Search results for: Leptokurtic feature

Authors: G. Murtaza, H. Faiza, M. Rafiq, S. Gul, F. Raza, Sarwat Anjum

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Our objective was to investigate whether and how extensively there is a correlation between aging in men, gonadotropin hormone regulation, and a decline in sperm parameters and whether it is possible to identify an age limit beyond which the decrease in sperm feature and hormonal regulation reaches statistical significance. A total of one hundred and twenty men (age: 20–50 years) were divided into two groups; each group contained 60 males (Group A with a young age of 20–35 years and Group B with an older age of 36–50 years) who visited the Center for Reproductive Medicine (CRM) in Peshawar General Hospital (PGH) Peshawar, Pakistan. Clinical assessment and sperm analysis were investigated. Hormone testing and semen analysis were carried out in accordance with World Health Organization (WHO) guidelines. Hormone tests, sperm morphology, and the total motile spermatozoa count (TMS) were computed. SPSS 20.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis. It was observed that the testosterone levels in Group A (mean = 3.770) and Group B (mean = 3.995) were comparable, with a significant P-value <0.005 in both age groups. Furthermore, similar levels are shown by follicle-stimulating hormone (FSH) (Group A mean = 19.73, Group B mean = 15.64) and luteinizing hormone (LH) (Group A mean = 12.25, Group B mean = 11.93) in both groups, with a significant P = <0.005. Sperm concentrations were most similar in Group A, with a mean of 4.44, and in Group B, with a mean of 4.42 and a significant P value of 0.005 in both groups. Additionally, it was discovered that sperm motility was higher in Group A, with a mean of 22.40 and a P-value of 0.052, which was non-significant when compared to Group B. Morphological differences were also observed in both age groups. This research found that advancing in male age does not affect sex hormone regulation; in contrast, the fraction of motile and morphologically normal spermatozoa decreases as male age increases, with the strongest evidence being when the age exceeds 40 years. To clarify the causes and clinical implications of these correlations, more research is necessary.

Keywords: gonadotropins, motility, spermatozoa, testosterone

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Anderson Chidi Amadioha, Promise Chidi Kenkwo, A. A. Markson

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Cassava (Manihot esculenta Crantz) is an important food and cash crop that provide cheap source of carbohydrate for food, feed and raw material for industries hence a commodity for feature economic development of developing countries. Despite the importance, its production potentials is undermined by disease agents that greatly reduce yield and render it unfit for human consumption and industrial use. Pathogenicity tests on fungal isolates from infected cassava revealed Aspergillus flavus, Rhizopus stolonifer, Aspergillus niger, and Trichodderma viride as rot-causing organisms. Water and ethanol extracts of Piper guineense, Ocimum graticimum, Cassia alata, and Tagetes erecta at 50% concentration significantly inhibited the radial growth of the pathogens in vitro and their development and spread in vivo. Low cassava rot incidence and severity was recorded when the extracts were applied before than after spray inoculating with spore suspension (1x105 spores/ml of distilled water) of the pathogenic organisms. The plant materials are readily available, and their extracts are biodegradable and cost effective. The fungitoxic potentials of extracts of these plant materials could be exploited as potent biopesticides in the management of postharvest fungal deterioration of cassava especially in developing countries where synthetic fungicides are not only scarce but also expensive for resource poor farmers who produce over 95% of the food consumed.

Keywords: cassava, biopesticides, in vitro, in vivo, pathogens, plant extracts

Procedia PDF Downloads 180

Authors: J. M. Counsell, Y. Khalid, M. J. Stewart

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Achieving nearly zero carbon heating continues to be identified by UK government analysis as an important feature of any lowest cost pathway to reducing greenhouse gas emissions. Heat currently accounts for 48% of UK energy consumption and approximately one third of UK’s greenhouse gas emissions. Heat Networks are being promoted by UK investment policies as one means of supporting hybrid heat pump based solutions. To this effect the RISE (Renewable Integrated and Sustainable Electric) heating system project is investigating how an all-electric heating sourceshybrid configuration could play a key role in long-term decarbonisation of heat.  For the purposes of this study, hybrid systems are defined as systems combining the technologies of an electric driven air source heat pump, electric powered thermal storage, a thermal vessel and micro-heat network as an integrated system.  This hybrid strategy allows for the system to store up energy during periods of low electricity demand from the national grid, turning it into a dynamic supply of low cost heat which is utilized only when required. Currently a prototype of such a system is being tested in a modern house integrated with advanced controls and sensors. This paper presents the virtual performance analysis of the system and its design for a micro heat network with multiple dwelling units. The results show that the RISE system is controllable and can reduce carbon emissions whilst being competitive in running costs with a conventional gas boiler heating system.

