Search results for: early Alzheimer’s recognition

Authors: Grace E. Santella, Shawn G. Gallaher, Joseph P. Smith

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In order to determine the importance of small-system riverine heat flux on regional landfast sea ice breakup, our study explores the annual spring freshet of the Sagavanirktok River from 2014-2019. Seasonal heat cycling ultimately serves as the driving mechanism behind the freshet; however, as an emerging area of study, the extent to which inland thermodynamics influence coastal tundra geomorphology and connected landfast sea ice has not been extensively investigated in relation to small-scale Arctic river systems. The Sagavanirktok River is a small-to-midsized river system that flows south-to-north on the Alaskan North Slope from the Brooks mountain range to the Beaufort Sea at Prudhoe Bay. Seasonal warming in the spring rapidly melts snow and ice in a northwards progression from the Brooks Range and transitional tundra highlands towards the coast and when coupled with seasonal precipitation, results in a pulsed freshet that propagates through the Sagavanirktok River. The concentrated presence of newly exposed vegetation in the transitional tundra region due to spring melting results in higher absorption of solar radiation due to a lower albedo relative to snow-covered tundra and/or landfast sea ice. This results in spring flood runoff that advances over impermeable early-season permafrost soils with elevated temperatures relative to landfast sea ice and sub-ice flow. We examine the extent to which interannual temporal variability influences the onset and magnitude of river discharge by analyzing field measurements from the United States Geological Survey (USGS) river and meteorological observation sites. Rapid influx of heat to the Arctic Ocean via riverine systems results in a noticeable decay of landfast sea ice independent of ice breakup seaward of the shear zone. Utilizing MODIS imagery from NASA’s Terra satellite, interannual variability of river discharge is visualized, allowing for optical validation that the discharge flow is interacting with landfast sea ice. Thermal erosion experienced by sediment fast ice at the arrival of warm overflow preconditions the ice regime for rapid thawing. We investigate the extent to which interannual heat flux from the Sagavanirktok River’s freshet significantly influences the onset of local landfast sea ice breakup. The early-season warming of atmospheric temperatures is evidenced by the presence of storms which introduce liquid, rather than frozen, precipitation into the system. The resultant decreased albedo of the transitional tundra supports the positive relationship between early-season precipitation events, inland thermodynamic cycling, and degradation of landfast sea ice. Early removal of landfast sea ice increases coastal erosion in these regions and has implications for coastline geomorphology which stress industrial, ecological, and humanitarian infrastructure.

Keywords: Albedo, freshet, landfast sea ice, riverine heat flux, seasonal heat cycling

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Authors: Mukhtar Raban

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Nelson Mandela University subscribes to a humanizing pedagogy (HP), as housed under broader critical pedagogy, that underpins and informs learning and teaching activities at the institution. The investigation sought to explore the implications of humanizing and critical pedagogical considerations for a technology-enhanced language learning (TELL) environment in a university course. The paper inquires into the design of a learning resource in an online learning environment of an English communication module, that applied HP principles. With an objective of creating agentive spaces for foregrounding identity, student voice, critical self-reflection, and recognition of others’ humanity; a flexible and open 'My Presence' feature was added to the TELL environment that allowed students and lecturers to share elements of their backgrounds in a ‘mutually vulnerable’ manner as a way of establishing digital identity and a more ‘human’ presence in the online language learning encounter, serving as a catalyst for the recognition of the ‘other’. Following a qualitative research design, the study adopted an auto-ethnographic approach, complementing the critical inquiry nature embedded into the activity’s practices. The study’s findings provide critical reflections and deductions on the possibilities of leveraging digital human expression within a humanizing pedagogical framework to advance the realization of HP-adoption in language learning and teaching encounters. It was found that the consideration of humanizing pedagogical principles in the design of online learning was more effective when the critical outcomes were explicated to students and lecturers prior to the completion of the activities. The integration of humanizing pedagogy also led to a contextual advancement of ‘affective’ language learning. Upon critical reflection and analysis, student identity and agency can flourish in a technology-enhanced learning environment when humanizing, and critical pedagogy influences the learning design.

Keywords: critical reflection, humanizing pedagogy, student identity, technology-enhanced language learning

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Authors: Jean-Pierre Dubois, Jihad S. Daba, H. Karam, J. Abdallah

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Tsunami early detection and warning systems have proved to be of ultimate importance, especially after the destructive tsunami that hit Japan in March 2012. Such systems are crucial to inform the authorities of any risk of a tsunami and of the degree of its danger in order to make the right decision and notify the public of the actions they need to take to save their lives. The purpose of this research is to enhance existing tsunami detection and warning systems. We first propose an automated and miniaturized model of an early tsunami detection and warning system. The model for the operation of a tsunami warning system is simulated using the data acquisition toolbox of Matlab and measurements acquired from specified internet pages due to the lack of the required real-life sensors, both seismic and hydrologic, and building a graphical user interface for the system. In the second phase of this work, we implement various satellite image filtering schemes to enhance the acquired synthetic aperture radar images of the tsunami affected region that are masked by speckle noise. This enables us to conduct a post-tsunami damage extent study and calculate the percentage damage. We conclude by proposing improvements to the existing telecommunication infrastructure of existing warning tsunami systems using a migration to IP-based networks and fiber optics links.

