Search results for: feature matching

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

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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

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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: Vincent Andrew Cappellano

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In the early phases of critical infrastructure system design, translating distributed computing requirements to an architecture has risk given the multitude of approaches (e.g., cloud, edge, fog). In many systems, a single requirement for system uptime / availability is used to encompass the system’s intended operations. However, when architected systems may perform to those availability requirements only during normal operations and not during component failure, or during outages caused by adversary attacks on critical infrastructure (e.g., physical, cyber). System designers lack a structured method to evaluate availability requirements against candidate system architectures through deep degradation scenarios (i.e., normal ops all the way down to significant damage of communications or physical nodes). This increases risk of poor selection of a candidate architecture due to the absence of insight into true performance for systems that must operate as a piece of critical infrastructure. This research effort proposes a process to analyze critical infrastructure system availability requirements and a candidate set of systems architectures, producing a metric assessing these architectures over a spectrum of degradations to aid in selecting appropriate resilient architectures. To accomplish this effort, a set of simulation and evaluation efforts are undertaken that will process, in an automated way, a set of sample requirements into a set of potential architectures where system functions and capabilities are distributed across nodes. Nodes and links will have specific characteristics and based on sampled requirements, contribute to the overall system functionality, such that as they are impacted/degraded, the impacted functional availability of a system can be determined. A machine learning reinforcement-based agent will structurally impact the nodes, links, and characteristics (e.g., bandwidth, latency) of a given architecture to provide an assessment of system functional uptime/availability under these scenarios. By varying the intensity of the attack and related aspects, we can create a structured method of evaluating the performance of candidate architectures against each other to create a metric rating its resilience to these attack types/strategies. Through multiple simulation iterations, sufficient data will exist to compare this availability metric, and an architectural recommendation against the baseline requirements, in comparison to existing multi-factor computing architectural selection processes. It is intended that this additional data will create an improvement in the matching of resilient critical infrastructure system requirements to the correct architectures and implementations that will support improved operation during times of system degradation due to failures and infrastructure attacks.

Keywords: architecture, resiliency, availability, cyber-attack

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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

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Authors: Baoshan Wang

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The present study investigates how music theory has developed across different countries due to their diverse histories, religions, and cultural differences. It is unknown how these various factors may contribute to differences in music theory across countries. Therefore, we examine the differences between China and Greece, which have developed unique music theories over time. Specifically, our analysis looks at musical notation and scales. For example, Tonal music originates from Greece, which harbors quite complex notation and scaling. There exist seven notes in each scale within seven modes of scales. Each mode of the diatonic scale has a unique temperament, two of which are most commonly used in modern music. In contrast, we find that Chinese music has only five notes in its scales. Interestingly, a unique feature of Chinese music theory is that there is no half-step, resulting in a highly divergent and culture-specific sound. Fascinatingly, these differences may arise from the contrasting ways that Western and Eastern musicians perceive music. While Western musicians tend to believe in music “without borders,” Eastern musicians generally embrace differing perspectives. Yet, the vast majority of colleges or music conservatories teach the borderless theory of Western music, which renders the music educational system incomplete. This is critically important because learning music is not simply a profession for musicians. Rather, it is an intermediary to facilitate understanding and appreciation for different countries’ cultures and religions. Education is undoubtedly the optimal mode to promote different countries’ music theory so people across the world can learn more about music and, in turn, each other. Even though Western music theory is predominantly taught, it is crucial we also pursue an understanding of other countries’ music because their unique aspects contribute to the systematic completeness of Music Theory in its entirety.

Keywords: culture, development, music theory, music history, religion, western music

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Authors: Mounia El Jaouhari, Mira Goral, Samir Diouny

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Introduction: Previous research has indicated that language impairments are recognized as a feature of many neurodegenerative disorders, including non-language-led dementia subtypes such as Alzheimer´s disease (AD). In this preliminary study, the focal aim is to quantify the semantic content of naming and connected speech samples of Moroccan patients diagnosed with AD using two tasks taken from the culturally adapted and validated Moroccan version of the Boston Diagnostic Aphasia Examination. Methods: Five individuals with AD and five neurologically healthy individuals matched for age, gender, and education will participate in the study. Participants with AD will be diagnosed on the basis of the Moroccan version of the Diagnostic and Statistial Manual of Mental Disorders (DSM-4) screening test, the Moroccan version of the Mini Mental State Examination (MMSE) test scores, and neuroimaging analyses. The participants will engage in two tasks taken from the MDAE-SF: 1) Picture description and 2) Naming. Expected findings: Consistent with previous studies conducted on English speaking AD patients, we expect to find significant word production and retrieval impairments in AD patients in all measures. Moreover, we expect to find category fluency impairments that further endorse semantic breakdown accounts. In sum, not only will the findings of the current study shed more light on the locus of word retrieval impairments noted in AD, but also reflect the nature of Arabic morphology. In addition, the error patterns are expected to be similar to those found in previous AD studies in other languages.