Keywords: gas boilers, heat pumps, hybrid heating and thermal storage, renewable integrated and sustainable electric

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Authors: Balogun R. Ayodeji, Adefisan E. Adesanya, Adeyewa Z. Debo, E. C. Okogbue

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Deep convective systems during active periods of the West African monsoon season have not been properly investigated over better temporal and spatial resolution in West Africa. Deep convective systems are investigated over seven climatic zones of the West African sub-region, which are; west-coast rainforest, dry rainforest, Nigeria-Cameroon rainforest, Nigeria savannah, Central African and South Sudan (CASS) Savannah, Sudano-Sahel, and Sahel, using data from Tropical Rainfall Measurement Mission (TRMM) Precipitation Feature (PF) database. The vertical structure of the convective systems indicated by the presence of at least one 40 dBZ and reaching (attaining) at least 1km in the atmosphere showed strong core (highest frequency (%)) of reflectivity values around 2 km which is below the freezing level (4-5km) for all the zones. Echoes are detected above the 15km altitude much more frequently in the rainforest and Savannah zones than the Sudano and Sahel zones during active periods in March-May (MAM), whereas during active periods in June-September (JJAS) the savannahs, Sudano and Sahel zones convections tend to reach higher altitude more frequently than the rainforest zones. The percentage frequencies of deep convection indicated that the occurrences of the systems are within the range of 2.3-2.8% during both March-May (MAM) and June-September (JJAS) active periods in the rainforest and savannah zones. On the contrary, the percentage frequencies were found to be less than 2% in the Sudano and Sahel zones, except during the active-JJAS period in the Sudano zone.

Keywords: active periods, convective system, frequency, reflectivity

Procedia PDF Downloads 152

Authors: Dorota Habrat

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In Polish law, the idea of the introduction of corporate responsibility for crimes is becoming more popular and creates a lot of questions. The need to introduce into the Polish legal system liability of corporate (collective entities) has resulted, among others, from the Polish Republic's international commitments, in particular related to membership in the European Union. The Act of 28 October 2002 on the liability of collective entities for acts prohibited under penalty is one of the example of adaptation of Polish law to Community law. Introduction to Polish law a criminal nature liability of corporations (legal persons) has resulted in a lot of controversy and lack of acceptance from both the scientific community as well as the judiciary. The responsibility of collective entities under the Act has a criminal nature. The main question concerns the ability of the collective entity to be brought to guilt under criminal law sense. Polish criminal law knows only the responsibility of individual persons. So far, guilt as a personal feature of action, based on the ability of the offender to feel in his psyche, could be considered only in relation to the individual person, while the said Act destroyed this conviction. Guilt of collective entity must be proven under at least one of the three possible forms: the guilt in the selection or supervision and so called organizational guilt. The next question is how the principle of proportionality in relation to criminal measures in response of collective entities should be considered. It should be remembered that the legal subjectivity of collective entities, including their rights and freedoms, is an emanation of the rights and freedoms of individual persons which create collective entities and through these entities implement their rights and freedoms. The adopted Act largely reflects the international legal regulations but also contains the unknown and original legislative solutions.