Keywords: detection, GIS, GSN, GTS, GPS, speckle noise, synthetic aperture radar, tsunami, wiener filter

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Authors: Bryony Pound

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This study investigated how views of green or urban space affect learning performance. It provides evidence of the value of visual access to greenspace in work and learning environments, and builds on the extensive research into the cognitive and learning-related benefits of access to green and natural spaces, particularly in learning environments. It demonstrates that benefits of visual access to natural spaces whilst learning can produce statistically significant faster responses than those facing urban views after only 5 minutes. The primary hypothesis of this research was that a greenspace view would improve short-term learning. Participants were randomly assigned to either a view of parkland or of urban buildings from the same room. They completed a psychological test of two stages. The first stage consisted of a presentation of words from eight different categories (four manmade and four natural). Following this a 2.5 minute break was given; participants were not prompted to look out of the window, but all were observed doing so. The second stage of the test involved a word recognition/false memory test of three types. Type 1 was presented words from each category; Type 2 was non-presented words from those same categories; and Type 3 was non-presented words from different categories. Participants were asked to respond with whether they thought they had seen the words before or not. Accuracy of responses and reaction times were recorded. The key finding was that reaction times for Type 2 words (highest difficulty) were significantly different between urban and green view conditions. Those with an urban view had slower reaction times for these words, so a view of greenspace resulted in better information retrieval for word and false memory recognition. Importantly, this difference was found after only 5 minutes of exposure to either view, during winter, and with a sample size of only 26. Greenspace views improve performance in a learning task. This provides a case for better visual access to greenspace in work and learning environments.

Keywords: benefits, greenspace, learning, restoration

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Authors: Anthony J. Kondracki, Bonzo I. Reddick, Jennifer L. Barkin

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We sought to examine the association of maternal Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), and treponema pallidum (TP) (syphilis) infections with preterm birth (PTB) (<37 weeks gestation), low birth weight (LBW) (<2500 grams) and prenatal care (PNC) attendance. This cross-sectional study was based on data drawn from the 2020 United States National Center for Health Statistics (NCHS) Natality File. We estimated the prevalence of all births, early/late PTBs, moderately/very LBW, and the distribution of sexually transmitted infections (STIs) according to maternal characteristics in the sample. In multivariable logistic regression models, we examined adjusted odds ratios (aORs) and their corresponding 95% confidence intervals (CIs) of PTB and LBW subcategories in the association with maternal/infant characteristics, PNC status, and maternal CT, NG, and TP infections. In separate logistic regression models, we assessed the risk of these newborn outcomes stratified by PNC status. Adjustments were made for race/ethnicity, age, education, marital status, health insurance, liveborn parity, previous preterm birth, gestational hypertension, gestational diabetes, PNC status, smoking, and infant sex. Additionally, in a sensitivity analysis, we assessed the association with early, full, and late term births and the potential impact of unmeasured confounding using the E-value. CT (1.8%) was most prevalent STI in pregnancy, followed by NG (0.3%), and TP (0.1%). Non-Hispanic Black women, 20-24 years old, with a high school education, and on Medicaid had the highest rate of STIs. Around 96.6% of women reported receiving PNC and about 60.0% initiated PNC early in pregnancy. PTB and LBW were strongly associated with NG infection (12.2% and 12.1%, respectively) and late initiation/no PNC (8.5% and 7.6%, respectively), and ≤10 prenatal visits received (13.1% and 10.3%, respectively). The odds of PTB and LBW were 2.5- to 3-foldhigher for each STI among women who received ≤10 prenatal visits than >10 visits. Adequate prenatal care utilization and timely screening and treatment of maternal STIs can substantially reduce the burden of adverse newborn outcomes.

Keywords: low birthweight, prenatal care, preterm birth, sexually transmitted infections

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Authors: Robin Lok-Wang Ma

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The Center/Unit for Industry Engagement and Collaboration, as well as Internship, play a significant role at university. In general, the Center serves as the official interface between the industry and the School or Department to cultivate students’ early exposure to professional experience. The missions of the Center are not limited to provide a communication channel and collaborative platform for the industries and the university but also to assist students to build up their career paths early while still in the university. In recent years, a cooperative education experience (commonly known as a co-op) has been strongly advocated for students to make the school-to-work transition. The nature of the co-op program is not only consistent with the internships/final year design projects, but it is also more industrial-oriented with academic support from faculty at the university. The purpose of this paper is to describe an approach to how cooperative education experience can be integrated into Engineering Curriculum. It provides a mutual understanding and exchange of ideas for the approach between the university and the industry. A suggested format in terms of timeline, duration, selection of candidates, students, and companies’ expectations for the co-op program is described. Also, feedbacks from employers/industries show that a longer-term co-op program is well suited for students compared with a short-term internship. To this end, it provides a new insight into collaboration and/or partnership between the university and the industries to prepare professional work-ready graduates.

Keywords: cooperative education, industry, engagement, collaboration

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Authors: Teodora Vukovic, Christoph Hottiger, Noah Bubenhofer

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The development of VIAN-DH aims at bridging two linguistic approaches: conversation analysis/interactional linguistics (IL), so far a dominantly qualitative field, and computational/corpus linguistics and its quantitative and automated methods. Contemporary IL investigates the systematic organization of conversations and interactions composed of speech, gaze, gestures, and body positioning, among others. These highly integrated multimodal behaviour is analysed based on video data aimed at uncovering so called “multimodal gestalts”, patterns of linguistic and embodied conduct that reoccur in specific sequential positions employed for specific purposes. Multimodal analyses (and other disciplines using videos) are so far dependent on time and resource intensive processes of manual transcription of each component from video materials. Automating these tasks requires advanced programming skills, which is often not in the scope of IL. Moreover, the use of different tools makes the integration and analysis of different formats challenging. Consequently, IL research often deals with relatively small samples of annotated data which are suitable for qualitative analysis but not enough for making generalized empirical claims derived quantitatively. VIAN-DH aims to create a workspace where many annotation layers required for the multimodal analysis of videos can be created, processed, and correlated in one platform. VIAN-DH will provide a graphical interface that operates state-of-the-art tools for automating parts of the data processing. The integration of tools that already exist in computational linguistics and computer vision, facilitates data processing for researchers lacking programming skills, speeds up the overall research process, and enables the processing of large amounts of data. The main features to be introduced are automatic speech recognition for the transcription of language, automatic image recognition for extraction of gestures and other visual cues, as well as grammatical annotation for adding morphological and syntactic information to the verbal content. In the ongoing instance of VIAN-DH, we focus on gesture extraction (pointing gestures, in particular), making use of existing models created for sign language and adapting them for this specific purpose. In order to view and search the data, VIAN-DH will provide a unified format and enable the import of the main existing formats of annotated video data and the export to other formats used in the field, while integrating different data source formats in a way that they can be combined in research. VIAN-DH will adapt querying methods from corpus linguistics to enable parallel search of many annotation levels, combining token-level and chronological search for various types of data. VIAN-DH strives to bring crucial and potentially revolutionary innovation to the field of IL, (that can also extend to other fields using video materials). It will allow the processing of large amounts of data automatically and, the implementation of quantitative analyses, combining it with the qualitative approach. It will facilitate the investigation of correlations between linguistic patterns (lexical or grammatical) with conversational aspects (turn-taking or gestures). Users will be able to automatically transcribe and annotate visual, spoken and grammatical information from videos, and to correlate those different levels and perform queries and analyses.

