Search results for: early detection of violence

Authors: Ali Murat Guler

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A variety of experiments have been developed over the past decades, aiming at the detection of Weakly Interactive Massive Particles (WIMPs) via their scattering in an instrumented medium. The sensitivity of these experiments has improved with a tremendous speed, thanks to a constant development of detectors and analysis methods. Detectors capable of reconstructing the direction of the nuclear recoil induced by the WIMP scattering are opening a new frontier to possibly extend Dark Matter searches beyond the neutrino background. Measurement of WIMP’s direction will allow us to detect the galactic origin of dark matter and, therefore to have a clear signal-background separation. The NEWSdm experiment, based on nuclear emulsions, is intended to measure the direction of WIMP-induced nuclear coils with a solid-state detector, thus with high sensitivity. We discuss the discovery potential of a directional experiment based on the use of a solid target made of newly developed nuclear emulsions and novel read-out systems achieving nanometric resolution. We also report results of a technical test conducted in Gran Sasso.

Keywords: dark matter, direct detection, nuclear emulsion, WIMPS

Procedia PDF Downloads 271

Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

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Authors: Vandana Mohan, Ashwani Koul

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Aim: To evaluate the potential of Aqueous Tinospora cordifolia stem extract (Aq.Tc) and Arabinogalactan (AG) on pulmonary carcinogenesis and associated tumor markers. Background: Lung cancer is one of the most frequent malignancy with high mortality rate due to limitation of early detection resulting in low cure rates. Current research effort focuses on identifying some blood-based biomarkers like CEA, ctDNA and LDH which may have potential to detect cancer at an early stage, evaluation of therapeutic response and its recurrence. Medicinal plants and their active components have been widely investigated for their anticancer potentials. Aqueous preparation of T. Cordifolia extract is enriched in the polysaccharide fraction i.e., AG when compared with other types of extract. Moreover, reports are available of polysaccharide fraction of T. Cordifolia in in vitro lung cancer models which showed profound anti-metastatic activity against these cell lines. However, not much has been explored about its effect in in vivo lung cancer models and the underlying mechanism involved. Experimental Design: Mice were randomly segregated into six groups. Group I animals served as control. Group II animals were administered with Aq. Tc extract (200 mg/kg b.w.) p.o.on the alternate days. Group III animals were fed with AG (7.5 mg/kg b.w.) p.o. on the alternate days (thrice a week). Group IV animals were installed with Benzo(a)pyrene (50 mg/kg b.w.), i.p. twice within an interval of two weeks. Group V animals received Aq. Tc extract as in group II along with it B(a)P was installed after two weeks of Aq. Tc administration following the same protocol as for group IV. Group VI animals received AG as in group III along with it B(a)P was installed after two weeks of AG administration. Results: Administration of B(a)P to mice resulted in increased tumor incidence, multiplicity and pulmonary somatic index with concomitant increase in serum/plasma markers like CEA, ctDNA, LDH and TNF-α.Aq.Tc and AG supplementation significantly attenuated these alterations at different stages of tumorigenesis thereby showing potent anti-cancer effect in lung cancer. A pronounced decrease in serum/plasma markers were observed in animals treated with Aq.Tc as compared to those fed with AG. Also, extensive hyperproliferation of alveolar epithelium was prominent in B(a)P induced lung tumors. However, treatment of Aq.Tc and AG to lung tumor bearing mice exhibited reduced alveolar damage evident from decreased number of hyperchromatic irregular nuclei. A direct correlation between the concentration of tumor markers and the intensity of lung cancer was observed in animals bearing cancer co-treated with Aq.Tc and AG. Conclusion: These findings substantiate the chemopreventive potential of Aq.Tc and AG against lung tumorigenesis. Interestingly, Aq.Tc was found to be more effective in modulating the cancer as reflected by various observations which may be attributed to the synergism offered by various components of Aq.Tc. Further studies are in progress to understand the underlined mechanism in inhibiting lung tumorigenesis by Aq.Tc and AG.