Keywords: alzheimer's disease, anomia, connected speech, semantic impairments, moroccan arabic

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Authors: Obiakor Casmir Uchenna, Ikegbunam Peter Chierike, Ezeja Perpetual Chisom

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Irregularities have been a major feature of the Nigerian political activities since 1999. The rate at which such impunities thrive in the country has made elections grossly unacceptable among the people because the outcomes have never reflected the wish of the masses. Conscious of this, citizens have subscribed to the use of social media in exposing the ugly faces of the country’s elections which have always been against the less privileged. This study is an exploration of the relationship between exposure to social media shared video-clips and the respondents’ perception of the 2023 presidential election in Nigeria. The general objective of the study is to find out what the respondents make of the election as a result of the video-clips shared on different social media platforms showing electoral irregularities. The study adopted survey research method in studying 378 university undergraduates from NAU, COOU and Paul University selected through purposive sampling technique. The study was premised on the theoretical provision of violation of expectation theory. Findings revealed that the respondents are well exposed to different video-clips showing irregularities on the election. It was also found that the respondents have negative perception of the election. It was concluded that electoral umpire, the government in power and the security apparatus violated the respondents’ expectation from the election based on the pre-election promises made to the citizens. It was recommended among others, that Nigeria must strengthen the various institutions responsible for the conduct of elections if violence will not be made the best option for the poor masses.

Keywords: social media shared video-clips, exposure, irregularities, elections, audience perception, outcome

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Authors: Adrien Pyskir, Manuel Collet, Zoran Dimitrijevic, Claude-Henri Lamarque

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During the last decades, great efforts have been made to reduce acoustic and vibrational disturbances in transportations, as it has become a key feature for comfort. Today, isolation and design have neutralized most of the troublesome vibrations, so that cars are quieter and more comfortable than ever. However, some problems remain unsolved, in particular concerning low-frequency isolation and the frequency-dependent stiffening of materials like rubber. To sum it up, a balance has to be found between a high static stiffness to sustain the vibration source’s mass, and low dynamic stiffness, as wideband as possible. Systems meeting these criteria are yet to be designed. We thus investigated solutions inspired by metamaterials to control efficiently low-frequency wave propagation. Structures exhibiting a negative Poisson ratio, also called auxetic structures, are known to influence the propagation of waves through beaming or damping. However, their stiffness can be quite peculiar as well, as they can present regions of zero stiffness on the stress-strain curve for compression. In addition, auxetic materials can be easily adapted in many ways, inducing great tuning potential. Using finite element software COMSOL Multiphysics, a resonant design has been tested through statics and dynamics simulations. These results are compared to experimental results. In particular, the bandgaps featured by these structures are analyzed as a function of design parameters. Great stiffness properties can be observed, including low-frequency dynamic stiffness loss and broadband transmission loss. Such features are very promising for practical isolation purpose, and we hope to adopt this kind of metamaterial into an effective industrial damper.

Keywords: auxetics, metamaterials, structural dynamics, vibration isolation

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Authors: Kyung-Tae Lee, Li Li Wang, Kwang-Woo Jung, Yong-Sun Bahn