Keywords: multimodal analysis, corpus linguistics, computational linguistics, image recognition, speech recognition

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Authors: Sara T. K. Li, Belinda H. J. Chung, Jeffery C. N. Yip, Janet H. Hsiao

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Music notation and text reading both involve statistical learning of music or linguistic structures. However, it remains unclear how music reading expertise influences text reading behavior. The present study examined this issue through an eye-tracking study. Chinese-English bilingual musicians and non-musicians read English sentences, Chinese sentences, musical phrases, and sentences in Tibetan, a language novel to the participants, with their eye movement recorded. Each set of stimuli consisted of two conditions in terms of structural regularity: syntactically correct and syntactically incorrect musical phrases/sentences. They then completed a sentence comprehension (for syntactically correct sentences) or a musical segment/word recognition task afterwards to test their comprehension/recognition abilities. The results showed that in reading musical phrases, as compared with non-musicians, musicians had a higher accuracy in the recognition task, and had shorter reading time, fewer fixations, and shorter fixation duration when reading syntactically correct (i.e., in diatonic key) than incorrect (i.e., in non-diatonic key/atonal) musical phrases. This result reflects their expertise in music reading. Interestingly, in reading Tibetan sentences, which was novel to both participant groups, while non-musicians did not show any behavior differences between reading syntactically correct or incorrect Tibetan sentences, musicians showed a shorter reading time and had marginally fewer fixations when reading syntactically correct sentences than syntactically incorrect ones. However, none of the musicians reported discovering any structural regularities in the Tibetan stimuli after the experiment when being asked explicitly, suggesting that they may have implicitly acquired the structural regularities in Tibetan sentences. This group difference was not observed when they read English or Chinese sentences. This result suggests that music reading expertise facilities reading texts in a novel language (i.e., Tibetan), but not in languages that the readers are already familiar with (i.e., English and Chinese). This phenomenon may be due to the similarities between reading music notations and reading texts in a novel language, as in both cases the stimuli follow particular statistical structures but do not involve semantic or lexical processing. Thus, musicians may transfer their statistical learning skills stemmed from music notation reading experience to implicitly discover structures of sentences in a novel language. This speculation is consistent with a recent finding showing that music reading expertise modulates the processing of English nonwords (i.e., words that do not follow morphological or orthographic rules) but not pseudo- or real words. These results suggest that the modulation of music reading expertise on language processing depends on the similarities in the cognitive processes involved. It also has important implications for the benefits of music education on language and cognitive development.

Keywords: eye movement behavior, eye-tracking, music reading expertise, sentence reading, structural regularity, visual processing

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Authors: B. A. Alwahaby

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Background: The number of Total Knee Replacement (TKR) and Anterior Cruciate Ligament Reconstruction (ACLR) performed every year is increasing. The main aim of physiotherapy early recovery rehabilitation after these surgeries is to control pain and edema and regain Range of Motion (ROM) and physical activity. All of these outcomes need to be managed by safe and effective modalities. Kinesiotaping (KT) is an elastic non-invasive therapeutic tape that has become recognised in different physiotherapy situation as injury prevention, rehabilitation, and performance enhancement and been used with different conditions. However, there is still clinical doubt regarding the effectiveness of KT due to inconclusive supporting evidence. The aim of this systematic review is to collate all the available evidence on the effectiveness of KT in the early rehabilitation of ACLR and TKR patients and analyse whether the use of KT combined with standard rehabilitation would facilitate recovery of postoperative outcome than standard rehabilitation alone. Methodology: A systematic review was conducted. Medline, EMBASE, Scopus, AMED PEDro, CINAHL, and Web of Science databases were searched. Each study was assessed for inclusion and methodological quality appraisal was undertaken by two reviewers using the JBI critical appraisal tools. The studies were then synthesised qualitatively due to heterogeneity between studies. Results: Five moderate to low quality RCTs were located. All five studies demonstrated statistically significant improvements in pain, swelling, ROM, and functional outcomes (p < 0.05). Between group comparison, KT combined with standardised rehabilitation were shown to be significantly more effective than standardised rehabilitation alone for pain and swelling (p < 0.05). However, there were inconstant findings for ROM, and no statistically significant differences reported between groups for functional outcomes (p > 0.05). Conclusion: Research in the area is generally low quality; however, there is consistent evidence to support the use of KT combined with standardised post-operative rehabilitation for reducing pain and swelling. There is also some evidence that KT may be effective in combination with standardised rehabilitation to regain knee extension ROM faster than standardised rehabilitation alone, but further primary research is required to confirm this.