Keywords: Arabinogalactan, Benzo(a)pyrene B(a)P, carcinoembryonic antigen (CEA), circulating tumor DNA (ctDNA), lactate dehydrogenase (LDH), Tinospora cordifolia

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Authors: Ranitha Guna Selvi D, Ramakrishnan R, Mohideen Abdul Kader

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Purpose: To study the role of Vision centers in managing preventable blindness through refractive error correction in Rural South India. Methods: A retrospective analysis of patients attending 15 Vision centers in Rural South India from a period of January 2021 to December 2021 was done. Medical records of 10,85,81 patients both new and reviewed, 79,562 newly registered patients and 29,019 review patient’s from15 Vision centers were included for data analysis. All the patients registered at the vision center underwent basic eye examination, including visual acuity, IOP measurement, Slit-lamp examination, retinoscopy, Fundus examination etc. Results: A total of 1,08,581 patients were included in the study. Of the total 1,08,581 patients, 79,562 were newly registered patients at Vision center and 29,019 were review patients. Males were 52,201(48.1%) and Females were 56,308(51.9) among them. The mean age of all examined patients was 41.03 ± 20.9 years (Standard deviation) and ranged from 01 – 113 years. Presenting mean visual acuity was 0.31 ± 0.5 in the right eye and 0.31 ± 0.4 in the left eye. Of the 1,08,581 patients 22,770 patients had refractive error in right eye and 22,721 patients had uncorrected refractive error in left eye. Glass prescription was given to 17,178 (15.8%) patients. 8,109 (7.5%) patients were referred to the base hospital for specialty clinic expert opinion or for cataract surgery. Conclusion: Vision center utilizing teleconsultation for comprehensive eye screening unit is a very effective tool in reducing the avoidable visual impairment caused due to uncorrected refractive error. Vision Centre model is believed to be efficient as it facilitates early detection and management of uncorrected refractive errors.

Keywords: refractive error, uncorrected refractive error, vision center, vision technician, teleconsultation

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Authors: Lauren Booker

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In the United States, over 2.5 million children have incarcerated parents. Recent studies have shown 13% of young adults and one-fourth of African Americans will experience parental incarceration. The increasing numbers of incarcerated citizens have left these children as collateral damage and are often forgotten, their special needs inadequately meet or understood. Parental arrest and incarceration creates a uniquely traumatic experience in childhood and has long-term consequences for these children. Until recently, the eating and sleeping habits following parental incarceration had been nonexistent in the literature. However, even this groundbreaking study on eating habits and sleeping disorders following parental incarceration did not touch on the root causes of unhealthy eating which may be influenced by food and housing insecurity and environmental factors that may impact a child’s healthy eating and sleeping behaviors. This study will examine those factors as it could greatly aid in the policies and programs that affect children’s health and development. This proposed study will examine the impact of traumatic stress reactions to parental incarceration by studying sleep and eating habits as the hypothesis is that parental incarceration will lead to disordered eating and sleep disturbances in early adolescents.

Keywords: parental incarceration, eating disorder, trauma, family instability

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Authors: Cristian Viespe, Dana Miu

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Surface acoustic wave (SAW) sensors with nanoparticles (NPs) of various dimensions and concentrations embedded in different types of polymer sensing films for detecting volatile organic compounds (VOCs) were studied. The sensors were ‘delay line’ type with a center frequency of 69.4 MHz on ST-X quartz substrates. NPs with different diameters of 7 nm or 13 nm were obtained by laser ablation with lasers having 5 ns or 10 ps pulse durations, respectively. The influence of NPs dimensions and concentrations on sensor properties such as frequency shift, sensitivity, noise and response time were investigated. To the best of our knowledge, the influence of NP dimensions on SAW sensor properties with has not been investigated. The frequency shift and sensitivity increased with increasing NP concentration in the polymer for a given NP dimension and with decreasing NP diameter for a given concentration. The best performances were obtained for the smallest NPs used. The SAW sensor with NPs of 7 nm had a limit of detection (LOD) of 65 ppm (almost five times better than the sensor with polymer alone), and a response time of about 9 s for ethanol.

Keywords: surface acoustic wave sensor, nanoparticles, volatile organic compounds, laser ablation

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Authors: Aqsa Jamil, Tamura Hiroshi, Katsuchi Hiroshi, Wang Jiaqi

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The yield point represents the upper limit of forces which can be applied to a specimen without causing any permanent deformation. After yielding, the behavior of the specimen suddenly changes, including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of a thermography camera. The yield point of specimens was estimated with the help of temperature dip, which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing a repeatability analysis. The effects of temperature imperfection and light source have been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of the thermographic technique.

