Search results for: weak target signals

Authors: Kyeong Hoon Park, Taiji Mazuda

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High damping rubber (HDR) bearings are dissipating devices mainly used in seismic isolation systems and have a great damping performance. Although many studies have been conducted on the dynamic model of HDR bearings, few models can reflect phenomena such as dependency of experienced shear strain on initial history. In order to develop a model that can represent the dependency of experienced shear strain of HDR by Mullins effect, dynamic loading test was conducted using HDR specimen. The reaction of HDR was measured by applying a horizontal vibration using a hybrid actuator under a constant vertical load. Dynamic program analysis was also performed after dynamic loading test. The dynamic model applied in program analysis is a bilinear type double-target model. This model is modified from typical bilinear model. This model can express the nonlinear characteristics related to the initial history of HDR bearings. Based on the dynamic loading test and program analysis results, equivalent stiffness and equivalent damping ratio were calculated to evaluate the mechanical properties of HDR and the feasibility of the bilinear type double-target model was examined.

Keywords: base-isolation, bilinear model, high damping rubber, loading test

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Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

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Authors: Meraj Ali Khan, Falleh R. Al-Solamy

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In the present paper, second order duality for multiobjective nonlinear programming are investigated under the second order generalized (F, b, φ, ρ, θ)− univex functions. The weak, strong and converse duality theorems are proved. Further, we also illustrated an example of (F, b, φ, ρ, θ)− univex functions. Results obtained in this paper extend some previously known results of multiobjective nonlinear programming in the literature.

Keywords: duality, multiobjective programming, univex functions, univex

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Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

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Authors: Changchun Liang, Xiaodong Zhang, Xin Liu, Pengfei Sun

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Space intelligent robotic systems are expected to play an increasingly important role in the future. The robotic on-orbital service, whose key is the tracking and capturing technology, becomes research hot in recent years. In this paper, the authors propose a vision servo control system for target capturing. Robotic manipulator will be an intelligent robotic system with large-scale movement, functional agility, and autonomous ability, and it can be operated by astronauts in the space station or be controlled by the ground operator in the remote operation mode. To realize the autonomous movement and capture mission of SRM, a kind of autonomous programming strategy based on multi-camera vision fusion is designed and the selection principle of object visual position and orientation measurement information is defined for the better precision. Distributed control system hierarchy is designed and reliability is considering to guarantee the abilities of control system. At last, a ground experiment system is set up based on the concept of robotic control system. With that, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm, and demonstrates that the control system can fulfill the needs of function, real-time and reliability.

Keywords: control system, on-orbital service, space robot, vision servo

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Authors: Moh Rizaldi, Ali Abdurachman, Indra Perwira

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The biggest demonstration after the 1998 reforms that took place in Indonesia for several days at the end of 2019 did not eliminate the intention of the People’s Representative Council (Dewan Perwakilan Rakyat or DPR) and the President to enact the law 19 of 2019 (KPK law). There is a central issue to be highlighted, namely whether the change is intended to strengthen or even weaken the KPK. To achieve this goal, the Analysis focuses on two agency principles namely the independent principle and the control principle as seen from three things namely the legal substance, legal structure, and legal culture. The research method is normative with conceptual, historical and statute approaches. The argument from this writing is that KPK Law has cut most of the KPK's authority as a result the KPK has become symbolic or toothless in combating corruption.

Keywords: control, independent, KPK, law no. 19 of 2019

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Authors: Mark Davey

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Communications on a Network on Chip (NoC) produce timing information, i.e., network injection delays, packet traversal times, throughput metrics, and other attributes relating to the traffic being sent across the chip. The security requirements of a platform encompass each node to operate with confidentiality, integrity, and availability (ISO 27001). Inherently, a shared NoC interconnect is exposed to analysis of timing patterns created by contention for the network components, i.e., links and switches/routers. This phenomenon is defined as information leakage, which represents a ‘side channel’ of sensitive information that can be correlated to platform activity. The key algorithm presented in this paper evaluates how an adversary can control two platform neighbouring nodes of a target node to obtain sensitive information about communication with the target node. The actual information obtained is the period value of a periodic task communication. This enacts a breach of the expected confidentiality of a node operating in a multiprocessor platform. An experimental investigation of the side channel is undertaken to judge the level and significance of inferred information produced by access times to the NoC. Results are presented with a series of expanding task set scenarios to evaluate the efficacy of the side channel detection algorithm as the network load increases.