Keywords: anterior cruciate ligament reconstruction, ACLR, kinesio taping, KT, postoperative, total knee replacement, TKR

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Authors: Monika Malik, Pooja Arora, Ruchi Sachdeva, Vishnampettai G. Ramachandran, Rahul Pal

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Apoptotic debris is believed to be the antigenic trigger in lupus. Whether such debris and autoantibodies induced in lupus-prone mice which specifically recognize its constituents can mediate differential effects on innate and humoral responses in such mice was assessed. The influence of apoptotic blebs and apoptotic cell-reactive monoclonal antibodies on phenotypic markers expressed on bone marrow-derived dendritic cells (BMDCs) and secreted cytokines were evaluated. Sera from lupus-prone and healthy mice immunized with the antibodies were analyzed for anti-self reactivity. Apoptotic blebs, as well as somatically-mutated IgG and non-mutated IgM apoptotic-cell reactive monoclonal antibodies, induced the preferential maturation of BMDCs derived from lupus-prone mice relative to BMDCs derived from healthy mice; antibody specificity and cell genotype both influenced the secretion of inflammatory cytokines. Immunization of lupus-prone mice with IgM and IgG antibodies led to hypergammaglobulinemia; elicited antibodies were self-reactive, and exhibited enhanced recognition of lupus-associated autoantigens (dsDNA, Ro60, RNP68, and Sm) in comparison with adjuvant-induced sera. While ‘natural’ IgM antibodies are believed to contribute to immune homeostasis, this study reveals that apoptotic cell-reactive IgM antibodies can promote inflammation and drive anti-self responses in lupus. Only in lupus-prone mice did immunization with IgG auto-antibodies enhance the kinetics of humoral anti-self responses, resulting in advanced-onset glomerulosclerosis. This study reveals that preferential innate and humoral recognition of the products of cell death in an autoimmune milieu influences the indices associated with lupus pathology.

Keywords: antigen spreading, apoptotic cell-reactive pathogenic IgG, and IgM autoantibodies, glomerulosclerosis, lupus

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Authors: Esther Van Andel, Stefanie C. Lange, Maarten M. J. Smulders, Han Zuilhof

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False outcomes of diagnostic tests are a major concern in medical health care. To improve the reliability of surface-based diagnostic tests, it is of crucial importance to diminish background signals that arise from the non-specific binding of biomolecules, a process called fouling. The aim is to create surfaces that repel all biomolecules except the molecule of interest. This can be achieved by incorporating antifouling protein repellent coatings in between the sensor surface and it’s recognition elements (e.g. antibodies, sugars, aptamers). Zwitterionic polymer brushes are considered excellent antifouling materials, however, to be able to bind the molecule of interest, the polymer brushes have to be functionalized and so far this was only achieved at the expense of either antifouling or binding capacity. To overcome this limitation, we combined both features into one single monomer: a zwitterionic sulfobetaine, ensuring antifouling capabilities, equipped with a clickable azide moiety which allows for further functionalization. By copolymerizing this monomer together with a standard sulfobetaine, the number of azides (and with that the number of recognition elements) can be tuned depending on the application. First, the clickable azido-monomer was synthesized and characterized, followed by copolymerizing this monomer to yield functionalizable antifouling brushes. The brushes were fully characterized using surface characterization techniques like XPS, contact angle measurements, G-ATR-FTIR and XRR. As a proof of principle, the brushes were subsequently functionalized with biotin via strain-promoted alkyne azide click reactions, which yielded a fully zwitterionic biotin-containing 3D-functionalized coating. The sensing capacity was evaluated by reflectometry using avidin and fibrinogen containing protein solutions. The surfaces showed excellent antifouling properties as illustrated by the complete absence of non-specific fibrinogen binding, while at the same time clear responses were seen for the specific binding of avidin. A great increase in signal-to-noise ratio was observed, even when the amount of functional groups was lowered to 1%, compared to traditional modification of sulfobetaine brushes that rely on a 2D-approach in which only the top-layer can be functionalized. This study was performed on stoichiometric silicon nitride surfaces for future microring resonator based assays, however, this methodology can be transferred to other biosensor platforms which are currently being investigated. The approach presented herein enables a highly efficient strategy for selective binding with retained antifouling properties for improved signal-to-noise ratios in binding assays. The number of recognition units can be adjusted to a specific need, e.g. depending on the size of the analyte to be bound, widening the scope of these functionalizable surface coatings.

Keywords: antifouling, signal-to-noise ratio, surface functionalization, zwitterionic polymer brushes

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Authors: Hui Fang Huang Su, Jia Borror

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This study compared two interventions for teaching math to preschool-aged students with autism spectrum disorder (ASD). The first is considered the business as usual (BAU) intervention, which uses the Strategies for Teaching Based on Autism Research (STAR) curriculum and discrete trial teaching as the instructional methodology. The second is the Math is Not Difficult (Project MIND) activity-embedded, naturalistic intervention. These interventions were randomly assigned to four preschool students with ASD classrooms and implemented over three months for Project Mind. We used measurement gained during the same three months for the STAR intervention. In addition, we used A quasi-experimental, pre-test/post-test design to compare the effectiveness of these two interventions in building mathematical knowledge and skills. The pre-post measures include three standardized instruments: the Test of Early Math Ability-3, the Problem Solving and Calculation subtests of the Woodcock-Johnson Test of Achievement IV, and the Bracken Test of Basic Concepts-3 Receptive. The STAR curriculum-based assessment is administered to all Baudhuin students three times per year, and we used the results in this study. We anticipated that implementing these two approaches would improve the mathematical knowledge and skills of children with ASD. Still, it is crucial to see whether a behavioral or naturalistic teaching approach leads to more significant results.