Keywords: signal to noise ratio, thermoelastic effect, thermography, yield point

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Authors: Dileep K. Verma, Sunil K. Lal

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Influenza still remains one of the most challenging diseases posing significant threat to public health causing seasonal epidemics and pandemics. Previous studies suggest that influenza hemagglutinin is essential for viral attachment to host sialic acid receptors and concentrate in lipid rafts for efficient viral fusion. Studies also reported selective nature of Influenza virus to utilize rafts micro-domain for efficient virus assembly and budding. However, the detailed mechanism of Influenza A Virus (IAV) binding to host cell membrane and entry inside the host remains elusive. In the present study, we investigated if host membrane lipid rafts play any significant role in early life cycle events of influenza A virus. Role of host lipid rafts was studied using raft disruption method by extraction of cholesterol and Methyl-β-Cyclodextrin was used to remove membrane cholesterol. We observed co-localization of Influenza A Virus to lipid rafts by visualization of known lipid raft marker GM1 on host cell membrane. Co-localization suggest direct involvement of these micro-domain in initiation of IAV life cycle. We found significant reduction in influenza A virus adsorption in raft disrupted target host cells indicating poor binding and attachment in absence of coherent membrane rafts. Taken together, the results of present study provide evidence for critical involvement of host lipid rafts and its constituents in adsorption process of Influenza A Virus and suggests crucial involvement in other early events of IAV life cycle. The present study opens a new domain to study influenza virus-host interaction and to combat flu at the very early steps of viral life cycle.

Keywords: lipid raft, adsorption, cholesterol, methyl-β-cyclodextrin, GM1

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Authors: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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Authors: Maham Leeza Adil, Mahrukh Alvi, Muhammad Osman

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Introduction: Sheehan's syndrome (SS) is a rare cause of hypopituitarism resulting from postpartum hemorrhage and pituitary necrosis. It remains an underdiagnosed condition, especially in developing countries, due to poor obstetric care and home deliveries. This case report highlights the significance of recognizing atypical presentations of SS, such as pancytopenia, to aid in early diagnosis and management. Case Presentation: A 40-year-old female presented with acute abdomen symptoms and was initially diagnosed with acalculous cholecystitis. However, a detailed history revealed a history of postpartum hemorrhage 18 years prior, leading to a provisional diagnosis of SS. Further investigations confirmed panhypopituitarism, including hypothyroidism, hypocortisolism, and hypogonadism. Notably, the patient also exhibited pancytopenia, a rarely reported hematological manifestation of SS. Discussion: SS often presents with nonspecific symptoms, leading to delayed or missed diagnoses. In this case, the patient's initial presentation of acute abdomen symptoms was attributed to secondary adrenal insufficiency due to panhypopituitarism. The presence of pancytopenia, along with hyponatremia, further complicated the clinical picture. Hormone replacement therapy led to a remarkable improvement in the patient's condition, emphasizing the importance of early diagnosis and intervention. Conclusion: SS is a common cause of panhypopituitarism in developing countries, but its atypical presentations, such as pancytopenia, are rare and often overlooked. This case highlights the need for increased awareness among clinicians to consider SS in patients with unexplained hematological abnormalities, particularly in regions with high rates of postpartum hemorrhage. Early recognition and appropriate hormone replacement therapy can significantly improve patients' outcomes and prevent long-term complications associated with this underdiagnosed syndrome.

Keywords: Sheehan syndrome, panhypopituitarism, pancytopenia, delayed diagnosis

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Authors: Huijing Wen, Daibao Guo

