining two are female. Abdominal pain was the most frequent clinical signs. Biological parameters were within normal limits, Abdominal ultrasound, practiced in all cases, completed by abdominal computed tomography (CT), showed a hydatid cystic. For all patients, surgical procedures were performed under laparoscopy. Total cystectomy in four patients, fenestration or subtotal cystectomy in three patients, respectively. A histopathological feature confirmed the nature of the cysts. During the follow-up period, there was no recurrence. Conclusions: Laparoscopic liver surgery is a safe and effective approach, it is an alternative to conventional surgery and a reproducible method. Laparoscopic surgery approach should follow the same principals with those of open surgery. This surgical technique can rectify the diagnosis of hydatid cyst, the histopathological examination confirms the nature of the cystic lesion.

Keywords: children, cyst, echinococcosis, laparoscopic, liver

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Authors: Elizabeth O'Brien, Raegan Murphy

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Research suggests that high levels of empathy in individuals with partners with mental health difficulties can lead to improved outcomes for their partner while compromising their own mental health. Specifically, it is proposed that the affective dimension of empathy diminishes resilience to the distress of a partner, whereas cognitive empathy (CE) enhances it. The relationship between different empathy dimensions and psychological resilience measures has not been investigated in partners of people with depression. Psychological inflexibility (PI) is a construct that can be understood as distress intolerance and is suggested to be an important feature of psychological resilience. The current study, therefore, aimed to investigate the differential role of dispositional empathy dimensions in PI for people living with a partner with depression. A cross-sectional design was employed in which 148 participants living with a partner with depression and 45 participants for a comparison sample were recruited using online platforms. Participants completed online surveys with measures relating to demographics, empathy, and PI. Scores were compared between the study and comparison samples. The study sample scored significantly lower for CE and affective empathy (AE) and significantly higher for PI than the comparison sample. Exploratory and regression analyses were run to examine associations between variables within the study sample. Analyses revealed that CE predicted the resilience outcome whilst AE did not. These results suggest that interventions for partners of people with depression that bolster the CE dimension alone may improve mental health outcomes for both members of the couple relationship.

Keywords: affective empathy, cognitive empathy, depression, partners, psychological inflexibility

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Authors: Harish Krishnamurthy, Anna Lafontant, Ren Yi

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Unidentified Flying Objects (UFOs) have been an interesting topic for most enthusiasts and hence people all over the United States report such findings online at the National UFO Report Center (NUFORC). Some of these reports are a hoax and among those that seem legitimate, our task is not to establish that these events confirm that they indeed are events related to flying objects from aliens in outer space. Rather, we intend to identify if the report was a hoax as was identified by the UFO database team with their existing curation criterion. However, the database provides a wealth of information that can be exploited to provide various analyses and insights such as social reporting, identifying real-time spatial events and much more. We perform analysis to localize these time-series geospatial events and correlate with known real-time events. This paper does not confirm any legitimacy of alien activity, but rather attempts to gather information from likely legitimate reports of UFOs by studying the online reports. These events happen in geospatial clusters and also are time-based. We look at cluster density and data visualization to search the space of various cluster realizations to decide best probable clusters that provide us information about the proximity of such activity. A random forest classifier is also presented that is used to identify true events and hoax events, using the best possible features available such as region, week, time-period and duration. Lastly, we show the performance of the scheme on various days and correlate with real-time events where one of the UFO reports strongly correlates to a missile test conducted in the United States.

Keywords: time-series clustering, feature extraction, hoax prediction, geospatial events

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Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

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In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

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Authors: V. V. Lakshmi, Y. V. S. Annapurna

Abstract:

Urinary tract infection (UTI) is one of the most common infectious diseases seen in the community. Susceptible individuals experience multiple episodes, and progress to acute pyelonephritis or uro-sepsis or develop asymptomatic bacteriuria (ABU). Ability to cause extraintestinal infections depends on several virulence factors required for survival at extraintestinal sites. Presence of virulence phenotypes enhances the pathogenicity of these otherwise commensal organisms and thus augments its ability to cause extraintestinal infections, the most frequent in urinary tract infections(UTI). The present study focuses on detection of the virulence characters exhibited by the uropathogenic organism and most common factors exhibited in the local pathogens. A total of 700 isolates of E.coli and Klebsiella spp were included in the study. These were isolated from patients from local hospitals reported to be suffering with UTI over a period of three years. Isolation and identification was done based on Gram character and IMVIC reactions. Antibiotic sensitivity profile was carried out by disc diffusion method and multi drug resistant strains with MAR index of 0.7 were further selected.. Virulence features examined included their ability to produce exopolysaccharides, protease- gelatinase production, hemolysin production, haemagglutination and hydrophobicity test. Exopolysaccharide production was most predominant virulence feature among the isolates when checked by congo red method. The biofilms production examined by microtitre plates using ELISA reader confirmed that this is the major factor contributing to virulencity of the pathogens followed by hemolysin production

Keywords: Escherichia coli, Klebsiella sp, Uropathogens, Virulence features.