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Microtubules are involved in mechanical support, cytoplasmic organization as well as in a number of cellular processes by interacting with diverse microtubule-associated proteins (MAPs), such as plus-end tracking proteins, motor proteins, and tubulin-folding cofactors. A common feature of these proteins is the presence of a cytoskeleton-associated protein-glycine-rich (CAP-Gly) domain, which is evolutionarily conserved and generally considered to bind to α-tubulin to regulate functions of microtubules. However, there has been a dearth of research on CAP-Gly proteins in fungal pathogens, including Cryptococcus neoformans, which causes fatal meningoencephalitis globally. In this study, we identified five CAP-Gly proteins encoding genes in C. neoformans. Among these, Cgp1, encoded by CNAG_06352, has a unique domain structure that has not been reported before in other eukaryotes. Supporting the role of Cpg1 in microtubule-related functions, we demonstrate that deletion or overexpression of CGP1 alters cellular susceptibility to thiabendazole, a microtubule destabilizer, and Cgp1 is co-localized with cytoplasmic microtubules. Related to the cellular functions of microtubules, Cgp1 also governs maintenance of membrane stability and genotoxic stress responses. Furthermore, we demonstrate that Cgp1 uniquely regulates sexual differentiation of C. neoformans with distinct roles in the early and late stage of mating. Our domain analysis reveals that the CAP-Gly domain plays major roles in all the functions of Cgp1. Finally, the cgp1Δ mutant is attenuated in virulence. In conclusion, this novel CAP-Gly protein, Cgp1, has pleotropic roles in regulating growth, stress responses, differentiation and pathogenicity of C. neoformans.

Keywords: human fungal pathogen, CAP-Glycine protein, microtubule, meningoencephalitis

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Authors: Mahmoud S Gewaily, Atif Hasan, Mohamed Kassab, Ali A. Mansour

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The current study was carried out on the nose of seventeenth healthy adult camels. Specimens were collected from slaughter houses then fixed, dissected and photographed. For ultra structural studies, fresh samples were fixed in different fixatives and prepared for examination by light, scanning and electron microscopes. Grossly, nose of the camel had narrow nostrils, slit like in outline. In the nasal cavity, the nasal vestibule was narrow and has scanty dorsal and lateral cartilaginous support. The Nasal conchae (dorsal, middle and ventral) enclosed the dorsal, middle conchal sinuses and no ventral conchal sinus; instead there was recess and bull a. The ethmoidal conchae (8 in number) were noticeably fewer than in the other domestic animals like ox and horse. The olfactory mucosa was restricted to a small area covering the caudal parts of the ethmoidal conchae. The lining epithelium of the nasal cavity changes gradually from stratified squamous epithelium in the nasal vestibule to pseudo stratified columnar ciliated in the respiratory region and finally, olfactory epithelium covering the caudal parts of the ethmoidal conchae. In the dromedary camel, a special feature was the presence of dense and relatively long hair covering the nostrils and the rostral part of the nasal vestibule. In conclusion, the anatomical features of the nose of the dromedary camel, especially in its rostral parts enable this animal to breathe properly in the sandy dry weather.

Keywords: camel nose, anatomy, dromedary camel, nasal vestibule

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Hadri Hussain, M.Nasir Ibrahim, Chee-Ming Ting, Mariani Idroas, Fuad Numan, Alias Mohd Noor

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A biometric security system has become an important application in client identification and verification system. A conventional biometric system is normally based on unimodal biometric that depends on either behavioural or physiological information for authentication purposes. The behavioural biometric depends on human body biometric signal (such as speech) and biosignal biometric (such as electrocardiogram (ECG) and phonocardiogram or heart sound (HS)). The speech signal is commonly used in a recognition system in biometric, while the ECG and the HS have been used to identify a person’s diseases uniquely related to its cluster. However, the conventional biometric system is liable to spoof attack that will affect the performance of the system. Therefore, a multimodal biometric security system is developed, which is based on biometric signal of ECG, HS, and speech. The biosignal data involved in the biometric system is initially segmented, with each segment Mel Frequency Cepstral Coefficients (MFCC) method is exploited for extracting the feature. The Hidden Markov Model (HMM) is used to model the client and to classify the unknown input with respect to the modal. The recognition system involved training and testing session that is known as client identification (CID). In this project, twenty clients are tested with the developed system. The best overall performance at 44 kHz was 93.92% for ECG and the worst overall performance was ECG at 88.47%. The results were compared to the best overall performance at 44 kHz for (20clients) to increment of clients, which was 90.00% for HS and the worst overall performance falls at ECG at 79.91%. It can be concluded that the difference multimodal biometric has a substantial effect on performance of the biometric system and with the increment of data, even with higher frequency sampling, the performance still decreased slightly as predicted.