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Writing is thought to be the most complex facet of language arts. Assessing writing is difficult and subjective, and there are few scientifically validated assessments exist. Research has proposed evaluating writing using a multidimensional approach, including both qualitative and quantitative measures of handwriting, spelling and prose. Given that narrative writing has historically been a staple of literacy instruction in primary grades and is one of the three major genres Common Core State Standards required students to acquire starting in kindergarten, it is essential for teachers to understand how to measure beginning writers writing development and sources of writing difficulties through narrative writing. Guided by the theoretical models of early written expression and using empirical data, this study examines ways teachers can enact a comprehensive approach to understanding beginning writer’s narrative writing through three writing rubrics developed for a Curriculum-based Measurement (CBM). The goal is to help classroom teachers structure a framework for assessing early writing in primary classrooms. Participants in this study included 380 first-grade students from 50 classrooms in 13 schools in three school districts in a Mid-Atlantic state. Three writing tests were used to assess first graders’ writing skills in relation to both transcription (i.e., handwriting fluency and spelling tests) and translational skills (i.e., a narrative prompt). First graders were asked to respond to a narrative prompt in 20 minutes. Grounded in theoretical models of earlier expression and empirical evidence of key contributors to early writing, all written samples to the narrative prompt were coded three ways for different dimensions of writing: length, quality, and genre elements. To measure the quality of the narrative writing, a traditional holistic rating rubric was developed by the researchers based on the CCSS and the general traits of good writing. Students' genre knowledge was measured by using a separate analytic rubric for narrative writing. Findings showed that first-graders had emerging and limited transcriptional and translational skills with a nascent knowledge of genre conventions. The findings of the study provided support for the Not-So-Simple View of Writing in that fluent written expression, measured by length and other important linguistic resources measured by the overall quality and genre knowledge rubrics, are fundamental in early writing development. Our study echoed previous research findings on children's narrative development. The study has practical classroom application as it informs writing instruction and assessment. It offered practical guidelines for classroom instruction by providing teachers with a better understanding of first graders' narrative writing skills and knowledge of genre conventions. Understanding students’ narrative writing provides teachers with more insights into specific strategies students might use during writing and their understanding of good narrative writing. Additionally, it is important for teachers to differentiate writing instruction given the individual differences shown by our multiple writing measures. Overall, the study shed light on beginning writers’ narrative writing, indicating the complexity of early writing development.

Keywords: writing assessment, early writing, beginning writers, transcriptional skills, translational skills, primary grades, simple view of writing, writing rubrics, curriculum-based measurement

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Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

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Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial Information Science, remote sensing, surface elevation changes,

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Authors: I. Filikhin, B. Vlahovic

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We focus on a novel type of detection based on electron tunneling properties of double nanoscale structures in semiconductor materials. Semiconductor heterostructures as quantum wells (QWs), quantum dots (QDs), and quantum rings (QRs) may have energy level structure of several hundred of electron confinement states. The single electron spectra of the double quantum objects (DQW, DQD, and DQR) were studied in our previous works with relation to the electron localization and tunneling between the objects. The wave function of electron may be localized in one of the QDs or be delocalized when it is spread over the whole system. The localizing-delocalizing tunneling occurs when an electron transition between both states is possible. The tunneling properties of spectra differ strongly for “regular” and “chaotic” systems. We have shown that a small violation of the geometry drastically affects localization of electron. In particular, such violations lead to the elimination of the delocalized states of the system. The same symmetry violation effect happens if electrical or magnetic fields are applied. These phenomena could be used to propose a new type of detection based on the high sensitivity of charge transport between double nanostructures and small violations of the shapes. It may have significant technological implications.

Keywords: double quantum dots, single electron levels, tunneling, electron localizations

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Authors: Dawit Thomas

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Feeding and weaning practices vary from culture to culture and depend on different child-rearing values. The socio-cultural dimensions that influence the acceptable infant feeding practices are varied and complex. Understanding cultural differences in beliefs and practices relating to infant feeding is important to enhance designing programs for delivering successful psychological, social, physiological and economic well-being of mothers and infants. The main purpose of this study was exploring mothers infant feeding practices in the context of Hadiyya culture. After purposively selecting Harche Huyaya Uyaya Kebele eight infant feeding mothers were selected using snowball sampling technique. The study employed interviews and focus group discussion. The study found out early initiation and prolonged breastfeeding and early complementary feeding in some instances immediately after birth. In addition, infants were not forced to wean unless the mothers encounter pressing issues like pregnancy and health related problems. Furthermore, the main weaning techniques were putting unpleasant materials on the tip of nipples and sending infants to grandparents home. The study also found out gender difference in weaning, i.e., early initiation of weaning for girls. This can be indicative of gender-based bias on weaning practice. Finally, health extension workers, office of women and children affairs and Hadiyya Zone Tourism office should organize awareness raising programs to preserve vital infant feeding practices like prolonged breastfeeding and length of weaning. In addition, the offices should raise awareness among communities on negative side effects of sending infant to grandparents home that may weaken infant-mothers attachment and create favorable ground for the development of phobia.