Keywords: embedded systems, multiprocessor, network on chip, side channel

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Authors: Bing-Shun Huang, Hung-Ju Chiu, Wen-Kai Hsieh, Hsiu-Chuan Lin, Ming-Lung Hung

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Taiwan's solar photovoltaic target in 2025 is 20 GW, of which the fish-electricity symbiosis target is 4 GW. In the future, many solar photovoltaic installations may cause local environmental or social impacts. Therefore, the Taiwan government inspects the fish-electricity symbiosis to reduce the impact of solar photovoltaics on the local environment or society. This stuy takes the symbiosis of fishery and electricity in Kaohsiung City as an example to explore Taiwan's environmental and social inspection practices. It mainly analyzes the two aspects of environmental ecology and social economy. The results show that the environmental inspection is mainly through site surveys, ecological information mapping, on-site interviews, and public consultation meetings. Social inspection mainly includes document analysis, on-site interviews, site surveys, expert discussions, and public consultations to identify possible local problems. Although the government had recognized the local issues, the future status may also change. It is recommended that future photoelectric companies should reconfirm the current situation of development sites when applying for the installation and propose countermeasures to solve the problem.

Keywords: taiwan, fish-electricity symbiosis, environment, society, inspection

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Authors: Marcin Baranski

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This article presents a vibration diagnostic method designed for permanent magnets (PM) traction motors. Those machines are commonly used in traction drives of electrical vehicles. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. This work presents: field-circuit model, results of static tests, results of calculations and simulations.

Keywords: electrical vehicle, permanent magnet, traction drive, vibrations, electrical machine, eccentricity

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Authors: Kuek Teik Sheng, Leaw Zee Guan, Lim Wah Kien, Ting Tin Tin

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Educational technology such as YouTube and Kahoot have arisen as an alternative to effective learning among higher education institutions. There are many researches done in carrying out experiments to test different educational technologies and received positive feedback from students. Yet, similar study is hardly found in Malaysia especially study that includes the latest educational technologies. As a developing country, it is crucial to ensure that these emerging technologies are assisting students in learning process before it is widely adopted in institutions. This paper conducted a study to explore the perception of higher education institution students on the current educational technologies in Malaysia which include online educational games, online videos/course, social media, presentation tools and resource management tool. Some of these technologies have not been looked into its potential in effective learning process. An online survey using questionnaire is conducted among a target of 300 university/college. In the survey, the result shows that majority of the target students in Malaysia agree that the current educational technologies help them in learning, understanding and manage their studies. It is necessary to discover students’ perceptions on the educational technologies in order to provide guidelines for the educators/institutions in selecting appropriate technology to conduct the lecture/tutorial efficiently and effectively.

Keywords: education, educational technology, Facebook, PowerPoint, YouTube

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Inkeri Jaaskelainen

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The aim of this study is to shed light on what is it like to study in apprenticeship training using intermediate (or even lower level) Finnish. The aim is to find out and describe these students' experiences and feelings while acquiring a profession in Finnish as it is important to understand how immigrant background adult learners learn and how their needs could be better taken into account. Many students choose apprenticeships and start vocational training while their language skills in Finnish are still very weak. At work, students should be able to simultaneously learn Finnish and do vocational studies in a noisy, demanding, and stressful environment. Learning and understanding new things is very challenging under these circumstances, and sometimes students get exhausted and experience a lot of stress - which makes learning even more difficult. Students are different from each other, and so are their ways to learn. Both duties at work and school assignments require reasonably good general language skills, and, especially at work, language skills are also a safety issue. The empirical target of this study is a group of students with an immigrant background who studied in various fields with intensive L2 support in 2016–2018 and who by now have completed a vocational upper secondary qualification. The interview material for this narrative study was collected from those who completed apprenticeship training in 2019–2020. The data collection methods used are a structured thematic interview, a questionnaire, and observational data. Interviewees with an immigrant background have an inconsistent cultural and educational background - some have completed an academic degree in their country of origin while others have learned to read and write only in Finland. The analysis of the material utilizes thematic analysis, which is used to examine learning and related experiences. Learning a language at work is very different from traditional classroom teaching. With evolving language skills, at an intermediate level at best, rushing and stressing makes it even more difficult to understand and increases the fear of failure. Constant noise, rapidly changing situations, and uncertainty undermine the learning and well-being of apprentices. According to preliminary results, apprenticeship training is well suited to the needs of an adult immigrant student. In apprenticeship training, students need a lot of support for learning and understanding a new communication and working culture. Stress can result in, e.g., fatigue, frustration, and difficulties in remembering and understanding. Apprenticeship training can be seen as a good path to working life. However, L2 support is a very important part of apprenticeship training, and it indeed helps students to believe that one day they will graduate and even get employed in their new country.

Keywords: apprenticeship training, vocational basic degree, Finnish learning, wee-being

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Authors: Parveen Kumar

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This article discusses institute information resources. Information, in its most restricted technical sense, is a sequence of symbols that can be interpreted as message information can be recorded as signs, or transmitted as signals. Information is any kind of event that affects the state of a dynamic system. Conceptually, information is the message being conveyed. This concept has numerous other meanings in different contexts. Moreover, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, representation, and especially entropy.