Keywords: criminal corporate responsibility, Polish criminal law, legislative solutions, Act of 28 October 2002

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Authors: Mohamed Ouazene

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The insulating properties of the polymers are widely used in electrical engineering for the production of insulators and various supports, as well as for the insulation of electric cables for medium and high voltage, etc. These polymeric materials have significant advantages both technically and economically. However, although the insulation with polymeric materials has advantages, there are also certain disadvantages such as the influence of the heat which can have a detrimental effect on these materials. Polyvinyl chloride (PVC) is one of the polymers used in a plasticized state in the cable insulation to medium and high voltage. The studied material is polyvinyl chloride (PVC 4000 M) from the Algerian national oil company whose formula is: Industrial PVC 4000 M is in the form of white powder. The test sample is a pastille of 1 mm thick and 1 cm in diameter. The consequences of increasing the temperature of a polymer are modifications; some of them are reversible and others irreversible [1]. The reversible changes do not affect the chemical composition of the polymer, or its structure. They are characterized by transitions and relaxations. The glass transition temperature is an important feature of a polymer. Physical aging of PVC is to maintain the material for a longer or shorter time to its glass transition temperature. The aim of this paper is to study this phenomenon by the method of thermally stimulated depolarization currents. Relaxations within the polymer have been recorded in the form of current peaks. We have found that the intensity decreases for more residence time in the polymer along its glass transition temperature. Furthermore, it is inferred from this work that the phenomenon of physical aging can have important consequences on the properties of the polymer. It leads to a more compact rearrangement of the material and a reconstruction or reinforcement of structural connections.

Keywords: depolarization currents, glass transition temperature, physical aging, polyvinyl chloride (PVC)

Procedia PDF Downloads 389

Authors: Li-Hua Luu, Alexis Doghmane, Abbas Farhat, Mohammad Sanayei, Pierre Philippe, Pablo Cuellar

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Suction buckets are promising innovative foundations for offshore wind turbines. They generally feature the shape of an inverted bucket and rely on a suction system as a driving agent for their installation into the seabed. Water is pumped out of the buckets that are initially placed to rest on the seabed, creating a net pressure difference across the lid that generates a seepage flow, lowers the soil resistance below the foundation skirt, and drives them effectively into the seabed. The stability of the suction mechanism as well as the possibility of a piping failure (i.e., localized fluidization within the internal soil plug) during their installation are some of the key questions that remain open. The present work deals with an experimental study of localized fluidization by suction within a fixed bucket partially embedded into a submerged artificial soil made of spherical beads. The transient process, from the onset of granular motion until reaching a stationary regime for the fluidization at the embedded bucket wall, is recorded using the combined optical techniques of planar laser-induced fluorescence and refractive index matching. To conduct a systematic study of the piping threshold for the seepage flow, we vary the beads size, the suction pressure, and the initial depth for the bucket. This experimental modelling, by dealing with erosion-related phenomena from a micromechanical perspective, shall provide qualitative scenarios for the local processes at work which are missing in the offshore practice so far.

Keywords: fluidization, micromechanical approach, offshore foundations, suction bucket

Procedia PDF Downloads 184

Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

Procedia PDF Downloads 327

Authors: Evan W. Krieger, Vijayan K. Asari, Saibabu Arigela

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Video imagery captured for real-time security and surveillance applications is typically captured in complex lighting conditions. These less than ideal conditions can result in imagery that can have underexposed or overexposed regions. It is also typical that the video is too low in resolution for certain applications. The purpose of security and surveillance video is that we should be able to make accurate conclusions based on the images seen in the video. Therefore, if poor lighting and low resolution conditions occur in the captured video, the ability to make accurate conclusions based on the received information will be reduced. We propose a solution to this problem by using image preprocessing to improve these images before use in a particular application. The proposed algorithm will integrate an intensity enhancement algorithm with a super resolution technique. The intensity enhancement portion consists of a nonlinear inverse sign transformation and an adaptive contrast enhancement. The super resolution section is a single image super resolution technique is a Fourier phase feature based method that uses a machine learning approach with kernel regression. The proposed technique intelligently integrates these algorithms to be able to produce a high quality output while also being more efficient than the sequential use of these algorithms. This integration is accomplished by performing the proposed algorithm on the intensity image produced from the original color image. After enhancement and super resolution, a color restoration technique is employed to obtain an improved visibility color image.