Keywords: feeding, infant, practices, weaning

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Authors: Venencia Albert, Arulselvi Subramanian, Hara Prasad Pati, Asok K. Mukhophadhyay

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Acute traumatic coagulopathy in an endogenous dysregulation of the intrinsic coagulation system in response to the injury, associated with three-fold risk of poor outcome, and is more amenable to corrective interventions, subsequent to early identification and management. Multiple definitions for stratification of the patients' risk for early acute coagulopathy have been proposed, with considerable variations in the defining criteria, including several trauma-scoring systems based on prehospital data. We aimed to develop a clinically relevant definition for acute coagulopathy of trauma based on conventional coagulation assays and to assess its efficacy in comparison to recently established prehospital prediction models. Methodology: Retrospective data of all trauma patients (n = 490) presented to our level I trauma center, in 2014, was extracted. Receiver operating characteristic curve analysis was done to establish cut-offs for conventional coagulation assays for identification of patients with acute traumatic coagulopathy was done. Prospectively data of (n = 100) adult trauma patients was collected and cohort was stratified by the established definition and classified as "coagulopathic" or "non-coagulopathic" and correlated with the Prediction of acute coagulopathy of trauma score and Trauma-Induced Coagulopathy Clinical Score for identifying trauma coagulopathy and subsequent risk for mortality. Results: Data of 490 trauma patients (average age 31.85±9.04; 86.7% males) was extracted. 53.3% had head injury, 26.6% had fractures, 7.5% had chest and abdominal injury. Acute traumatic coagulopathy was defined as international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s. Of the 100 adult trauma patients (average age 36.5±14.2; 94% males), 63% had early coagulopathy based on our conventional coagulation assay definition. Overall prediction of acute coagulopathy of trauma score was 118.7±58.5 and trauma-induced coagulopathy clinical score was 3(0-8). Both the scores were higher in coagulopathic than non-coagulopathic patients (prediction of acute coagulopathy of trauma score 123.2±8.3 vs. 110.9±6.8, p-value = 0.31; trauma-induced coagulopathy clinical score 4(3-8) vs. 3(0-8), p-value = 0.89), but not statistically significant. Overall mortality was 41%. Mortality rate was significantly higher in coagulopathic than non-coagulopathic patients (75.5% vs. 54.2%, p-value = 0.04). High prediction of acute coagulopathy of trauma score also significantly associated with mortality (134.2±9.95 vs. 107.8±6.82, p-value = 0.02), whereas trauma-induced coagulopathy clinical score did not vary be survivors and non-survivors. Conclusion: Early coagulopathy was seen in 63% of trauma patients, which was significantly associated with mortality. Acute traumatic coagulopathy defined by conventional coagulation assays (international normalized ratio ≥ 1.19; prothrombin time ≥ 15.5 s; activated partial thromboplastin time ≥ 29 s) demonstrated good ability to identify coagulopathy and subsequent mortality, in comparison to the prehospital parameter-based scoring systems. Prediction of acute coagulopathy of trauma score may be more suited for predicting mortality rather than early coagulopathy. In emergency trauma situations, where immediate corrective measures need to be taken, complex multivariable scoring algorithms may cause delay, whereas coagulation parameters and conventional coagulation tests will give highly specific results.

Keywords: trauma, coagulopathy, prediction, model

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Authors: Roseminu Varghese, Laveena Anitha Barboza, Jyothi Chakrabarty, Ravishankar

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Depression among post-stroke survivors is prevalent, but it is unidentified. The purpose of this review was to determine the pooled prevalence of depression among post-stroke survivors in the South Asian region from all published health sciences research articles. The review also aimed to analyze the disparities in the prevalence of depression among the post-stroke survivors from different study locations. Data search to identify the relevant research articles published from 2005 to 2016 was done by using mesh terms and keywords in Web of Science, PubMed Medline, CINAHL, Scopus, J gate, IndMED databases. The final analysis comprised of 9 studies, including a population of 1,520 men and women. Meta-analysis was performed in STATA version 13.0. The overall pooled post-stroke depression prevalence was 0.46, 95% (CI), (0.3- 0.62). The prevalence rate in this systematic review is evident of depression among post-stroke survivors in the South Asian Region. Identifying the prevalence of post-stroke depression at an early stage is important to improve outcomes of the rehabilitative process of stroke survivors and for its early intervention.

Keywords: depression, post stroke survivors, prevalence, systematic review

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Authors: Akila Murali, Prithika Manivel

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Disasters are defined as a serious disruption of the functioning of a community or a society, which involves widespread human, material, economic or environmental impacts. The number of people suffering food crisis as a result of natural disasters has tripled in the last thirty years. The economic losses due to natural disasters have shown an increase with a factor of eight over the past four decades, caused by the increased vulnerability of the global society, and also due to an increase in the number of weather-related disasters. Efficient disaster detection and alerting systems could reduce the loss of life and properties. In the event of a disaster, another important issue is a good search and rescue system with high levels of precision, timeliness and safety for both the victims and the rescuers. Wireless Sensor Networks technology has the capability of quick capturing, processing, and transmission of critical data in real-time with high resolution. This paper studies the capacity of sensors and a Wireless Sensor Network to collect, collate and analyze valuable and worthwhile data, in an ordered manner to help with disaster management.