Keywords: institutions, information institutions, information services for mission-oriented institute, pattern

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

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With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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Authors: Nilgün Öztürk, Hakan Sabahtin Ali, Hülya Tuba Kıyan

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The genus Chenopodium belongs to Amaranthaceae, is represented by approximately 250 species in the world and 15 species and three subspecies in Turkey. Chenopodium species are traditionally used to treat chest and abdominal pain, shortness of breath, cough and neurological disorders. Chenopodium quinoa Willd. (Quinoa) is native to Andes region of South America (especially Peru and Bolivia) and cultivated in many countries include also Turkey in the world nowadays. The seeds of quinoa are rich in protein, and the phytochemical composition consists of antioxidant substances such as polyphenolic compounds, flavonoids, vitamins, and minerals; anticancer and neuroprotective compounds such as tocotrienols; anti-inflammatory compounds such as carotenoids and anthocyanins and also saponins and starch. Food products of quinoa such as quinoa cereal bar, pasta and cornflakes are used in the diet made during many disorders like obesity, cardiovascular disorder, hypertension and Celiac disease. Also quinoa seems to have antimicrobial, anti-inflammatory and cholesterol-lowering properties because of its bioactive compounds. In this present study, the aqueous ethanolic extracts of the seeds of three different coloured genotypes of quinoa were investigated for their antioxidant activities using 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity, ferrous ion-chelating effect, ferric-reducing antioxidant power, ABTS radical cation decolorization assays and total phenolic contents using Folin-Ciocalteu assay. Among the three genotypes of quinoa; the aqueous ethanolic extract of the red genotype had the highest total phenolic content (83.54 ± 2.12 mg gallic acid/100 g extract) whereas the extract of the white genotype had the lowest total phenolic content (70.66 ± 0.25 mg gallic acid/100 g). According to the antioxidant activity results; the extracts showed moderate reducing power effect whereas weak ABTS radical cation decolorization and ferrous ion-chelating effect and also too weak DPPH radical scavenging activity when compared to the positive standards.

Keywords: amaranthaceae, antioxidant activity, Chenopodium quinoa willd., total phenolic content

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Authors: Aoqi Zhang, Changgil Lee Lee, Seunghee Park

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A non-contact nondestructive technique using laser-induced ultrasonic wave generation method was applied to visualize corrosion damage at aluminum alloy plate structures. The ultrasonic waves were generated by a Nd:YAG pulse laser, and a galvanometer-based laser scanner was used to scan specific area at a target structure. At the same time, wave responses were measured at a piezoelectric sensor which was attached on the target structure. The visualization of structural damage was achieved by calculating logarithmic values of root mean square (RMS). Damage-sensitive feature was defined as the scattering characteristics of the waves that encounter corrosion damage. The corroded damage was artificially formed by hydrochloric acid. To observe the effect of the location where the corrosion was formed, the both sides of the plate were scanned with same scanning area. Also, the effect on the depth of the corrosion was considered as well as the effect on the size of the corrosion. The results indicated that the damages were successfully visualized for almost cases, whether the damages were formed at the front or back side. However, the damage could not be clearly detected because the depth of the corrosion was shallow. In the future works, it needs to develop signal processing algorithm to more clearly visualize the damage by improving signal-to-noise ratio.

Keywords: non-destructive testing, corrosion, pulsed laser scanning, ultrasonic waves, plate structure

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Authors: Zeina Merabi, Arij Dao

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The temporal order of information processing in the brain is an important code in many acoustic signals, including speech, music, and animal vocalizations. Despite its significance, surprisingly little is known about its underlying cellular mechanisms and network manifestations. In the songbird telencephalic nucleus HVC, a subset of neurons shows temporal combination sensitivity (TCS). These neurons show a high temporal specificity, responding differently to distinct patterns of spectral elements and their combinations. HVC neuron types include basal-ganglia-projecting HVCX, forebrain-projecting HVCRA, and interneurons (HVC¬INT), each exhibiting distinct cellular, electrophysiological and functional properties. In this work, we develop conductance-based neural network models connecting the different classes of HVC neurons via different wiring scenarios, aiming to explore possible neural mechanisms that orchestrate the combination sensitivity property exhibited by HVCX, as well as replicating in vivo firing patterns observed when TCS neurons are presented with various auditory stimuli. The ionic and synaptic currents for each class of neurons that are presented in our networks and are based on pharmacological studies, rendering our networks biologically plausible. We present for the first time several realistic scenarios in which the different types of HVC neurons can interact to produce this behavior. The different networks highlight neural mechanisms that could potentially help to explain some aspects of combination sensitivity, including 1) interplay between inhibitory interneurons’ activity and the post inhibitory firing of the HVCX neurons enabled by T-type Ca2+ and H currents, 2) temporal summation of synaptic inputs at the TCS site of opposing signals that are time-and frequency- dependent, and 3) reciprocal inhibitory and excitatory loops as a potent mechanism to encode information over many milliseconds. The result is a plausible network model characterizing auditory processing in HVC. Our next step is to test the predictions of the model.