Keywords: dynamic range compression, multi-level Fourier features, nonlinear enhancement, super resolution

Procedia PDF Downloads 554

Authors: Olivier Delage, Hassan Bencherif, Alain Bourdier

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Signal decomposition approaches represent an important step in time series analysis, providing useful knowledge and insight into the data and underlying dynamics characteristics while also facilitating tasks such as noise removal and feature extraction. As most of observational time series are nonlinear and nonstationary, resulting of several physical processes interaction at different time scales, experimental time series have fluctuations at all time scales and requires the development of specific signal decomposition techniques. Most commonly used techniques are data driven, enabling to obtain well-behaved signal components without making any prior-assumptions on input data. Among the most popular time series decomposition techniques, most cited in the literature, are the empirical mode decomposition and its variants, the empirical wavelet transform and singular spectrum analysis. With increasing popularity and utility of these methods in wide ranging applications, it is imperative to gain a good understanding and insight into the operation of these algorithms. In this work, we describe all of the techniques mentioned above as well as their ability to denoise signals, to capture trends, to identify components corresponding to the physical processes involved in the evolution of the observed system and deduce the dimensionality of the underlying dynamics. Results obtained with all of these methods on experimental total ozone columns and rainfall time series will be discussed and compared

Keywords: denoising, empirical mode decomposition, singular spectrum analysis, time series, underlying dynamics, wavelet analysis

Procedia PDF Downloads 118

Authors: Sukhvinder Singh

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Punjabi language is related to the languages of New Indo-Aryan group that, in turn, is related to the branch of Indo-European language family. Punjabi language covers the areas of Western part (that is in Pakistan) and Eastern part (the Punjab state, Haryana, Delhi Himachal and J&K) and abroad (particularly Canada, USA, U.K. and Arab Emirates), where it is spoken widely. Besides India and Pakistan, Punjabi is the third language spoken in Canada after English, French having more than one hundred millions speakers worldwide. It is the fourth language spoken in Canada after English, French, and Chinese. It is also being taught as second language in most of the community school of British Columbia. The total number of Punjabi speakers is more than one hundred millions including India, Pakistan and abroad. Punjabi has a long tradition of linguistic tradition. A large number of scholars have studied Punjabi at different linguistic levels. Various studies are devoted to its special phonological characteristics, especially the tone, which has now started disappearing in favour of aspiration, a rare example of a language change in progress in its reversal direction. This process of language change in progress in reversal is dealt with in this paper a change towards a loss of linguistic indigeneity. The tone being a distinctive linguistic feature of Punjabi language is getting lost due to the increasing influence of Hindi and English particularly in the speech Urban Punjabi and Punjabi settled abroad. In this paper, an attempt has been made to discuss the sociolinguistics and sociology of Punjabi language and Punjab to trace the initiation and progression of this change towards a loss of Linguistic Indigeneity.

Keywords: language change in reversal, reaspiration, detonalization, new Indo-Aryan group

Procedia PDF Downloads 173

Authors: Shramana Mondal

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As a city is growing in population, sprawl, and complexity, use of public spaces increases variably and thus ensuring safety for the people becomes an utmost priority. While active monitoring measures may be necessary in some places, urban design can play a major role in devising self-policing and encourage active public life. This paper aims to explore the various spatial and psychological reasons for the occurrence of crime and the role of ‘urban design’ to address this issue. In this research, the principles of urban design are examined, as well as projected on actual site by addressing the issue with urban design principles. In this review the sociological, psychological, typological and morphological factors are addressed which affect the safety of a space and the possible framing guidelines, controls and urban design strategies are explored to address a safe neighborhood. On the basis of statistical survey, the residential and street network of Malviya Nagar in Delhi is chosen as the area of demonstration. The programs inhibit a safe neighborhood and a movement network that are addressed based on the four principles of natural surveillance, territoriality, community building, and connectivity. The paper concludes with a discussion of the urban design as an effective tool by creating an intense active zone with mixed use feature to ensure throughout activity and also ensuring safe pedestrian zone by introducing sense of community feeling and territoriality thus achieving active, useful and public friendly space.