Keywords: electrocardiogram, phonocardiogram, hidden markov model, mel frequency cepstral coeffiecients, client identification

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Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Bijay Kumar Sahoo

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An important feature of InₓGa₁-ₓN/GaN heterostructures is strong built-in polarization (BIP) electric field at the hetero-interface due to spontaneous (sp) and piezoelectric (pz) polarizations. The intensity of this electric field reaches several MV/cm. This field has profound impact on optical, electrical and thermal properties. In this work, the effect of BIP field on thermal conductivity of InₓGa₁-ₓN/GaN heterostructure has been investigated theoretically. The interaction between the elastic strain and built in electric field induces additional electric polarization. This additional polarization contributes to the elastic constant of InₓGa₁-ₓN alloy. This in turn modifies material parameters of InₓGa₁-ₓN. The BIP mechanism enhances elastic constant, phonon velocity and Debye temperature and their bowing constants in InₓGa₁-ₓN alloy. These enhanced thermal parameters increase phonon mean free path which boost thermal conduction process. The thermal conductivity (k) of InxGa1-xN alloy has been estimated for x=0, 0.1, 0.3 and 0.9. Computation finds that irrespective of In content, the room temperature k of InₓGa₁-ₓN/GaN heterostructure is enhanced by BIP mechanism. Our analysis shows that at a certain temperature both k with and without BIP show crossover. Below this temperature k with BIP field is lower than k without BIP; however, above this temperature k with BIP field is significantly contributed by BIP mechanism leading to k with BIP field become higher than k without BIP field. The crossover temperature is primary pyroelectric transition temperature. The pyroelectric transition temperature of InₓGa₁-ₓN alloy has been predicted for different x. This signature of pyroelectric nature suggests that thermal conductivity can reveal pyroelectricity in InₓGa₁-ₓN alloy. The composition dependent room temperature k for x=0.1 and 0.3 are in line with prior experimental studies. The result can be used to minimize the self-heating effect in InₓGa₁-ₓN/GaN heterostructures.

Keywords: built-in polarization, phonon relaxation time, thermal properties of InₓGa₁-ₓN /GaN heterostructure, self-heating

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Authors: Francisco Pedroza-Montero, Jesús Naín Pedroza-Montero, Diego Soto-Puebla, Osiris Alvarez-Bajo, Beatriz Castaneda, Sofía Navarro-Espinoza, Martín Pedroza-Montero

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Nanodiamonds have been widely studied for their physical properties, including chemical inertness, biocompatibility, optical transparency from the ultraviolet to the infrared region, high thermal conductivity, and mechanical strength. In this work, we studied how the fluorescence spectrum of nanodiamonds quenches concerning the concentration in aqueous solutions systematically ranging from 0.1 to 10 mg/mL. Our results demonstrated a non-linear fluorescence quenching as the concentration increases for both of the NV zero-phonon lines; the 5 mg/mL concentration shows the maximum fluorescence emission. Furthermore, this behaviour is theoretically explained as an electronic recombination process that modulates the intensity in the NV centres. Finally, to gain more insight, the FRET methodology is used to determine the fluorescence efficiency in terms of the fluorophores' separation distance. Thus, the concentration level is simulated as follows, a small distance between nanodiamonds would be considered a highly concentrated system, whereas a large distance would mean a low concentrated one. Although the 5 mg/mL concentration shows the maximum intensity, our main interest is focused on the concentration of 0.5 mg/mL, which our studies demonstrate the optimal human cell viability (99%). In this respect, this concentration has the feature of being as biocompatible as water giving the possibility to internalize it in cells without harming the living media. To this end, not only can we track nanodiamonds on the surface or inside the cell with excellent precision due to their fluorescent intensity, but also, we can perform thermometry tests transforming a fluorescence contrast image into a temperature contrast image.

Keywords: nanodiamonds, fluorescence spectroscopy, concentration, bioimaging, thermometry

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Authors: Tingyu Zhang

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This paper utilizes the Super-SBM model to assess water resources green efficiency (WRGE) among provinces in China and investigate its spatial and temporal features, based on the characteristic framework of “economy-environment-society.” The social network analysis is employed to examine the network pattern and spatial interaction of WRGE. Further, the quadratic assignment procedure method is utilized for examining the influencing factors of the spatial association of WRGE regarding “relationship.” The study reveals that: (1) the spatial distribution of WRGE demonstrates a distribution pattern of Eastern>Western>Central; (2) a remarkable spatial association exists among provinces; however, no strict hierarchical structure is observed. The internal structure of the WRGE network is characterized by the feature of "Eastern strong and Western weak". The block model analysis discovers that the members of the “net spillover” and “two-way spillover” blocks are mostly in the eastern and central provinces; “broker” block, which plays an intermediary role, is mostly in the central provinces; and members of the “net beneficiary” block are mostly in the western region. (3) Differences in economic development, degree of urbanization, water use environment, and water management have significant impacts on the spatial connection of WRGE. This study is dedicated to the realization of regional linkages and synergistic enhancement of WRGE, which provides a meaningful basis for building a harmonious society of human and water coexistence.