Keywords: combination sensitivity, songbirds, neural networks, spatiotemporal integration

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Authors: M. Van 'T Hof, R. V. Pasma, J. T. Bailly, H. W. Hoek, W. A. Ester

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Background: The transition into primary school can be challenging for children with an autism spectrum disorder (ASD). Due to the new demands that are made to children in this period, their limitations in social functioning and school achievements may manifest and appear faster. Detection of possible ASD signals mainly takes place by parents, teachers and during obligatory municipal maternal and child health centre visits. Physicians of municipal maternal and child health centres have limited education and instruments to detect ASD. Further education on detecting ASD is needed to optimally equip these doctors for this task. Most research aims to increase the early detection of ASD in children aged 0-3 years and shows positive results. However, there is a lack of research on educational interventions to detect ASD in children aged 4-6 years by municipal maternal and child health physicians. Aim: The aim of this study is to explore the effect of the online educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health physicians. This educational intervention is developed within The Reach-Aut Academic Centre for Autism; Transitions in education, and will be available throughout The Netherlands. Methods: Ninety-two participants will follow the educational intervention: Detection of ASD in children aged 4-6 years for municipal maternal and child health centre physicians. The educational intervention consists of three, one and a half hour sessions, which are offered through an online interactive classroom. The focus and content of the course has been developed in collaboration with three groups of stakeholders; autism scientists, clinical practitioners (municipal maternal and child health doctors and ASD experts) and parents of children with ASD. The primary outcome measure is knowledge about ASD: signals, early detection, communication with parents and referrals. The secondary outcome measures are the number of ASD related referrals, the attitude towards the mentally ill (CAMI), perceived competency about ASD knowledge and detection skills, and satisfaction about the educational intervention. Results and Conclusion: The study started in January 2016 and data collection will end mid 2017.

Keywords: ASD, child, detection, educational intervention, physicians

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Authors: Caroline Mendes, Mary McNamara, Orla Howe

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Drug delivery systems are proposed for use in cancer treatment to specifically target cancer cells and deliver a therapeutic dose without affecting normal cells. For that purpose, the use of folate receptors (FR) can be considered a key strategy, since they are commonly over-expressed in cancer cells. In this study, cyclodextrins (CD) have being used as vehicles to target FR and deliver the chemotherapeutic drug, methotrexate (MTX). CDs have the ability to form inclusion complexes, in which molecules of suitable dimensions are included within their cavities. Here, β-CD has been modified using folic acid so as to specifically target the FR. Thus, this drug delivery system consists of β-CD, folic acid and MTX (CDEnFA:MTX). Cellular uptake of folic acid is mediated with high affinity by folate receptors while the cellular uptake of antifolates, such as MTX, is mediated with high affinity by the reduced folate carriers (RFCs). This study addresses the gene (mRNA) and protein expression levels of FRs and RFCs in the cancer cell lines CaCo-2, SKOV-3, HeLa, MCF-7, A549 and the normal cell line BEAS-2B, quantified by real-time polymerase chain reaction (real-time PCR) and flow cytometry, respectively. From that, four cell lines with different levels of FRs, were chosen for cytotoxicity assays of MTX and CDEnFA:MTX using the MTT assay. Real-time PCR and flow cytometry data demonstrated that all cell lines ubiquitously express moderate levels of RFC. These experiments have also shown that levels of FR protein in CaCo-2 cells are high, while levels in SKOV-3, HeLa and MCF-7 cells are moderate. A549 and BEAS-2B cells express low levels of FR protein. FRs are highly expressed in all the cancer cell lines analysed when compared to the normal cell line BEAS-2B. The cell lines CaCo-2, MCF-7, A549 and BEAS-2B were used in the cell viability assays. 48 hours treatment with the free drug and the complex resulted in IC50 values of 93.9 µM ± 15.2 and 56.0 µM ± 4.0 for CaCo-2 for free MTX and CDEnFA:MTX respectively, 118.2 µM ± 16.8 and 97.8 µM ± 12.3 for MCF-7, 36.4 µM ± 6.9 and 75.0 µM ± 10.5 for A549 and 132.6 µM ± 16.1 and 288.1 µM ± 26.3 for BEAS-2B. These results demonstrate that free MTX is more toxic towards cell lines expressing low levels of FR, such as the BEAS-2B. More importantly, these results demonstrate that the inclusion complex CDEnFA:MTX showed greater cytotoxicity than the free drug towards the high FR expressing CaCo-2 cells, indicating that it has potential to target this receptor, enhancing the specificity and the efficiency of the drug. The use of cell imaging by confocal microscopy has allowed visualisation of FR targeting in cancer cells, as well as the identification of the interlisation pathway of the drug. Hence, the cellular uptake and internalisation process of this drug delivery system is being addressed.