Keywords: crime, public life, safety, urban design

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Authors: Talal S. Abdullah, Shahin Mehraban, Geraint Lodwig, Nicholas P. Lavery

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The defining property of Interstitial Free (IF) steels is formability, comprehensively measured using the Lankford coefficient (r-value) on uniaxial tensile test data. The contributing factors supporting this feature are grain size, orientation, and elemental additions. The processes that effectively modulate these factors are the casting procedure, hot rolling, and heat treatment. An existing methodology is well-practised in the steel Industry; however, large-scale production and experimentation consume significant proportions of time, money, and material. Introducing small-scale rapid alloy prototyping (RAP) as an alternative process would considerably reduce the drawbacks relative to standard practices. The aim is to finetune the existing fundamental procedures implemented in the industrial plant to adapt to the RAP route. IF material is remelted in the 80-gram coil induction melting (CIM) glovebox. To birth small grains, maximum deformation must be induced onto the cast material during the hot rolling process. The rolled strip must then satisfy the polycrystalline behaviour of the bulk material by displaying a resemblance in microstructure, hardness, and formability to that of the literature and actual plant steel. A successful outcome of this work is that small-scale RAP can achieve target compositions with similar microstructures and statistically consistent mechanical properties which complements and accelerates the development of novel steel grades.

Keywords: rapid alloy prototyping, plastic anisotropy, interstitial free, miniaturised tensile testing, formability

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Authors: Zhilin Wang, Zhihao Zheng, Linxin Liu

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This study proposes a low-code model-building approach that aims to simplify the development and deployment of artificial intelligence (AI) models. With an intuitive way to drag and drop and connect components, users can easily build complex models and integrate multiple algorithms for training. After the training is completed, the system automatically generates a callable model service API. This method not only lowers the technical threshold of AI development and improves development efficiency but also enhances the flexibility of algorithm integration and simplifies the deployment process of models. The core strength of this method lies in its ease of use and efficiency. Users do not need to have a deep programming background and can complete the design and implementation of complex models with a simple drag-and-drop operation. This feature greatly expands the scope of AI technology, allowing more non-technical people to participate in the development of AI models. At the same time, the method performs well in algorithm integration, supporting many different types of algorithms to work together, which further improves the performance and applicability of the model. In the experimental part, we performed several performance tests on the method. The results show that compared with traditional model construction methods, this method can make more efficient use, save computing resources, and greatly shorten the model training time. In addition, the system-generated model service interface has been optimized for high availability and scalability, which can adapt to the needs of different application scenarios.

Keywords: low-code, model building, artificial intelligence, algorithm integration, model deployment

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Authors: H. Boukhatem, L. Djouadi, H. Khalaf, R. M. Navarro, F. V. Ganzalez

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Synthetic organic dyes are used in various industries, such as textile industry, leather tanning industry, paper production, hair dye production, etc. Wastewaters containing these dyes may be harmful to the environment and living organisms. Therefore, it is very important to remove or degrade these dyes before discharging them into the environment. In addition to standard technologies for the degradation and/or removal of dyes, several new specific technologies, the so-called advanced oxidation processes (AOPs), have been developed to eliminate dangerous compounds from polluted waters. AOPs are all characterized by the same chemical feature: production of radicals (•OH) through a multistep process, although different reaction systems are used. These radicals show little selectivity of attack and are able to oxidize various organic pollutants due to their high oxidative capacity (reduction potential of HO• Eo = 2.8 V). Heterogeneous photocatalysis, as one of the AOPs, could be effective in the oxidation/degradation of organic dyes. A major advantage of using heterogeneous photocatalysis for this purpose is the total mineralization of organic dyes, which results in CO2, H2O and corresponding mineral acids. In this study, nanomaterials based on montmorillonite and CuxCd1-xS with different Cu concentration (0.3 < x < 0.7) were utilized for the degradation of the commercial cationic textile dye Methylene blue (MB), used as a model pollutant. The synthesized nanomaterials were characterized by fourier transform infrared (FTIR) and thermogravimetric-differential thermal analysis (TG–DTA). Test results of photocatalysis of methylene blue under UV-Visible irradiation show that the photoactivity of nanomaterials montmorillonite/ CuxCd1-xS increases with the increasing of Cu concentration. The kinetics of the degradation of the MB dye was described with the Langmuir–Hinshelwood (L–H) kinetic model.