Keywords: alerting systems, disaster detection, Ad Hoc network, WSN technology

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Abrahão S. Fontes, Carlos A. V. Cardoso, Levi P. B. Oliveira

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The induction motors are used worldwide in various industries. Several maintenance techniques are applied to increase the operating time and the lifespan of these motors. Among these, the predictive maintenance techniques such as Motor Current Signature Analysis (MCSA), Motor Square Current Signature Analysis (MSCSA), Park's Vector Approach (PVA) and Park's Vector Square Modulus (PVSM) are used to detect and diagnose faults in electric motors, characterized by patterns in the stator current frequency spectrum. In this article, these techniques are applied and compared on a real motor, which has the fault of eccentricity in the air-gap. It was used as a theoretical model of an electric induction motor without fault in order to assist comparison between the stator current frequency spectrum patterns with and without faults. Metrics were purposed and applied to evaluate the sensitivity of each technique fault detection. The results presented here show that the above techniques are suitable for the fault of eccentricity in the air gap, whose comparison between these showed the suitability of each one.

Keywords: eccentricity in the air-gap, fault diagnosis, induction motors, predictive maintenance

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Authors: Kripesh Adhikari, Hamid Bouchachia, Hammadi Nait-Charif

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Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls.

Keywords: fall detection, machine learning, deep learning, pose estimation, tracking

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Authors: Al Romano, Jeryl C. Hije, Mechaela Marie O. Tabiolo

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The decline in water quality is a major factor in aquatic disease outbreaks and can lead to significant mortality among aquatic organisms. Understanding the relationship between dissolved oxygen (DO) and water temperature is crucial, as these variables directly impact the health, behavior, and survival of fish populations. This study investigated how DO levels, water temperature, and atmospheric temperature interact in Sampaloc Lake to assess the risk of fish mortality. By employing a combination of linear regression models and machine learning techniques, researchers developed predictive models to forecast DO concentrations at various depths. The results indicate that while DO levels generally decrease with depth, the predicted concentrations are sufficient to support the survival of common fish species in Sampaloc Lake during March, April, and May 2025.

Keywords: aquaculture, dissolved oxygen, water temperature, regression analysis, machine learning, fish mass mortality, early warning system

Procedia PDF Downloads 32

Authors: Atuhurra Jesse, N'guessan Yves-Roland Douha, Pabitra Lenka

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The detection of diseases within plants has attracted a lot of attention from computer vision enthusiasts. Despite the progress made to detect diseases in many plants, there remains a research gap to train image classifiers to detect the cacao swollen shoot virus disease or CSSVD for short, pertinent to cacao plants. This gap has mainly been due to the unavailability of high quality labeled training data. Moreover, institutions have been hesitant to share their data related to CSSVD. To fill these gaps, image classifiers to detect CSSVD-infected cacao plants are presented in this study. The classifiers are based on VGG16, ResNet50 and Vision Transformer (ViT). The image classifiers are evaluated on a recently released and publicly accessible KaraAgroAI Cocoa dataset. The best performing image classifier, based on ResNet50, achieves 95.39\% precision, 93.75\% recall, 94.34\% F1-score and 94\% accuracy on only 20 epochs. There is a +9.75\% improvement in recall when compared to previous works. These results indicate that the image classifiers learn to identify cacao plants infected with CSSVD.

Keywords: CSSVD, image classification, ResNet50, vision transformer, KaraAgroAI cocoa dataset

Procedia PDF Downloads 101

Authors: Mohammad Mozammel Haque

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That feminism is nothing but the name of a belief that women should have the same rights as men needs no telling. The history of modern western feminism is divided into three waves and each is described as dealing with different aspects of the same feminist issues. The first wave refers to the movement of the 19th through early 20th centuries, which dealt mainly with suffrage, working conditions and educational rights for women. The second wave (1960s-1980s) dealt with the inequality of laws and the role of women in society. The third wave (late 1980s-early 2000s) is seen as both a continuation of the second wave and a response to the perceived failures. Mary Wollstonecraft struggled for the emancipation and freedom of the women of Europe, Begum Rokeya brought about revolution for the women of the East and West Bengal, Jeremy Bentham wrote for the independence of women in England. But if feminism refers to the movement of giving women what they deserve, then it won’t be an overstatement to state that Mohammad is the precursor of what we call feminism. This paper investigates the background of official starting of feminism, and also the backdrop of the women of Muhammad’s time. The article, besides showing that this great prophet and philosopher firstly brought about a movement for the education and rights of women and took them out of grave where they were buried alive, also delineates Mohammedan endeavours he attempted to give the women what they ought to have.

