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: 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: 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: 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: 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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Authors: Gokhan Zengin, Cengiz Sarikurkcu, Abdurrahman Aktumsek, Ramazan Ceylan

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The present study was designated to characterize the essential oil from S. galatica (SGEOs) and evaluate its antioxidant and enzyme inhibitory activities. Antioxidant capacity were tested different methods including free radical scavenging (DPPH, ABTS and NO), reducing power (FRAP and CUPRAC), metal chelating and phosphomolybdenum. Inhibitory activities were analyzed on acetylcholiesterase, butrylcholinesterase, α-amylase and α-glucosidase. SGEOs were chemically analyzed and identified by gas chromatography (GC) and gas chromatography/mass spectrophotometry (GC/MS). 23 components, representing 98.1% of SGEOs were identified. Monoterpene hydrocarbons (74.1%), especially α- (23.0%) and β-pinene (32.2%), were the main constituents in SGEOs. The main sesquiterpene hydrocarbons were β-caryophyllene (16.9%), Germacrene-D (1.2%) and Caryophyllene oxide (1.2%), respectively. Generally, SGEOs has shown moderate free radical, reducing power, metal chelating and enzyme inhibitory activities. These activities related to chemical profile in SGEOs. Our findings supported that the possible utility of SGEOs is a source of natural agents for food, cosmetics or pharmaceutical industries.

Keywords: sideritis galatica, antioxidant, monoterpenes, cholinesterase, anti-diabetic

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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: Gina M. Newton

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Australia’s Environment Protection and Biodiversity Conservation Act (EPBC Act) is the premiere, national law under which species and 'ecological communities' (i.e., like ecosystems) can be formally recognised and 'listed' as threatened across all jurisdictions. The listing process involves assessment against a range of criteria (similar to the IUCN process) to demonstrate conservation status (i.e., vulnerable, endangered, critically endangered, etc.) based on the best available science. Over the past decade in Australia, there’s been a transition from almost solely terrestrial to the first aquatic threatened ecological community (TEC or ecosystem) listings (e.g., River Murray, Macquarie Marshes, Coastal Saltmarsh, Salt-wedge Estuaries). All constitute large areas, with some including multiple state jurisdictions. Development of these conservation and listing advices has enabled, for the first time, a more forensic analysis of three key factors across a range of aquatic and coastal ecosystems: -the contribution of invasive species to conservation status, -how to demonstrate and attribute decline in 'ecological integrity' to conservation status, and, -identification of related priority conservation actions for management. There is increasing global recognition of the disproportionate degree of biodiversity loss within aquatic ecosystems. In Australia, legislative protection at Commonwealth or State levels remains one of the strongest conservation measures. Such laws have associated compliance mechanisms for breaches to the protected status. They also trigger the need for environment impact statements during applications for major developments (which may be denied). However, not all jurisdictions have such laws in place. There remains much opposition to the listing of freshwater systems – for example, the River Murray (Australia's largest river) and Macquarie Marshes (an internationally significant wetland) were both disallowed by parliament four months after formal listing. This was mainly due to a change of government, dissent from a major industry sector, and a 'loophole' in the law. In Australia, at least in the immediate to medium-term time frames, invasive species (aliens, native pests, pathogens, etc.) appear to be the number one biotic threat to the biodiversity and ecological function and integrity of our aquatic ecosystems. Consequently, this should be considered a current priority for research, conservation, and management actions. Another key outcome from this analysis was the recognition that drawing together multiple lines of evidence to form a 'conservation narrative' is a more useful approach to assigning conservation status. This also helps to addresses a glaring gap in long-term ecological data sets in Australia, which often precludes a more empirical data-driven approach. An important lesson also emerged – the recognition that while conservation must be underpinned by the best available scientific evidence, it remains a 'social and policy' goal rather than a 'scientific' goal. Communication, engagement, and 'politics' necessarily play a significant role in achieving conservation goals and need to be managed and resourced accordingly.

Keywords: aquatic ecosystem conservation, conservation law, ecological integrity, invasive species

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Authors: Rita Chattopadhyay, Vivek Anand Thoutam

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Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

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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: Omar Abdalla, Abdelfatah Ahmed, Ahmed Mustafa, Abdelazem Eldouma

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The purpose of this study is evaluating the effect of extremely low frequency magnetic field on Waster rats brain. The number of rats used in this study were 25, which were divided into five groups, each group containing five rats as follows: Group 1: The control group which was not exposed to energized field; Group 2: Rats were exposed to a magnetic field with an intensity of 0.6 mT (2 hours/day); Group 3: Rats were exposed to a magnetic field of 1.2 mT (2 hours/day); Group4: Rats were exposed to a magnetic field of 1.8 mT (2 hours/day); Group 5: Rats were exposed to a magnetic field of 2.4 mT (2 hours/day) and all groups were exposed for seven days, by designing a maze and calculating the time average for arriving to the decoy at special conditions. We found the time average before exposure for the all groups was G2=330 s, G3=172 s, G4=500 s and G5=174 s, respectively. We exposed all groups to ELF-MF and measured the time and we found: G2=465 s, G3=388 s, G4=501 s, and G5=442 s. It was observed that the time average increased directly with field strength. Histological samples of frontal lop of brain for all groups were taken and we found lesion, atrophy, empty vacuoles and disorder choroid plexus at frontal lope of brain. And finally we observed the disorder of choroid plexus in histological results and Alzheimer's symptoms increase when the magnetic field increases.