Keywords: cancer treatment, cyclodextrins, drug delivery, folate receptors, reduced folate carriers

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Authors: Hari K. Adhikari, Keshab Sharma, K. C. Apil

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The Gorkha Nepal earthquake of moment magnitude (Mw) 7.8 hit the central region of Nepal on April 25, 2015; with the epicenter about 77 km northwest of Kathmandu Valley. This paper aims to explore challenges of reconstruction in the rural earthquake-stricken areas of Nepal. The Gorkha earthquake on April 25, 2015, has significantly affected the livelihood of people and overall economy in Nepal, causing severe damage and destruction in central Nepal including nation’s capital. A larger part of the earthquake affected area is difficult to access with rugged terrain and scattered settlements, which posed unique challenges and efforts on a massive scale reconstruction and rehabilitation. 800 thousand buildings were affected leaving 8 million people homeless. Challenge of reconstruction of optimum 800 thousand houses is arduous for Nepal in the background of its turmoil political scenario and weak governance. With significant actors involved in the reconstruction process, no appreciable relief has reached to the ground, which is reflected over the frustration of affected people. The 2015 Gorkha earthquake is one of most devastating disasters in the modern history of Nepal. Best of our knowledge, there is no comprehensive study on reconstruction after disasters in modern Nepal, which integrates the necessary information to deal with challenges and opportunities of reconstructions. The study was conducted using qualitative content analysis method. Thirty engineers and ten social mobilizes working for reconstruction and more than hundreds local social workers, local party leaders, and earthquake victims were selected arbitrarily. Information was collected through semi-structured interviews and open-ended questions, focus group discussions, and field notes, with no previous assumption. Author also reviewed literature and document reviews covering academic and practitioner studies on challenges of reconstruction after earthquake in developing countries such as 2001 Gujarat earthquake, 2005 Kashmir earthquake, 2003 Bam earthquake and 2010 Haiti earthquake; which have very similar building typologies, economic, political, geographical, and geological conditions with Nepal. Secondary data was collected from reports, action plans, and reflection papers of governmental entities, non-governmental organizations, private sector businesses, and the online news. This study concludes that inaccessibility, absence of local government, weak governance, weak infrastructures, lack of preparedness, knowledge gap and manpower shortage, etc. are the key challenges of the reconstruction after 2015 earthquake in Nepal. After scrutinizing different challenges and issues, study counsels that good governance, integrated information, addressing technical issues, public participation along with short term and long term strategies to tackle with technical issues are some crucial factors for timely and quality reconstruction in context of Nepal. Sample collected for this study is relatively small sample size and may not be fully representative of the stakeholders involved in reconstruction. However, the key findings of this study are ones that need to be recognized by academics, governments, and implementation agencies, and considered in the implementation of post-disaster reconstruction program in developing countries.

Keywords: Gorkha earthquake, reconstruction, challenges, policy

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Seon Ho Kim, Minho Bang, Seok Min Choi, Young Moon Lee, Dong Kwan Kim, Hyung Hee Cho

Abstract:

Gas turbines are heat engines that convert chemical energy into electrical energy through mechanical energy. Since their high energy density per unit volume and low pollutant emissions, gas turbines are classified as clean energy. In order to obtain better performance, the turbine inlet temperature of the current gas turbine is operated at about 1600℃, and thermal damage is a very serious problem. Especially, these thermal damages are more prominent in off-design conditions than in design conditions. In this study, the thermal damage characteristics of high temperature components of a gas turbine made of a single crystal material are studied numerically for the off-design operating conditions. The target gas turbine is configured as a reheat cycle and is operated in peak load operation mode, not normal operation. In particular, the target gas turbine features a lot of low-load operation. In this study, a commercial code, ANSYS 18.2, was used for analyzing the thermal-flow coupling problems. As a result, the flow separation phenomenon on the pressure side due to the flow reduction was remarkable at the off-design condition, and the high heat transfer coefficient at the upper end of the suction surface due to the tip leakage flow was appeared.