Keywords: early learning, autism, math for pre-schoolers, special education, teaching strategies

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Authors: Sirimanee Banjong

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This study aimed to 1) develop pre-service teachers’ leadership skills through camp-based learning, and 2) develop pre-service teachers’ teamwork skills through camp-based learning. An applied research methodology was used. The target group was derived from a purposive selection. It involved 32 fourth-year students in Early Childhood Education Program enrolling in a course entitled Seminar in Early Childhood Education provided during the second semester of the academic year 2013. The treatment was camp-based learning activities which applied a PDCA process including four stages: 1) plan, 2) do, 3) check, and 4) act. Research instruments were a learning camp program, a camp-based learning management plan, a 5-level assessment form for leadership skills and a 5-level assessment form for assessing teamwork skills. Data were analyzed using descriptive statistics. Results were: 1) pre-service teachers’ leadership skills yielded the before treatment average score at ¯("x" )=3.4, S.D.= 0.62 and the after-treatment average score at ¯("x" ) 4.29, S.D.=0.66 pre-service teachers’ teamwork skills yielded the before-treatment average score at ¯("x" )=3.31, S.D.= 0.60 and the after-treatment average score at ¯("x" )=4.42, S.D.= 0.66. Both differences were statistically significant at the .05 level. Thus, the pre-service teachers’ leadership and teamwork skills were significantly improved through the camp-based learning approach.

Keywords: learning camp, leadership skills, teamwork skills, pre-service teachers

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Authors: Rutanachai Thaipratum

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At present, it is widely-known that free radicals are the causes of illness such as cancers, coronary heart disease, Alzheimer’s disease and aging. One method of protection from free radical is the consumption of antioxidant-containing foods or herbs. Several analytical methods have been used for qualitative and quantitative determination of antioxidants. This project aimed to evaluate antioxidant activity of ethanolic and aqueous extracts from cabbage (Brassicca oleracea L. var. capitata L.) measured by DPPH and hydroxyl radical scavenging method. The results show that averaged antioxidant activity measured in ethanolic extract (µmol ascorbic acid equivalent/g fresh mass) were 7.316 ± 0.715 and 4.66 ± 1.029 as determined by DPPH and hydroxyl radical scavenging activity assays, respectively. Averaged antioxidant activity measured in aqueous extract (µmol ascorbic acid equivalent/g fresh mass) were 15.141 ± 2.092 and 4.955 ± 1.975 as determined by DPPH and hydroxyl radical scavenging activity assays respectively.

Keywords: free radical, antioxidant, cabbage, Brassica oleracea L. var. capitata L.

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Authors: Živilė Jurgelėnė, Vitalijus Karabanovas, Augustas Morkvėnas, Reda Dzingelevičienė, Nerijus Dzingelevičius, Saulius Raugelė, Boguslaw Buszewski

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The short- and long-term effects of COVID-19 antiviral drug molnupiravir and micro/nanoplastics on the early development of Salmo trutta were investigated using accumulation and exposure studies. Salmo trutta were used as standardized test organisms in toxicity studies of COVID-19 waste contaminants. The 2D/3D imaging was performed using confocal fluorescence spectral imaging microscopy to assess the uptake, bioaccumulation, and distribution of molnupiravir and micro/nanoplastics complex in live fish. Our study results demonstrated that molnupiravir may interact with a micro/nanoplastics and modify their spectroscopic parameters and toxicity to S. trutta embryos and larvae. The 0.2 µm size microplastics at a concentration of 10 mg/L were found to be stable in aqueous media than 0.02 µm, and 2 µm sizes polymeric particles. This study demonstrated that polymeric particles can adsorb molnupiravir that are present in mixtures and modify the accumulation of molnupiravir in Salmo trutta embryos and larvae. In addition, 2D/3D confocal fluorescence imaging showed that the single polymeric particle hardly accumulates and couldn't penetrate outer tissues of the tested organism. However, co-exposure micro/nanoplastics and molnupiravir could significantly enhance the polymeric particles capability of accumulating on surface tissues and penetrating surface tissue of fish in early development. Exposure to molnupiravir at 2 g/L concentration and co-exposure to micro/nanoplastics and molnupiravir did not bring about survival changes in in the early stages of Salmo trutta development, but we observed the reduction in heart rate and decrease in gill ventilation. The statistical analysis confirmed that micro/nanoplastics used in combination with molnupiravir enhance the toxicity of the latter micro/nanoplastics to embryos and larvae. This research has received funding from the European Regional Development Fund (project No 13.1.1-LMT-K-718-05-0014) under a grant agreement with the Research Council of Lithuania (LMTLT), and it was funded as part of the European Union’s measure in response to the COVID-19 pandemic.

Keywords: fish, micro/nanoplastics, molnupiravir, toxicity

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Authors: Mohua Chatterjee, Rajib De

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Introduction: Bone Marrow Transplant (BMT) is often performed to treat patients with various types of leukemia. A majority of these patients develop complications like chronic musculoskeletal GVHD post-BMT where patients get scleroderma, pain and restricted range of motion of joints of hand. If not treated early, it may cause permanent deformity of hand. This study was done to find the effectiveness of physiotherapy in hand dysfunction caused due to chronic musculoskeletal GVHD of leukemia patients after BMT. Methodology: 23 patients diagnosed with leukemia and having musculoskeletal GVHD were treated with a set of exercises including active exercises and stretching. The outcome was measured by Cochin Hand Function Scale (CHFS) at baseline and after four weeks of intervention. Results: Two patients were not able to carry out exercises beyond two weeks due to relapse of disease and one patient defaulted. It was found that all the patients who received physiotherapy had significant improvement in hand function. Mean CHFS decreased from 63.67 to 27.43 (P value < 0.001) indicating improvement in hand function after four weeks of physiotherapy. Conclusion: Early intervention of physiotherapy is effective in reducing hand dysfunction of leukemia patients with musculoskeletal GVHD post-BMT.