Keywords: heterogeneous photocatalysis, methylene blue, montmorillonite, nanomaterial

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Authors: Maria Pavlova

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In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems.

Keywords: camera, object recognition, OpenCV, Raspberry

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Authors: Yelena Mazour-Matusevich, Jean-Robert Ancheta

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This study is based on more than fifteen years of experience of teaching a foreign language, in my case French, to the English-speaking students. It represents a qualitative research on foreign language learners’ reaction and their gains in terms of vocabulary and listening comprehension through repeatedly viewing foreign feature films with the original sountrack but without English subtitles. The initial idea emerged upon realization that the first challenge faced by my students when they find themselves in a francophone environment has been their lack of listening comprehension. Their inability to understand colloquial speech affects not only their academic performance, but their psychological health as well. To remedy this problem, I have designed and applied for many years my own teaching method based on one particular French film, exceptionally suited, for the reasons described in detail in the paper, for the intermediate-advanced level foreign language learners. This project, conducted together with my undergraduate assistant and mentoree J-R Ancheta, aims at showing how the paralinguistic features, such as characters’ facial expressions, settings, music, historical background, images provided before the actual viewing, etc., offer crucial support and enhance students’ listening comprehension. The study, based on students’ interviews, also offers special pedagogical techniques, such as ‘anticipatory’ vocabulary lists and exercises, drills, quizzes and composition topics that have proven to boost students’ performance. For this study, only the listening proficiency and vocabulary gains of the interviewed participants were assessed.

Keywords: comprehension, film, listening, subtitles, vocabulary

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Authors: Francesco Scarpati

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Because of their long history, ranging from ancient times to the present day, European cities feature many historical layers, whose single identities are represented by traces surviving in the urban design. However, urban transformations, in particular, the ones that have been produced by the property speculation phenomena of the 20th century, often compromised the readability of these traces, resulting in a loss of the historical identities of the single layers. The purpose of this research is, therefore, a reflection on the theme of the reenactment of the historical memory in the stratified European contexts and on how contemporary architecture can help to reveal past signs of the cities. The research work starts from an analysis of a series of emblematic examples that have already provided an original solution to the described problem, going from the architectural detail scale to the urban and landscape scale. The results of these analyses are then applied to the case study of the city of Naples, as an emblematic example of a stratified city, with an ancient Greek origin; a city where it is possible to read most of the traces of its transformations. Particular consideration is given to the trace of the medieval walls of the city, which a long time ago clearly divided the city itself from the outer fields, and that is no longer readable at the current time. Finally, solutions and methods of intervention are proposed to ensure that the trace of the walls, read as a boundary, can be revealed through the contemporary project.

Keywords: contemporary project, historic memory, historic urban contexts, medieval walls, naples, stratified cities, urban traces

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Authors: Hanania Nasan Shokry Abdelmasih

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The discipline of regulation is pretty complex and has its own terminology. other than written legal guidelines, there's also dwelling regulation, which refers to prison exercise. primary legal rules purpose at the happiness of individuals in social existence and feature different characteristics in unique branches including public or non-public regulation. on the other hand, law is a countrywide phenomenon. The law of 1 state and the legal device implemented at the territory of another state can be completely exceptional. individuals who are professionals in a specific discipline of regulation in a single united states may have inadequate know-how within the regulation of every other united states. today, similarly to the neighborhood nature of regulation, worldwide and even supranational regulation rules are implemented as a way to defend basic human values and make sure the protection of human rights around the sector. systems that offer algorithmic answers to prison problems using synthetic intelligence (AI) gear will perhaps serve to produce very meaningful consequences in phrases of human rights. but, algorithms to be used need to no longer be evolved with the aid of only pc professionals, however additionally want the contribution of folks who are familiar with law, values, judicial choices, and even the social and political culture of the society to which it'll provide answers. otherwise, even supposing the set of rules works perfectly, it may not be well suited with the values of the society in which it is applied. The present day traits involving using AI techniques in legal systems suggest that artificial law will come to be a brand new subject within the area of law.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Nikita Katyal, Shubham Jain