Keywords: education, equality, feminism, precursor

Procedia PDF Downloads 495

Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

Procedia PDF Downloads 200

Authors: Hongyu Chen, Li Jiang

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Ubiquitous anomalies endanger the security of our system con- stantly. They may bring irreversible damages to the system and cause leakage of privacy. Thus, it is of vital importance to promptly detect these anomalies. Traditional supervised methods such as Decision Trees and Support Vector Machine (SVM) are used to classify normality and abnormality. However, in some case, the abnormal status are largely rarer than normal status, which leads to decision bias of these methods. Generative adversarial network (GAN) has been proposed to handle the case. With its strong generative ability, it only needs to learn the distribution of normal status, and identify the abnormal status through the gap between it and the learned distribution. Nevertheless, existing GAN-based models are not suitable to process data with discrete values, leading to immense degradation of detection performance. To cope with the discrete features, in this paper, we propose an efficient GAN-based model with specifically-designed loss function. Experiment results show that our model outperforms state-of-the-art models on discrete dataset and remarkably reduce the overhead.

Keywords: GAN, discrete feature, Wasserstein distance, multiple intermediate layers

Procedia PDF Downloads 128

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

Procedia PDF Downloads 78

Authors: Aakriti Lohiya

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This study examines the commonly recognised childhood trauma that can have a significant and enduring effect on a person's cognitive and psychological health. The purpose of this study was to look at the intricate interactions that exist between negative self-identity, cognitive distortions, and early trauma. For the study, a sample of (200 women were taken, who were socially active) was gathered. Standardised measures were utilised to evaluate the participants' experiences of childhood trauma, and validated psychological tools were employed to assess negative self-identity and cognitive distortions. The links and predicting correlations between childhood trauma, negative self-identity, and cognitive distortions were investigated using statistical techniques, such as correlation analysis and multiple regression modelling. The results demonstrated that there is no correlation between the degree of early trauma and the emergence of a negative self-identity and cognitive distortions. It examines whether cognitive distortion and events in childhood have any relationship with negative self-identity using various scales. Participants completed the Childhood Trauma Questionnaire, which assessed retrospective accounts of childhood trauma; the Cognitive Distortions Scale, which measured internal attributions and perceptions of controllability; and the attachment style questionnaire, which assessed the attachment attribute of their daily life, which will lead negative. The implications for therapy were also considered.

Keywords: cognitive distortion, therapy, childhood trauma, attachment

Procedia PDF Downloads 77

Authors: Yaara Shilo

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Early childhood education and care (ECEC)in Israel has undergone extensive reform and now requires daycare centers to meet internationally recognized professional standards. Since 1948, one of the aims of childcare facilities was to enable women’s participation in the workforce.A 1965 law grouped daycare centers for young children with facilities for the elderly and for disabled persons under the same authority. In the 1970’s, ECEC leaders sought to change childcare from proprietary to educational facilities. From 1976 deliberations in the Knesset regarding appropriate attribution of ECEC frameworks resulted in their being moved to various authorities that supported women’s employment: Ministries of Finance, Industry, and Commerce, as well as the Welfare Department. Prior to 2018, 75% of infants and toddlers in institutional care were in unlicensed and unsupervised settings. Legislative processes accompanied the conceptual change to an eventual appropriate attribution of ECEC frameworks. Position papers over the past two decades resulted in recommendations for standards conforming to OECD regulations. Simultaneous incidents of child abuse, some resulting in death, riveted public attention to the need for adequate government supervision, accelerating the legislative process. Appropriate care for very young children must center on quality interactions with caregivers, thus requiring adequate staff training. Finally, in 2018 a law was passed stipulating standards for staff training, proper facilities, child-adult ratios, and safety measures. The Ariav commission expanded training to caregivers for ages 0-3. Transfer of the ECEC to the Ministry of Education ensured establishment of basic training. Groundwork created by new legislation initiated professional development of EC educators for ages 0-3. This process should raise salaries and bolster the system’s ability to attract quality employees. In 2022 responsibility for ECEC ages 0-3 was transferred from the Ministry of Finance to the Ministry of Education, shifting emphasis from proprietary care to professional considerations focusing on wellbeing and early childhood education. The recent revolutionary changes in ECEC point to a new age in the care and education of Israel’s youngest citizens. Implementation of international standards, adequate training, and professionalization of the workforce focus on the child’s needs.