Keywords: bone marrow transplant, hand dysfunction, leukemia, musculoskeletal graft versus host disease, physiotherapy

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: Himanshu Sharma, Karthik Kumar, Harish Kumar

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The major problem in crop cultivation is the occurrence of multiple crop diseases. During the growth stage, timely identification of crop diseases is paramount to ensure the high yield of crops, lower production costs, and minimize pesticide usage. In most cases, crop diseases produce observable characteristics and symptoms. The Surveyors usually diagnose crop diseases when they walk through the fields. However, surveyor inspections tend to be biased and error-prone due to the nature of the monotonous task and the subjectivity of individuals. In addition, visual inspection of each leaf or plant is costly, time-consuming, and labour-intensive. Furthermore, the plant pathologists and experts who can often identify the disease within the plant according to their symptoms in early stages are not readily available in remote regions. Therefore, this study specifically addressed early detection of leaf scald, red rot, and eyespot types of diseases within sugarcane plants. The study proposes a computer vision-based approach using a convolutional neural network (CNN) for automatic identification of crop diseases. To facilitate this, firstly, images of sugarcane diseases were taken from google without modifying the scene, background, or controlling the illumination to build the training dataset. Then, the testing dataset was developed based on the real-time collected images from the sugarcane field from India. Then, the image dataset is pre-processed for feature extraction and selection. Finally, the CNN-based Visual Geometry Group (VGG) model was deployed on the training and testing dataset to classify the images into diseased and healthy sugarcane plants and measure the model's performance using various parameters, i.e., accuracy, sensitivity, specificity, and F1-score. The promising result of the proposed model lays the groundwork for the automatic early detection of sugarcane disease. The proposed research directly sustains an increase in crop yield.

Keywords: automatic classification, computer vision, convolutional neural network, image processing, sugarcane disease, visual geometry group

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Authors: C. M. Mat Isa, H. Mohd Saman, S. R. Mohd Nasir, A. Jaapar

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International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.

Keywords: factors, early movers, entry timing choices, late movers, logistic regression model, principal axis factorial analysis, Malaysian construction firms

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Authors: Hussein Alahmer, Amr Ahmed

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Liver cancer is one of the common diseases that cause the death. Early detection is important to diagnose and reduce the incidence of death. Improvements in medical imaging and image processing techniques have significantly enhanced interpretation of medical images. Computer-Aided Diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver disease and hence reduce liver cancer death rate.  This paper presents an automated CAD system consists of three stages; firstly, automatic liver segmentation and lesion’s detection. Secondly, extracting features. Finally, classifying liver lesions into benign and malignant by using the novel contrasting feature-difference approach. Several types of intensity, texture features are extracted from both; the lesion area and its surrounding normal liver tissue. The difference between the features of both areas is then used as the new lesion descriptors. Machine learning classifiers are then trained on the new descriptors to automatically classify liver lesions into benign or malignant. The experimental results show promising improvements. Moreover, the proposed approach can overcome the problems of varying ranges of intensity and textures between patients, demographics, and imaging devices and settings.

Keywords: CAD system, difference of feature, fuzzy c means, lesion detection, liver segmentation

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Authors: Rajlakshmi Guha, Sajjad Ansari, Shazia Nasreen, Hirak Banerjee, Jiaul Paik

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Dyslexia is a neurocognitive disorder, affecting around fifteen percent of the Indian population. The symptoms include difficulty in reading alphabet, words, and sentences. This can be difficult at the phonemic or recognition level and may further affect lexical structures. Therapeutic intervention of dyslexic children post assessment is generally done by special educators and psychologists through one on one interaction. Considering the large number of children affected and the scarcity of experts, access to care is limited in India. Moreover, unavailability of resources and timely communication with caregivers add on to the problem of proper intervention. With the development of Educational Technology and its use in India, access to information and care has been improved in such a large and diverse country. In this context, this paper proposes an ICT enabled home-based intervention program for dyslexic children which would support the child, and provide an interactive interface between expert, parents, and students. The paper discusses the details of the database design and system layout of the program. Along with, it also highlights the development of different technical aids required to build out personalized android applications for the Indian dyslexic population. These technical aids include speech database creation for children, automatic speech recognition system, serious game development, and color coded fonts. The paper also emphasizes the games developed to assist the dyslexic child on cognitive training primarily for attention, working memory, and spatial reasoning. In addition, it talks about the specific elements of the interactive intervention tool that makes it effective for home based intervention of dyslexia.

Keywords: Android applications, cognitive training, dyslexia, intervention

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Authors: Mina Salamatmanesh, Elizabeth Fitzpatrick, Tim Ramsay, Josee Lagacé, Lindsey Sikora, JoAnne Whittingham

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Introduction: Hearing loss (HL) is one of the most common disorders that presents at birth and in early childhood. Universal newborn hearing screening (UNHS) has been adopted based on the assumption that with early identification of HL, children will have access to optimal amplification and intervention at younger ages, therefore, taking advantage of the brain’s maximal plasticity. One particular challenge for parents in the early years is achieving consistent hearing aid (HA) use which is critical to the child’s development and constitutes the first step in the rehabilitation process. This study examined the consistency of hearing aid use in young children based on data logging information documented during audiology sessions in the first three years after hearing aid fitting. Methodology: The first 100 children who were diagnosed with bilateral HL before 72 months of age since 2003 to 2015 in a pediatric audiology clinic and who had at least two hearing aid follow-up sessions with available data logging information were included in the study. Data from each audiology session (age of child at the session, average hours of use per day (for each ear) in the first three years after HA fitting) were collected. Clinical characteristics (degree of hearing loss, age of HA fitting) were also documented to further understanding of factors that impact HA use. Results: Preliminary analysis of the results of the first 20 children shows that all of them (100%) have at least one data logging session recorded in the clinical audiology system (Noah). Of the 20 children, 17(85%) have three data logging events recorded in the first three years after HA fitting. Based on the statistical analysis of the first 20 cases, the median hours of use in the first follow-up session after the hearing aid fitting in the right ear is 3.9 hours with an interquartile range (IQR) of 10.2h. For the left ear the median is 4.4 and the IQR is 9.7h. In the first session 47% of the children use their hearing aids ≤5 hours, 12% use them between 5 to 10 hours and 22% use them ≥10 hours a day. However, these children showed increased use by the third follow-up session with a median (IQR) of 9.1 hours for the right ear and 2.5, and of 8.2 hours for left ear (IQR) IQR is 5.6 By the third follow-up session, 14% of children used hearing aids ≤5 hours, while 38% of children used them ≥10 hours. Based on the primary results, factors like age and level of HL significantly impact the hours of use. Conclusion: The use of data logging information to assess the actual hours of HA provides an opportunity to examine the: a) challenges of families of young children with HAs, b) factors that impact use in very young children. Data logging when used collaboratively with parents, can be a powerful tool to identify problems and to encourage and assist families in maximizing their child’s hearing potential.