Keywords: gas turbine, single crystal blade, off-design, thermal analysis

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Authors: I. Asanuma, T. Yamaguchi, J. Park, K. J. Mackin

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Fishery lights on the surface could be detected by the Day and Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi-NPP). The DNB covers the spectral range of 500 to 900 nm and realized a higher sensitivity. The DNB has a difficulty of identification of fishing lights from lunar lights reflected by clouds, which affects observations for the half of the month. Fishery lights and lights of the surface are identified from lunar lights reflected by clouds by a method using the DNB and the infrared band, where the detection limits are defined as a function of the brightness temperature with a difference from the maximum temperature for each level of DNB radiance and with the contrast of DNB radiance against the background radiance. Fishery boats or structures on islands could be detected by the Synthetic Aperture Radar (SAR) on the polar orbit satellites using the reflected microwave by the surface reflecting targets. The SAR has a difficulty of tradeoff between spatial resolution and coverage while detecting the small targets like fishery boats. A distribution of fishery boats and island activities were detected by the scan-SAR narrow mode of Radarsat-2, which covers 300 km by 300 km with various combinations of polarizations. The fishing boats were detected as a single pixel of highly scattering targets with the scan-SAR narrow mode of which spatial resolution is 30 m. As the look angle dependent scattering signals exhibits the significant differences, the standard deviations of scattered signals for each look angles were taken into account as a threshold to identify the signal from fishing boats and structures on the island from background noise. It was difficult to validate the detected targets by DNB with SAR data because of time lag of observations for 6 hours between midnight by DNB and morning or evening by SAR. The temporal changes of island activities were detected as a change of mean intensity of DNB for circular area for a certain scale of activities. The increase of DNB mean intensity was corresponding to the beginning of dredging and the change of intensity indicated the ending of reclamation and following constructions of facilities.

Keywords: day night band, SAR, fishery, South China Sea

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Authors: John McDowall, Lucy Lightfoot

Abstract:

Stigma constitutes a significant barrier to the recovery and social integration of individuals affected by mental illness. Although there is some debate in the literature regarding the definition and utility of stigma as a concept, it is widely accepted that it comprises three components: stereotypical beliefs, prejudicial reactions, and discrimination. Stereotypical beliefs describe the cognitive knowledge-based component of stigma, referring to beliefs (often negative) about members of a group that is based on cultural and societal norms (e.g. ‘People with anxiety are just weak’). Prejudice refers to the affective/evaluative component of stigma and describes the endorsement of negative stereotypes and the resulting negative emotional reactions (e.g. ‘People with anxiety are just weak, and they frustrate me’). Discrimination refers to the behavioural component of stigma, which is arguably the most problematic, as it exerts a direct effect on the stigmatized person and may lead people to behave in a hostile or avoidant way towards them (i.e. refusal to hire them). Research exploring anti-stigma initiatives focus primarily on an educational approach, with the view that accurate information will replace misconceptions and decrease stigma. Many approaches take a biogenetic stance, emphasising brain and biochemical deficits - the idea being that ‘mental illness is an illness like any other.' While this approach tends to effectively reduce blame, it has also demonstrated negative effects such as increasing prognostic pessimism, the desire for social distance and perceptions of stereotypes. In the present study 144 participants were split into three groups and read one of three vignettes presenting causal explanations for Generalised Anxiety Disorder (GAD): One explanation emphasized biogenetic factors as being important in the etiology of GAD, another emphasised psychosocial factors (e.g. aversive life events, poverty, etc.), and a third stressed the adaptive features of the disorder from an evolutionary viewpoint. A variety of measures tapping the various components of stigma were administered following the vignettes. No difference in stigma measures as a function of causal explanation was found. People who had contact with mental illness in the past were significantly less stigmatising across a wide range of measures, but this did not interact with the type of causal explanation.

Keywords: generalised anxiety disorder, discrimination, prejudice, stigma

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Authors: Mohamed H. Sorour, Hayam F. Shaalan, Heba A. Hani, Eman S. Sayed, Amany A. El-Mansoup