Keywords: policy, early childhood, care and education, daycare, development

Procedia PDF Downloads 114

Authors: S. Delgado, C. Cerrada, R. S. Gómez

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This research introduces an approach to voxelizing the surfaces of triangular meshes with efficiency and accuracy. Our method leverages parallel equidistant scan-lines and introduces a Gap Detection technique to address the limitations of existing approaches. We present a comprehensive study showcasing the method's effectiveness, scalability, and versatility in different scenarios. Voxelization is a fundamental process in computer graphics and simulations, playing a pivotal role in applications ranging from scientific visualization to virtual reality. Our algorithm focuses on enhancing the voxelization process, especially for complex models and high resolutions. One of the major challenges in voxelization in the Graphics Processing Unit (GPU) is the high cost of discovering the same voxels multiple times. These repeated voxels incur in costly memory operations with no useful information. Our scan-line-based method ensures that each voxel is detected exactly once when processing the triangle, enhancing performance without compromising the quality of the voxelization. The heart of our approach lies in the use of parallel, equidistant scan-lines to traverse the interiors of triangles. This minimizes redundant memory operations and avoids revisiting the same voxels, resulting in a significant performance boost. Moreover, our method's computational efficiency is complemented by its simplicity and portability. Written as a single compute shader in Graphics Library Shader Language (GLSL), it is highly adaptable to various rendering pipelines and hardware configurations. To validate our method, we conducted extensive experiments on a diverse set of models from the Stanford repository. Our results demonstrate not only the algorithm's efficiency, but also its ability to produce 26 tunnel free accurate voxelizations. The Gap Detection technique successfully identifies and addresses gaps, ensuring consistent and visually pleasing voxelized surfaces. Furthermore, we introduce the Slope Consistency Value metric, quantifying the alignment of each triangle with its primary axis. This metric provides insights into the impact of triangle orientation on scan-line based voxelization methods. It also aids in understanding how the Gap Detection technique effectively improves results by targeting specific areas where simple scan-line-based methods might fail. Our research contributes to the field of voxelization by offering a robust and efficient approach that overcomes the limitations of existing methods. The Gap Detection technique fills a critical gap in the voxelization process. By addressing these gaps, our algorithm enhances the visual quality and accuracy of voxelized models, making it valuable for a wide range of applications. In conclusion, "Closing the Gap: Efficient Voxelization with Equidistant Scan-lines and Gap Detection" presents an effective solution to the challenges of voxelization. Our research combines computational efficiency, accuracy, and innovative techniques to elevate the quality of voxelized surfaces. With its adaptable nature and valuable innovations, this technique could have a positive influence on computer graphics and visualization.

Keywords: voxelization, GPU acceleration, computer graphics, compute shaders

Procedia PDF Downloads 71

Authors: Samiul Islam Hazarika, Domngam Boje, Ananta Kumar Atta

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It is well familiar that solvents play a significant role in modern chemistry. Solvents can change the reactivity and physicochemical properties of molecules in a solution. Keeping this in mind, we have designed and synthesized a mono-triazolyl-linked pyrenyl-appended xylofuranose derivative for the detection of metal ions with changing solvent systems. The incorporation of a sugar backbone in the sensor increases the water solubility and biocompatibility. The experimental study revealed that the xylofuranose-based fluorescence probe did not exhibit any specific selectivity towards metal ions in acetonitrile (CH₃CN) solvent. Whereas, we revealed that triazole-linked pyrenyl-appended xylofuranose-based fluorescent sensor would exhibit high selectivity and sensitivity towards Au³⁺ ions in CH₃CN-H₂O (1/1, v/v) system. This observation might be explained by the viscosity and polarity differences of CH₃CN and CH₃CN-H₂O solvent systems. The formation of the sensor-Au³⁺ complex was also established by high-resolution mass spectrometry (HRMS) data of the complex.

Keywords: triazole, furanose, fluorometric, solvent dependent

Procedia PDF Downloads 114