Keywords: hearing loss, hearing aid, data logging, hours of use

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Authors: Roseline Oghogho Osaseri, Osaseri E. I.

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Lassa fever is an epidemic hemorrhagic fever caused by the Lassa virus, an extremely virulent arena virus. This highly fatal disorder kills 10% to 50% of its victims, but those who survive its early stages usually recover and acquire immunity to secondary attacks. One of the major challenges in giving proper treatment is lack of fast and accurate diagnosis of the disease due to multiplicity of symptoms associated with the disease which could be similar to other clinical conditions and makes it difficult to diagnose early. This paper proposed an Adaptive Neuro Fuzzy Inference System (ANFIS) for the prediction of Lass Fever. In the design of the diagnostic system, four main attributes were considered as the input parameters and one output parameter for the system. The input parameters are Temperature on admission (TA), White Blood Count (WBC), Proteinuria (P) and Abdominal Pain (AP). Sixty-one percent of the datasets were used in training the system while fifty-nine used in testing. Experimental results from this study gave a reliable and accurate prediction of Lassa fever when compared with clinically confirmed cases. In this study, we have proposed Lassa fever diagnostic system to aid surgeons and medical healthcare practictionals in health care facilities who do not have ready access to Polymerase Chain Reaction (PCR) diagnosis to predict possible Lassa fever infection.

Keywords: anfis, lassa fever, medical diagnosis, soft computing

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Authors: A. Duncan, A. Blake, A. O'Gara, J. Fitzgerald

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Introduction: Acute traumatic rib fractures have significant morbidity and mortality and are a commonly seen injury in trauma patients. Rib fracture pain can often be acute and can prove challenging to manage. We performed an audit on patients with acute traumatic rib fractures with the aim of composing a referral and treatment pathway for such patients. Methods: From January 2021 to January 2022, the pain medicine service encouraged early referral of all traumatic rib fractures to the pain service for a multi-modal management approach. A retrospective audit of analgesic management was performed on a select cohort of 24 patients, with a mean age of 67, of which 19 had unilateral rib fractures. Results: 17 of 24 patients (71%) underwent local, regional block as part of a multi-modal analgesia regime. Only one regional complication was observed, seen with hypotension occurring in one patient with a thoracic epidural. The group who did not undergo regional block had a length of stay (LOS) 17 days longer than those who did (27 vs. 10) and higher rates of pneumonia (29% vs. 18%). Conclusion: Early referral to pain specialists is an important component of the effective management of acute traumatic rib fractures. From our audit, it is evident that regional blocks can be effectively used in these cases as part of a multi-modal analgesia regime and may confer benefits in terms of respiratory complications and length of stay.

Keywords: rib fractures, regional blocks, thoracic epidural, erector spina block

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Authors: Akane Uda, Ami Tabata, Mi An, Misa Komaki, Ryotaro Ito, Mayumi Inoue, Takehiro Sasai, Yusuke Kusano, Toshihiro Kato

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Early intervention is recommended for children with autism spectrum disorder (ASD), and an increasing number of children have received support and intervention before school age in recent years. In this study, we systematically reviewed preschool interventions focused on repetitive behaviors observed in children with ASD, which are often observed at younger ages. Inclusion criteria were as follows : (1) Child of preschool status (age ≤ 7 years) with a diagnosis of ASD (including autism, Asperger's, and pervasive developmental disorder) or a parent (caregiver) with a preschool child with ASD, (2) Physician-confirmed diagnosis of ASD (autism, Asperger's, and pervasive developmental disorder), (3) Interventional studies for repetitive behaviors, (4) Original articles published within the past 10 years (2012 or later), (5) Written in English and Japanese. Exclusion criteria were as follows: (1) Systematic reviews or meta-analyses, (2) Conference reports or books. We carefully scrutinized databases to remove duplicate references and used a two-step screening process to select papers. The primary screening included close scrutiny of titles and abstracts to exclude articles that did not meet the eligibility criteria. During the secondary screening, we carefully read the complete text to assess eligibility, which was double-checked by six members at the laboratory. Disagreements were resolved through consensus-based discussion. Our search yielded 304 papers, of which nine were included in the study. The level of evidence was as follows: three randomized controlled trials (level 2), four pre-post studies (level 4b), and two case reports (level 5). Seven articles selected for this study described the effectiveness of interventions. Interventions for repetitive behaviors in preschool children with ASD were categorized as five interventions that directly involved the child and four educational programs for caregivers and parents. Studies that directly intervened with children used early intensive intervention based on applied behavior analysis (Early Start Denver Model, Early Intensive Behavioral Intervention, and the Picture Exchange Communication System) and individualized education based on sensory integration. Educational interventions for caregivers included two methods; (a) education regarding combined methods and practices of applied behavior analysis in addition to classification and coping methods for repetitive behaviors, and (b) education regarding evaluation methods and practices based on children’s developmental milestones in play. With regard to the neurophysiological basis of repetitive behaviors, environmental factors are implicated as possible contributors. We assumed that applied behavior analysis was shown to be effective in reducing repetitive behaviors because analysis focused on the interaction between the individual and the environment. Additionally, with regard to educational interventions for caregivers, the intervention was shown to promote behavioral change in children based on the caregivers' understanding of the classification of repetitive behaviors and the children’s developmental milestones in play and adjustment of the person-environment context led to a reduction in repetitive behaviors.