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In today’s competitive business environment, the success of campaigns relies not only on their creation but also on effectively reaching the right customers. Campaigns often feature products that customers may not have considered or are unaware of, including popular items. This research aims to enhance retailer sales by leveraging an efficient recommender system that reminds targeted customers to purchase their preferred products and suggests additional items they hadn’t initially considered during a campaign. Our focus is on utilizing the recommender system to identify potential customers for a curated set of products selected by the marketing team for a specific campaign. Communicating with all customers can be time-consuming and costly, and irrelevant messages may harm customer loyalty. Therefore, the primary objective is to strategically select the right customers for a campaign, increasing sales and reducing communication costs. This paper provides valuable insights into connecting with the right customer segments to optimize revenue generation for businesses. The analysis shows that high-value customers (those generating the highest revenue) contributed to increases in average basket size, while win-back customers (with low engagement) and about to churn customers (those at risk of attrition) improved the effectiveness of marketing contacts by increasing engagement and reducing churn. Targeted communication, focused on revenue, also enhanced the quality of the relationship between the customer and the firm, helping to lower churn rates by engaging customers with suitable campaigns. This research provides empirical evidence supporting the theoretical benefits of targeting the right customers for a campaign.

Keywords: recommendation, ALS, marketing campaigns, target customers, churn

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: Premjit K. Sanjram, Gagan Jakhotiya, Apoorv Goyal, Shanu Shukla

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In psychological research, laboratory experiments often face the trade-off issue between experimental control and mundane realism. With the advent of Immersive Virtual Environment Technology (IVET), this issue seems to be at bay. However there is a growing challenge within the IVET itself to design and develop system or software that captures the psychological phenomenon of everyday lives. One such phenomena that is of growing interest is ‘media-multitasking’ To aid laboratory researches in media-multitasking this paper introduces Query Task Modulator (QTM), a computerized experimentation system to study media-multitasking behavior in a controlled laboratory environment. The system provides a computerized platform in conducting an experiment for experimenters to study media-multitasking in which participants will be involved in a query task. The system has Instant Messaging, E-mail, and Voice Call features. The answers to queries are provided on the left hand side information panel where participants have to search for it and feed the information in the respective communication media blocks as fast as possible. On the whole the system will collect multitasking behavioral data. To analyze performance there is a separate output table that records the reaction times and responses of the participants individually. Information panel and all the media blocks will appear on a single window in order to ensure multi-modality feature in media-multitasking and equal emphasis on all the tasks (thus avoiding prioritization to a particular task). The paper discusses the development of QTM in the light of current techniques of studying media-multitasking.

Keywords: experimentation system, human performance, media-multitasking, query-task

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Authors: Priyanka Gupta, Bipin Kumar

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Recent developments in smart materials motivate researchers to create novel textile products for innovative and functional applications, which have several potential uses beyond the conventional. This study investigates the memory behavior of shape memory filaments integrated into a knitted textile structure. The research advances the knowledge of how these intelligent materials respond within textile structures. This integration may also open new avenues for developing smart fabrics with unique sensing and actuation capabilities. A shape memory filament and polyester yarn were knitted to produce a shape memory knitted fabric (SMF). Thermo-mechanical tensile test was carried out to quantify the memory behavior of SMF under different conditions. The experimental findings demonstrate excellent shape recovery (100%) and shape fixity up to 88% at different strains (20% and 60%) and temperatures (30 ℃ and 50 ℃). Experimental results reveal that memory filament behaves differently in a fabric structure than in its pristine condition at various temperatures and strains. The cycle test of SMF under different thermo-mechanical conditions indicated complete shape recovery with an increase in shape fixity. So, the utterly recoverable textile structure was achieved after a few initial cycles. These intelligent textiles are beneficial for the development of novel, innovative, and functional fabrics like elegant curtains, pressure garments, compression stockings, etc. In addition to fashion and medical uses, this unique feature may also be leveraged to build textile-based sensors and actuators.