Keywords: water resources green efficiency, super-SBM model, social network analysis, quadratic assignment procedure

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Authors: Hassan Bohra, Mingfeng Wang

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Conjugated porous polymers (CPPs) are amorphous, insoluble and highly robust organic semiconductors that have been largely synthesized by traditional transition-metal catalyzed reactions. The distinguishing feature of CPP materials is that they combine microporosity and high surface areas with extended conjugation, making them ideal for versatile applications such as separation, catalysis and energy storage. By applying a modular approach to synthesis, chemical and electronic properties of CPPs can be tailored for specific applications making these materials economical alternatives to inorganic semiconductors. Direct arylation - an environmentally benign alternative to traditional polymerization reactions – is one such reaction that extensively over the last decade for the synthesis of linear p-conjugated polymers. In this report, we present the synthesis and characterization of a new series of robust conjugated porous polymers synthesized by facile direct arylation polymerization of thiophene-flanked acceptor building blocks with multi-brominated aryls with different geometries. We observed that the porosities and morphologies of the polymers are determined by the chemical structure of the aryl bromide used. Moreover, good control of the optical bandgap in the range 2.53 - 1.3 eV could be obtained by using different building blocks. Structure-property relationships demonstrated in this study suggest that direct arylation polymerization is an attractive synthetic tool for the rational design of porous organic materials with tunable photo-physical properties for applications in photocatalysis, energy storage and conversion.

Keywords: direct arylation, conjugated porous polymers, triazine, photocatalysis

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Authors: R. Geetha, J. Arunkumar, P. Gopal, D. Loganathan, K. Pavithra, C. Vikashini

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Mobile Number Portability is an attempt to switch over from one network to another network facility for mobile based on applications. This facility is currently not available for mobile handsets. This application is intended to assist the mobile network and its service customers in understanding the criteria; this will serve as a universal set of requirements which must be met by the customers. This application helps the user's network portability. Accessing permission from the network provider to enable services to the user and utilizing the available network signals. It is enabling the user to make a temporary switch over to other network. The main aim of this research work is to adapt multiple networks at the time of no network coverage. It can be accessed at rural and geographical areas. This can be achieved by this mobile application. The application is capable of temporary switch over between various networks. With this application both the service provider and the network user are benefited. The service provider is benefited by charging a minimum cost for utilizing other network. It provides security in terms of password that is unique to avoid unauthorized users and to prevent loss of balance. The goal intended to be attained is a complete utilization of available network at significant situations and to provide feature that satisfy the customer needs. The temporary switch over is done to manage emergency calls when user is in rural or geographical area, where there will be a very low network coverage. Since people find it trend in using Android mobile, this application is designed as an Android applications, which can be freely downloaded and installed from Play store. In the current scenario, the service provider enables the user to change their network without shifting their mobile network. This application affords a clarification for users while they are jammed in a critical situation. This application is designed by using Android 4.2 and SQLite Version3.