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Authors: Saleh Maina

Abstract:

There is an on-going debate among intellectuals and scholars of international relations and world politics over the direction which the world is heading particularly in the current era of globalization. On the one hand are adherents to the convergence thesis which is premised on the assumption that global social order is tending toward universalism which could translate into the possible end of the classical state system and the unification of world societies under a single and common ideological dispensation. The convergence thesis is hinged on the globalization process which is gradually reducing world societies into a 'global village'. On the other hand are intellectuals who hold the view that despite advances made in communication technology which appear to threaten the survival of the classical state system. Invariance, as expressed in the survival of the existing state system and the diverse social traditions in world societies, remain a realistic possibility contrary to the conclusions of the convergence thesis. The invariance thesis has been advanced by scholars like Samuel P. Huntington whose work on clash of civilizations suggests that world peace can only be sustained through the co-habitation of diverse civilizations across the world. The purpose of this paper is to examine both sides of the debate with the aim of making a realistic assessment on where world societies are headed, between convergence and invariance. Using the realist theory of international relations as our theoretical premise the paper argues that while there is sufficient ground to predict the future direction of world societies as headed towards some form of convergence, invariance as expressed in the co-existence of diverse civilizations will for a long time remain a major feature of the international system.

Keywords: convergence, invariance, clash of civilization, classical state system, universalism

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Authors: Md Omar Faruq Howlader, Tariq Pervez Sattar, Sandra Dudley

Abstract:

This paper describes the design process of a 200 MHz Ground Penetrating Radar (GPR) and a battery powered concrete vertical concrete surface climbing mobile robot. The key design feature is a miniaturized 200 MHz dipole antenna using additional radiating arms and procedure records a reduction of 40% in length compared to a conventional antenna. The antenna set is mounted in front of the robot using a servo mechanism for folding and unfolding purposes. The robot’s adhesion mechanism to climb the reinforced concrete wall is based on neodymium permanent magnets arranged in a unique combination to concentrate and maximize the magnetic flux to provide sufficient adhesion force for GPR installation. The experiments demonstrated the robot’s capability of climbing reinforced concrete wall carrying the attached prototype GPR system and perform floor-to-wall transition and vice versa. The developed GPR’s performance is validated by its capability of detecting and localizing an aluminium sheet and a reinforcement bar (rebar) of 12 mm diameter buried under a test rig built of wood to mimic the concrete structure environment. The present robotic GPR system proves the concept of feasibility of undertaking inspection procedure on large concrete structures in hazardous environments that may not be accessible to human inspectors.

Keywords: climbing robot, dipole antenna, ground penetrating radar (GPR), mobile robots, robotic GPR

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Authors: Junbeom Lee, Jaehyuck Cho, Wookyeong Jeong, Jonghan Won, Jungmin Hwang, Youngseok Song, Taikyeong Jeong

Abstract:

This paper investigates and analyzes meta-learning technologies that use multiple-cameras to monitor and check abnormal behavior in people in real-time in the area of healthcare fields. Advances in artificial intelligence and computer vision technologies have confirmed that cameras can be useful for individual health monitoring and abnormal behavior detection. Through this, it is possible to establish a system that can respond early by automatically detecting abnormal behavior of the elderly, such as patients and the elderly. In this paper, we use a technique called meta-learning to analyze image data collected from cameras and develop a commercial product to determine abnormal behavior. Meta-learning applies machine learning algorithms to help systems learn and adapt quickly to new real data. Through this, the accuracy and reliability of the abnormal behavior discrimination system can be improved. In addition, this study proposes a meta-learning-based abnormal behavior detection system that includes steps such as data collection and preprocessing, feature extraction and selection, and classification model development. Various healthcare scenarios and experiments analyze the performance of the proposed system and demonstrate excellence compared to other existing methods. Through this study, we present the possibility that camera-based meta-learning technology can be useful for monitoring and testing abnormal behavior in the healthcare area.

Keywords: artificial intelligence, abnormal behavior, early detection, health monitoring

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