Keywords: nonionizing radiation, biophysics, magnetic field, shrinkage

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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: 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: Eman Al-khateeba, Ebaa Al-Zayadneha, Osama Al-Dalahmahb, Zeinab Alawadib, Faisal Khatiba, Randa Naffaa, Yanal Shafagoj

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The purpose of the current study was to examine the association of plasma copper and lipid concentrations with changes in cognitive function in elderly Jordanian individuals. The study population consisted of two groups; 52 subjects with dementia, and 50 controls. All individuals were screened with Mini-Mental State Examination (MMSE) and Clock drawing test (CDT).Serum copper and lipid profile were assessed in all subjects, and the results were statistically evaluated at P < 0.05 level of significance. Dementia group had 10.1 % higher copper levels than controls however the difference was not statistically significant. No significant differences could be found between the two groups in lipid profile levels. There was no significant correlation between serum copper, lipid profile and cognitive decline in elderly Jordanians. Demographic variables indicate that educational level less than 12 years and illiterate demonstrated a 3.29 fold (p=0.026) and 6.29 fold (p=0.002) increase in risk of developing dementia, respectively. While coffee intake showed a protective effect against cognitive decline with 6.25 fold lower risk with increased coffee intake.

Keywords: copper, cholesterol, dementia, Alzheimer's disease, lipid profile, coffee

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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: Lizelle Ramaccio Calvino

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Article 3 of the United Nations Convention on the Rights of Child states that 'The best interests of the child must be a top priority in all decisions and actions that affect children.' This stance is also echoed in terms of article 20 of the African Charter on the Rights and Welfare of the Child. South Africa, as a signatory of the aforesaid international and national conventions, constitutionalised the best interest of the child in terms of section 28(2) of the Republic of South Africa, 1996. Section 28(2) provides that '[A] child’s best interests are of paramount importance in every matter concerning the child.' The application of 'the best interests of the child' principle is consequently applicable in all fields of South African law, including matrimonial law. Two separate but equal Acts regulate civil marriages in South Africa, namely the Marriage Act 25 of 1961 and the Civil Union Act 17 of 2006. Customary marriages are regulated by the Recognition of Customary Marriages Act 120 of 1998. In terms of the Marriage Act and the Recognition of Customary Marriages Act, a minor may (provided he/she obtains the required consent) enter into a marriage. Despite the aforesaid, section 1 of the Civil Union Act categorically prohibits a minor from entering into a civil union. The article will first determine whether the ban of minors from entering into a civil union undermines the 'the best interests of the child' principle, and if so, whether it is in violation of the Constitution as well as international and national conventions. In addition, the article will critically analyse whether the application of the Marriage Act and the Civil Union Act (dual Acts) result in disparity within the South African marriage law framework, and if so, whether such discrepancy violates same-sex couples’ right (in particular a same-sex minor) to equality before the law and to have their dignity protected. The article intends, through the application of a qualitative research methodology and by way of a comparative analyses of international and domestic laws, consider whether a single well-defined structure such as the Dutch marriage law system would not be an improved alternative to address the existing paradox resulting from the application of an Act that undermines 'the best interest of the child' principle. Ultimately the article proposes recommendations for matrimonial law reform.

Keywords: best interests of the child, civil marriage, civil union, minor

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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: 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: Lingxia Li, Frédéric Schnell, Thibault Lachard, Anne-Charlotte Dupont, Shuzhe Ding, Solène Le Douairon Lahaye

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Background: Prolonged intensive endurance exercise is associated with cardiovascular adaptations in athletes. However, the sex differences in electrocardiographic (ECG) performance in triathletes are poorly understood. Methods: ECG results of male and female triathletes registered on the French ministerial lists of high-level athletes between 2015 and 2021 were involved. The ECG was evaluated according to commonly accepted criteria. Results: Eighty-six triathletes (male 50, female 36) were involved; the average age was 19.9 ± 4.8 years. The training volume was 21±6 hours/week in males and 19 ± 6 hours/week in females (p>0.05). Despite the relatively larger P wave (96.0 ± 12.0 vs. 89.9 ± 11.5 ms, p=0.02) and longer QRS complex (96.6 ± 11.1 vs. 90.3 ± 8.6 ms, p=0.005) in males than in females, all indicators were within normal ranges. The most common electrical manifestations were early repolarization (46.5%) and incomplete right bundle branch block (39.5%). No difference between sexes was found in electrical manifestations (p > 0.05). Conclusion: All ECG patterns were within normal limits under similar training volumes, but male triathletes were more susceptible to cardiovascular changes than females. The most common ECG manifestations in triathletes were early repolarization and incomplete right bundle branch block, with no disparity between males and females. Large samples involving both sexes are required.

Keywords: cardiovascular remodeling, electrocardiography, triathlon, elite athletes

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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: Saleh Alshehri

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Human skin detection is a vital step in many applications. Some of the applications are critical especially those related to security. This leverages the importance of a high-performance detection algorithm. To validate the accuracy of the algorithm, image databases are usually used. However, the suitability of these image databases is still questionable. It is suggested that the suitability can be measured mainly by the span the database covers of the color space. This research investigates the validity of three famous image databases.

Keywords: image databases, image processing, pattern recognition, neural networks

Procedia PDF Downloads 271

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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