Keywords: knitting, memory filament, shape memory, smart textiles, thermo-mechanical cycle

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Authors: M. Al-Majidi, A. Lampropoulos, A. Cundy

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Geopolymer (cement-free) concrete is the most promising green alternative to ordinary Portland cement concrete and other cementitious materials. While a range of different geopolymer concretes have been produced, a common feature of these concretes is heat curing treatment which is essential in order to provide sufficient mechanical properties in the early age. However, there are several practical issues with the application of heat curing in large-scale structures. The purpose of this study is to develop cement-free concrete without heat curing treatment. Experimental investigations were carried out in two phases. In the first phase (Phase A), the optimum content of water, polycarboxylate based superplasticizer contents and potassium silicate activator in the mix was determined. In the second stage (Phase B), the effect of ground granulated blast furnace slag (GGBFS) incorporation on the compressive strength of fly ash (FA) and Slag based geopolymer mixtures was evaluated. Setting time and workability were also conducted alongside with compressive tests. The results showed that as the slag content was increased the setting time was reduced while the compressive strength was improved. The obtained compressive strength was in the range of 40-50 MPa for 50% slag replacement mixtures. Furthermore, the results indicated that increment of water and superplasticizer content resulted to retarding of the setting time and slight reduction of the compressive strength. The compressive strength of the examined mixes was considerably increased as potassium silicate content was increased.

Keywords: fly ash, geopolymer, potassium silicate, slag

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Authors: Sophia Shi

Abstract:

Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

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Authors: Haruo Okabayashi

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The mutual understanding in conversation is very important for human relations. This study investigates the mental function of the formation of mutual understanding between two people in conversation using the embodied approach. Forty people participated in this study. They are divided into pairs randomly. Four conversation situations between two (make/listen to fun or pleasant talk, make/listen to regrettable talk) are set for four minutes each, and the finger plethysmogram (200 Hz) of each participant is measured. As a result, the attractors of the participants who reported “I did not understand my partner” show the collapsed shape, which means the fluctuation of their rhythm is too small to match their partner’s rhythm, and their cross correlation is low. The autonomic balance of both persons tends to resonate during conversation, and both LLEs tend to resonate, too. In human history, in order for human beings as weak mammals to live, they may have been with others; that is, they have brought about resonating characteristics, which is called self-organization. However, the resonant feature sometimes collapses, depending on the lifestyle that the person was formed by himself after birth. It is difficult for people who do not have a lifestyle of mutual gaze to resonate their biological signal waves with others’. These people have features such as anxiety, fatigue, and confusion tendency. Mutual understanding is thought to be formed as a result of cooperation between the features of self-organization of the persons who are talking and the lifestyle indicated by mutual gaze. Such an entanglement phenomenon is called a nonlinear relation. By this research, it is found that the formation of mutual understanding is expressed by the rhythm of a biological signal showing a nonlinear relationship.

Keywords: embodied approach, finger plethysmogram, mutual understanding, nonlinear phenomenon

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

Abstract:

Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Guilherme Henrique Gabriel, Lucí Hidalgo Nunes

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An important feature of rainfall regimes is the variability, which is subject to the atmosphere’s general and regional dynamics, geographical position and relief. Despite being inherent to the climate system, it can harshly impact virtually all human activities. In turn, global climate change has the ability to significantly affect smaller-scale rainfall regimes by altering their current variability patterns. In this regard, it is useful to know if regional climates are changing over time and whether it is possible to link these variations to climate change trends observed globally. This study is part of an international project (Metropole-FAPESP, Proc. 2012/51876-0 and Proc. 2015/11035-5) and the objective was to identify and evaluate possible changes in rainfall behavior in the state of São Paulo, southeastern Brazil, using rainfall data from 79 rain gauges for the last forty years. Cluster analysis and gamma distribution parameters were used for evaluating spatial and temporal trends, and the outcomes are presented by means of geographic information systems tools. Results show remarkable changes in rainfall distribution patterns in São Paulo over the years: changes in shape and scale parameters of gamma distribution indicate both an increase in the irregularity of rainfall distribution and the probability of occurrence of extreme events. Additionally, the spatial outcome of cluster analysis along with the gamma distribution parameters suggest that changes occurred simultaneously over the whole area, indicating that they could be related to remote causes beyond the local and regional ones, especially in a current global climate change scenario.

Keywords: climate change, cluster analysis, gamma distribution, rainfall

Procedia PDF Downloads 322