Keywords: mobile number, random number, alarm, imei number, call

Procedia PDF Downloads 344

Authors: Kanika S. Raheja, A. Pandey, Shaila Bahl, Pratik Kumar, S. P. Lochab

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The subject of undertaking for this paper is the study of BaSO4 nanophosphor doped with Europium in which mainly the concentration of the rare earth impurity Eu (0.05, 0.1, 0.2, 0.5, and 1 mol %) has been varied. A comparative study of the thermoluminescence(TL) properties of the given nanophosphor has also been done using a well-known standard dosimetry material i.e. TLD-100.Firstly, a number of samples were prepared successfully by the chemical co-precipitation method. The whole lot was then compared to a well established standard material (TLD-100) for its TL sensitivity property. BaSO4:Eu ( 0.2 mol%) showed the highest sensitivity out of the lot. It was also found that when compared to the standard TLD-100, BaSo4:Eu (0.2mol%) showed surprisingly high sensitivity for a large range of doses. The TL response curve for all prepared samples has also been studied over a wide range of doses i.e 10Gy to 2kGy for gamma radiation. Almost all the samples of BaSO4:Eu showed a remarkable linearity for a broad range of doses, which is a characteristic feature of a fine TL dosimeter. The graph remained linear even beyond 1kGy for gamma radiation. Thus, the given nanophosphor has been successfully optimised for the concentration of the dopant material to achieve its highest TL sensitivity. Further, the comparative study with the standard material revealed that the current optimised sample shows an astonishingly better TL sensitivity and a phenomenal linear response curve for an incredibly wide range of doses for gamma radiation (Co-60) as compared to the standard TLD-100, which makes the current optimised BaSo4:Eu quite promising as an efficient gamma radiation dosimeter. Lastly, the present phosphor has been optimised for its annealing temperature to acquire the best results while also studying its fading and reusability properties.

Keywords: gamma radiation, nanoparticles, radiation dosimetry, thermoluminescence

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Authors: Babatunde Olumide Olawale, Oyebode Olumide Oyediran

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The security safe is a place or building where classified document and precious items are kept. To prevent unauthorised persons from gaining access to this safe a lot of technologies had been used. But frequent reports of an unauthorised person gaining access into security safes with the aim of removing document and items from the safes are pointers to the fact that there is still security gap in the recent technologies used as access control for the security safe. In this paper we try to solve this problem by developing a multimodal biometric system for physical access control into a security safe using face and voice recognition. The safe is accessed by the combination of face and speech pattern recognition and also in that sequential order. User authentication is achieved through the use of camera/sensor unit and a microphone unit both attached to the door of the safe. The user face was captured by the camera/sensor while the speech was captured by the use of the microphone unit. The Scale Invariance Feature Transform (SIFT) algorithm was used to train images to form templates for the face recognition system while the Mel-Frequency Cepitral Coefficients (MFCC) algorithm was used to train the speech recognition system to recognise authorise user’s speech. Both algorithms were hosted in two separate web based servers and for automatic analysis of our work; our developed system was simulated in a MATLAB environment. The results obtained shows that the developed system was able to give access to authorise users while declining unauthorised person access to the security safe.

Keywords: access control, multimodal biometrics, pattern recognition, security safe

Procedia PDF Downloads 311

Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Meng-Chyi Wu, Bonn Lin, Jyun-Hao Liao, Chein-Ju Chen, Yu-Cheng Jhuang, Mau-Phon Houng, Fang-Hsing Wang, Min-Chu Liu, Cheng-Fu Yang, Cheng-Shong Hong

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Since the use of wireless communications has become critical nowadays, the available RF spectrum has become limited. Ultraviolet (UV) communication system can alleviate the spectrum constraint making UV communication system a potential alternative to future communication demands. Also, UV links can provide faster communication rate and can be used in combination with existing RF communication links, providing new communications diversity with higher user capacity. The UV region of electromagnetic spectrum has been of interest to detector, imaging and communication technologies because the stratospheric ozone layer effectively absorbs some solar UV radiation from reaching the earth surface. The wavebands where most of UV radiation is absorbed by the ozone are commonly known as the solar blind region. By operating in UV-C band (200-280 nm) the communication system can minimize the transmission power consumption since it will have less radiation noise. UV communication uses the UV ray as the medium. Electric signal is carried on this band after being modulated and then be transmitted within the atmosphere as channel. Though the background noise of UV-C communication is very low owing to the solar-blind feature, it leads to a large propagation loss. The 370 nm UV provides a much lower propagation loss than that the UV-C does and the recent device technology for UV source on this band is more mature. The fabricated 370 nm AlGaN light-emitting diodes (LEDs) with an aperture size of 45 m exhibit a modulation bandwidth of 165 MHz at 30 mA and a high power of 7 W/cm2 at 230 A/cm2. In order to solve the problem of low power in single UV LED, a UV LED array is presented in.