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Heavy metals (HMs) present an increasing threat to aquatic and soil environment. Thus, techniques should be developed for the removal and/or recovery of those HMs from point sources in the generating industries. This paper reports our endeavors concerning the development of in-house developed weak cation exchange polyacrylate hydrogel kaolin composites for heavy metals removal. This type of composite enables desirable characteristics and functions including mechanical strength, bed porosity and cost advantages. This paper emphasizes the effect of varying crosslinker (methylenebis(acrylamide)) concentration. The prepared cation exchanger has been subjected to intensive characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF) and Brunauer Emmett and Teller (BET) method. Moreover, the performance was investigated using synthetic and real wastewater for an industrial complex east of Cairo. Simulated and real wastewater compositions addressed; Cr, Co, Ni, and Pb are in the range of (92-115), (91-103), (86-88) and (99-125), respectively. Adsorption experiments have been conducted in both batch and column modes. In general, batch tests revealed enhanced cation exchange capacities of 70, 72, 78.2 and 99.9 mg/g from single synthetic wastes while, removal efficiencies of 82.2, 86.4, 44.4 and 96% were obtained for Cr, Co, Ni and Pb, respectively from mixed synthetic wastes. It is concluded that the mixed synthetic and real wastewaters have lower adsorption capacities than single solutions. It is worth mentioned that Pb attained higher adsorption capacities with comparable results in all tested concentrations of synthetic and real wastewaters. Pilot scale experiments were also conducted for mixed synthetic waste in a fluidized bed column for 48 hour cycle time which revealed 86.4%, 58.5%, 66.8% and 96.9% removal efficiency for Cr, Co, Ni, and Pb, respectively with maximum regeneration was also conducted using saline and acid regenerants. Maximum regeneration efficiencies for the column studies higher than the batch ones about by about 30% to 60%. Studies are currently under way to enhance the regeneration efficiency to enable successful scaling up of the adsorption column.

Keywords: polyacrylate hydrogel kaolin, ultrasonic irradiation, heavy metals, adsorption and regeneration

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Authors: Ivana Prochazkova

Abstract:

Cognitive linguistics offers biblical scholarship a specific methodological tool for analysis and interpretation of metaphorical expressions. Its methodology makes it possible to study processes involved in constructing the meaning of individual metaphorical expressions and whole conceptual metaphors; to analyze their function in the text; to follow the semantic development of concepts and conceptual domains, and to trace semantic changes and their motivation. The legal language in the Hebrew canon is extremely specific and formalized. Especially in the preambles to the collections of laws in the Pentateuch, more general considerations of the motif of keeping and breaking the law are encountered. This is also true in the psalms and wisdom literature. Legal theory and the philosophy of law deal with these motifs today. Metaphors play an important role in texts that reflect on more general issues. The purpose of this conference contribution is to write all over the central metaphorical concept, conceptual metaphor ךרד תורה (TORAH/LAW IS A JOURNEY), its function in the Torah and principal trends of the further development in the Prophets and the Writings. The conceptual metaphor תורה ךרד (TORAH/LAW IS A JOURNEY) constitutes a coherent system in conjunction with other metaphors that include e.g., conceptual metaphors נחה תורה (TORAH/LAW LEADS); its variant רעה תורה (TORAH IS A SHEPHERD/GUIDE); מקור תורה (TORAH/LAW IS A FOUNTAIN/A SOURCE OF LIFE). Some conceptual metaphors are well known, and their using are conventional (עשׁר תורה TORAH/LAW IS RICHES, שׂשׂון תורה TORAH/LAW IS DELIGHT, דבשׁ תורה TORAH/LAW IS HONEY, שׁמשׁ תורה TORAH/LAW IS SUN ). But some conceptual metaphors are by its occurrence innovative and unique (e.g., שׁריון תורה TORAH /LAW IS BODY ARMOR, כובע תורה TORAH /LAW IS A HELMET, בגד תורה TORAH/LAW IS A GARMENT, etc.). There will be given examples. Conceptual metaphors will be described by means of some 'metaphorical vehicles,' which are Hebrew expressions in the source domain that are repeatedly used in metaphorical conceptualizations of the target domain(s). Conceptual metaphors will be further described by means of 'generic narrative structures,' which are the particular aspects of a conceptual metaphor that emerge during the metaphorical structuring of concepts. They are the units of the metaphorical vehicles – the Hebrew expressions in the source domain – that structure concepts in much the same way that the conceptual metaphor in the target domain does. And finally, they will be described by means of the network of correspondences that exist between metaphorical vehicles – or generic metaphorical structures – and the Hebrew expressions in the target domain.

Keywords: cognitive theology, conceptual metaphor in the Old Testament, conceptual metaphors of the Torah, conceptual domain of law, righteousness, and justice

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Authors: Héctor González Espinosa, Ricardo Ivan Cordova Chávez, Alejandra Contreras Ramos, Itzia Irene Padilla Martínez, José Guadalupe Trujillo Ferrara, Marvin Antonio Soriano Ursúa

Abstract:

Boronic acids are able to create diester bonds with carbohydrates because of their hydroxyl groups; in nature, there are some organoborates with these characteristics, such as the calcium fructoborate, formed by the union of two fructose molecules and a boron atom, synthesized by plants. In addition, it has been observed that, in animal cells only the compounds with cis-diol functional groups are capable of linking to boric or boronic acids. The formation of these organoboron compounds could impair the physical and chemical properties of the precursors, even their acute toxicity. In this project, two carbohydrate-derived boron-containing compounds from D-fructose and D-arabinose and phenylboronic acid are analyzed by different spectroscopy techniques such as Raman, Infrared with Fourier Transform Infrared (FT-IR), Nuclear Magnetic Resonance (NMR) and X-ray diffraction crystallography to describe their chemical characteristics. Also, an acute toxicity test was performed to determine their LD50 using the Lorke’s method. It was confirmed by multiple spectra the formation of the adducts by the generation of the diester bonds with a β-D-pyranose of fructose and arabinose. The most prominent findings were the presence of signals corresponding to the formation of new bonds, like the stretching of B-O bonds, or the absence of signals of functional groups like the hydroxyls presented in the reagents used for the synthesis of the adducts. The NMR spectra yielded information about the stereoselectivity in the synthesis reaction, observed by the interaction of the protons and their vicinal atoms in the anomeric and second position carbons; but also, the absence of a racemic mix by the finding of just one signal in the range for the anomeric carbon in the 13C NMR spectra of both adducts. The acute toxicity tests by the Lorke’s method showed that the LD50 value for both compounds is 1265 mg/kg. Those results let us to propose these adducts as highly safe agents for further biological evaluation with medical purposes.

Keywords: acute toxicity, adduct, boron, carbohydrate, diester bond

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Authors: Nandhana Vivek, Bhaskar Gogoi, Ayyavu Mahesh

Abstract:

Breast cancer metastasis plays a key role in cancer progression and fatality. The present study examines the potential causes of metastasis in breast cancer by investigating the novel interactions between genes and their pathways. The gene expression profile of GSE99394, GSE1246464, and GSE103865 was downloaded from the GEO data repository to analyze the differentially expressed genes (DEGs). Protein-protein interactions, target factor interactions, pathways and gene relationships, and functional enrichment networks were investigated. The proliferation pathway was shown to be highly expressed in breast cancer progression and metastasis in all three datasets. Gene Ontology analysis revealed 11 DEGs as gene targets to control breast cancer metastasis: LYN, DLGAP5, CXCR4, CDC6, NANOG, IFI30, TXP2, AGTR1, MKI67, and FTH1. Upon studying the function, genomic and proteomic data, and pathway involvement of the target genes, DLGAP5 proved to be a promising candidate due to it being highly differentially expressed in all datasets. The study takes a unique perspective on the avenues through which DLGAP5 promotes metastasis. The current investigation helps pave the way in understanding the role DLGAP5 plays in metastasis, which leads to an increased incidence of death among breast cancer patients.

Keywords: genomics, metastasis, microarray, cancer

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Authors: Mahnaz Seyedoshohadaee

Abstract:

Background and Aims: Hospitalization of one of the family members in the hospital, especially in the emergency department, causes anxiety and psychological problems in family members and others. The way nurses interact with patients and their companions can play an important role in controlling and managing their anxiety. This study aims to determine the relationship between the anxiety of family members of patients referring to emergency departments and their views on the communication skills of nurses. Materials and Methods: The current research was a descriptive-correlation cross-sectional study on 263 family members of patients referred to the department. The emergency of two selected medical training centers affiliated with Iran University of Medical Sciences was performed. The samples were selected continuously in 2018 based on the inclusion criteria. Information was collected using the Health Communication Questionnaire (HCCQ) and Beck Anxiety Questionnaire (BAI). To analyze the data, Pearson's correlation coefficient, independent t-tests, analysis of variance, and Kruskal-Wallis were used at a significance level of 0.05. The data was analyzed using SPSS version 16 statistical software. Results: The mean score of communication skills of emergency department nurses from the point of view of patients' companions was at a low level (74.36 with a standard deviation of 3.7). 3.75% of patients' companions had anxiety at a mild level. There was no statistically significant correlation between the anxieties of the patient's companions. The anxiety of the patient's companions had a statistically significant relationship with the educational level (P=0.039), economic status (P=0.033), and family relationship with the patient (P=0.001). Also, the average anxiety score in children was significantly higher than that of patients' wives (P=0.008). The triage level of the patient also had a statistically significant relationship with the anxiety of the patient's companions (P>0.001). Conclusion: Most of the family members of the patients referred to the emergency room experienced mild anxiety. Also, from their point of view, the communication skills of emergency nurses were at a weak level. Despite the fact that there was no statistically significant relationship between the patient's family member's anxiety and their opinion about nurses' communication skills in this study, it seems that the weak communication skills of nurses from the patient's family member's point of view need special attention. The results of the present study can provide the necessary grounds for planning to improve the communication skills of nurses and also control the anxiety of patient caregivers through in-service training or other incentive mechanisms.

Keywords: anxiety, family, emergency department, communication skills, nurse

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