Keywords: autism spectrum disorder, early intervention, repetitive behaviors, systematic review

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Authors: Ibrahim Sani, Suleiman Mohammed., Salisu Sulaiman, Aminu Musa

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The fall army worm (FAW), Spodoptera frugiperda (J. E. Smith) (Lepidoptera, Noctuidae) has recently become one of the major threats to maize production in the world. It is native to tropical and subtropical America and began to spread to many African and a few Asian Countries. A survey for the observation of infestation and collection of fall armyworm was conducted in field planted with maize in the northern part of Katsina state. Eggs and immature stages were collected, place in a plastic container and brought to the laboratory for observation and study of developmental stages. FAW was identified based on the morphological characteristics, i.e. the “Y” inverted shape on the head capsule and the patterns of black spots on the abdominal segments (square and trapezoidal forms). Different growing stage of maize are affected by fall armyworm, but the damage is greatest during the early growing phase of corn. Heavy infestation on the leaves also cause defoliation. Four developmental stages (eggs larvae, pupae and adults) of the FAW were studied when fed with young corn under laboratory conditions. Furthermore, effective scouting or monitoring of FAW could be practice at early stage of growth of maize.

Keywords: infestation, katsina, maize, fall armyworm

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Authors: Angela Maria Fasnacht

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Detection of anomalies due to contaminants’ presence, also known as early detection systems in water treatment plants, has become a critical point that deserves an in-depth study for their improvement and adaptation to current requirements. The design of these systems requires a detailed analysis and processing of the data in real-time, so it is necessary to apply various statistical methods appropriate to the data generated, such as Spearman’s Correlation, Factor Analysis, Cross-Correlation, and k-fold Cross-validation. Statistical analysis and methods allow the evaluation of large data sets to model the behavior of variables; in this sense, statistical treatment or analysis could be considered a vital step to be able to develop advanced models focused on machine learning that allows optimized data management in real-time, applied to early detection systems in water treatment processes. These techniques facilitate the development of new technologies used in advanced sensors. In this work, these methods were applied to identify the possible correlations between the measured parameters and the presence of the glyphosate contaminant in the single-pass system. The interaction between the initial concentration of glyphosate and the location of the sensors on the reading of the reported parameters was studied.

Keywords: glyphosate, emergent contaminants, machine learning, probes, sensors, predictive

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Authors: Marinete Araujo da Silva Fobister

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This presentation aims to discuss how the feeling of ‘nostalgia’ both present on Westerns and migrants could shed light to a mutual recognition and an exchange of ways of life that could enhance mutual possibilities of self-realization. It seems that this feeling of nostalgia is related to another unfolding of the nihilism of the death of God diagnosed by Nietzsche. Westerns are feeling on the margins of the values of their own culture as they feel such values as external to them. At the same time, some groups are claiming the return of the old devalued values. In this scenario, the West is receiving many migrants from different parts of the world since the second half of the last century. Migrants might be suffering from nostalgia or homesickness for having left their home. It might be that sharing a sense of nostalgia, although with different meanings, can bring together Westerns and migrants. Migrants bring ways of life that might be unknown and inexperienced in the West, and these can shed light to new forms of interpretation and cultivation of ones’ drives, and forces and this could become a source of mutual strength cultivation. Therefore, this mutual feeling of nostalgia could lead to ways of exploring the idea of self- realization in Nietzsche detaching it from the idea of being mainly individual to a more trans-individual-cultural claim. Nietzsche argues that nihilism is a European event here translated as a Western event, which would take 200 years until it could be overcome. To overcome nihilism a new kind of human would be needed, a creative and strong kind. For Nietzsche, there is not a fixed or true self, hence one possibility for one to achieve self-realization would reside on cultivating their multiple creative forces. The argument here is that in this recent unfolding of nihilism, translated in the sense of nostalgia, the encounter between the mainstream western immersed on nihilism with migrants could create a sense of a shared temporary home, where these different ways of life could inspire each other to create new meanings. Indeed, contributing to the expansion of one’s world view, drives and forces. Therefore, fertilizing the soil for the cultivation of self-realization and consequently the creation of new values.

Keywords: migration, nihilism, nostalgia, self-realization

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Authors: Amaladhithyan Krishnamoorthy, Ruvaitha Banu

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Vision is considered as the most important sense in humans, without which leading a normal can be often difficult. There are many existing smart canes for visually impaired with obstacle detection using ultrasonic transducer to help them navigate. Though the basic smart cane increases the safety of the users, it does not help in filling the void of visual loss. This paper introduces the concept of Digi-Buddy which is an evolved smart cane for visually impaired. The cane consists for several modules, apart from the basic obstacle detection features; the Digi-Buddy assists the user by capturing video/images and streams them to the server using a wide-angled camera, which then detects the objects using Deep Convolutional Neural Network. In addition to determining what the particular image/object is, the distance of the object is assessed by the ultrasonic transducer. The sound generation application, modelled with the help of Natural Language Processing is used to convert the processed images/object into audio. The object detected is signified by its name which is transmitted to the user with the help of Bluetooth hear phones. The object detection is extended to facial recognition which maps the faces of the person the user meets in the database of face images and alerts the user about the person. One of other crucial function consists of an automatic-intimation-alarm which is triggered when the user is in an emergency. If the user recovers within a set time, a button is provisioned in the cane to stop the alarm. Else an automatic intimation is sent to friends and family about the whereabouts of the user using GPS. In addition to safety and security by the existing smart canes, the proposed concept devices to be implemented as a prototype helping visually-impaired visualize their surroundings through audio more in an amicable way.

Keywords: artificial intelligence, facial recognition, natural language processing, internet of things

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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