Keywords: ultraviolet (UV) communication, light-emitting diodes (LEDs), modulation bandwidth, LED array, 370 nm

Procedia PDF Downloads 393

Authors: Indira Sinha

Abstract:

According to the Oxford Advanced Learner's English Dictionary of Current English, environment is the complex of physical, chemical and biotic factors that act upon an organism or an ecological community and ultimately determines its form and survival. It is defined as conditions and circumstances which are affecting people's lives. The meaning of environmental degradation is the degradation of the environment through depletion of resources such as air, water and soil and the destruction of ecosystems and extinction of wildlife. Globalization is a significant feature of recent world history. The aim of this phenomenon is to integrate societies, economies and cultures through a common link of trading policies, technology and communication. Undoubtedly it has opened up the world economy at a very high speed but at the same time it has an adverse impact on the environment. The purpose of the present study is to investigate the impact of globalization on the environmental conditions. An overview of what the forces of globalization have in store for the environment with constructing large number of industries and destroying large forests lands will be given in this paper. The forces of globalization have created many serious environmental problems like high temperature, extinction of many species of plant and animal and outlet of poisonous chemicals from industries. The revelation of this study is that in case of developing economics these problems are more critical. In developing countries like India many factories are built with less environmental regulations, while developed economies maintain positive environmental practices. The present study is a micro level study which aims to employ a combination of theoretical, descriptive, empirical and analytical approach in addition to the time tested case method.

Keywords: globalization, trade policies, environmental degradation, developing economies, large industries

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Authors: G. Singh, H.Schuster, U. Füssel

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The stability of electric arc and durability of electrode tip used in Tungsten Inert Gas (TIG) welding demand a metallurgical study about the chemical transport mechanism of emitter oxide particles in tungsten electrode during its real welding conditions. The tungsten electrodes doped with emitter oxides of rare earth oxides such as La₂O₃, Th₂O₃, Y₂O₃, CeO₂ and ZrO₂ feature a comparatively lower work function than tungsten and thus have superior emission characteristics due to lesser surface temperature of the cathode. The local change in concentration of these emitter particles in tungsten electrode due to high temperature diffusion (chemical transport) can change its functional properties like electrode temperature, work function, electron emission, and stability of the electrode tip shape. The resulting increment in tip surface temperature results in the electrode material loss. It was also observed that the tungsten recrystallizes to large grains at high temperature. When the shape of grain boundaries are granular in shape, the intergranular diffusion of oxide emitter particles takes more time to reach the electrode surface. In the experimental work, the microstructure of the used electrode's tip surface will be studied by scanning electron microscope and reflective X-ray technique in order to gauge the extent of the diffusion and chemical reaction of emitter particles. Besides, a simulated model is proposed to explain the effect of oxide particles diffusion on the electrode’s microstructure, electron emission characteristics, and electrode tip erosion. This model suggests metallurgical modifications in tungsten electrode to enhance its erosion resistance.

Keywords: rare-earth emitter particles, temperature-dependent diffusion, TIG welding, Tungsten electrode

Procedia PDF Downloads 167

Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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Authors: Patrick Mueller, Alan Martin, Justin Stockwell, Robert Goldblatt

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Elevated concentrations of inorganic nitrogen (N) compounds (nitrate, nitrite, and ammonia) are a ubiquitous feature to mine-influenced drainages due to the leaching of blasting residues and use of cyanide in the milling of gold ores. For many mines, the management of N is a focus for environmental protection, therefore understanding the factors controlling the speciation and behavior of N is central to effective decision making. In this paper, the passive attenuation of ammonia and nitrite is described for three northern water bodies (two lakes and a tailings pond) influenced by mining activities. In two of the water bodies, inorganic N compounds originate from explosives residues in mine water and waste rock. The third water body is a decommissioned tailings impoundment, with N compounds largely originating from the breakdown of cyanide compounds used in the processing of gold ores. Empirical observations from water quality monitoring indicate nitrification (the oxidation of ammonia to nitrate) occurs in all three waterbodies, where enrichment of nitrate occurs commensurately with ammonia depletion. The N species conversions in these systems occurred more rapidly than chemical oxidation kinetics permit, indicating that microbial mediated conversion was occurring, despite the cool water temperatures. While nitrification of ammonia and nitrite to nitrate was the primary process, in all three waterbodies nitrite was consistently present at approximately 0.5 to 2.0 % of total N, even following ammonia depletion. The persistence of trace amounts of nitrite under these conditions suggests the co-occurrence denitrification processes in the water column and/or underlying substrates. The implications for N management in mine waters are discussed.

Keywords: explosives, mining, nitrification, water

Procedia PDF Downloads 297