Search results for: mental health detection

Authors: N. Hammid, F. Bouarab-Dahmani, T. Berkane

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Since its appearance, the MOOC (massive open online course) is gaining more and more intention of the educational communities over the world. Apart from the current MOOCs design and purposes, the creators of MOOC focused on the importance of the connection and knowledge exchange between individuals in learning. In this paper, we present a conceptual model for massive open online blended courses where teachers over the world can collaborate and exchange their experience to get a common efficient content designed as a MOOC opened to their students to live a better learning experience. This model is based on disciplines’ concepts capitalization and the detection of the obstacles met by their students when faced with problem situations (exercises, projects, case studies, etc.). This detection is possible by analyzing the frequently of semantic errors committed by the students. The participation of teachers in the design of the course and the attendance by their students can guarantee an efficient and extensive participation (an important number of participants) in the course, the learners’ motivation and the evaluation issues, in the way that the teachers designing the course assess their students. Thus, the teachers review, together with their knowledge, offer a better assessment and efficient connections to their students.

Keywords: massive open online course, MOOC, online learning, e-learning

Procedia PDF Downloads 269

Authors: Aliya Ashraf, Mehreen Sirshar, Fatima Akhtar, Farwa Kazmi, Jawaria Wazir

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Vision, the basis for performing navigational tasks, is absent or greatly reduced in visually impaired people due to which they face many hurdles. For increasing the navigational capabilities of visually impaired people a desktop application ODAPTA is presented in this paper. The application uses camera to capture video from surroundings, apply various image processing algorithms to get information about path and obstacles, tracks them and delivers that information to user through voice commands. Experimental results show that the application works effectively for straight paths in daylight.

Keywords: visually impaired, ODAPTA, Region of Interest (ROI), driver fatigue, face detection, expression recognition, CCD camera, artificial intelligence

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Authors: Amanda Noble

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The process of exiting/escaping situations of sex trafficking can be arduous and fraught with numerous barriers. In this paper the results of a national Canadian study on escaping situations of sex trafficking is discussed. Surveys and focus groups were conducted with 201 stakeholders in 8 cities, including 50 survivors of sex trafficking, service providers, health care providers and police. The results show that survivors are both vulnerable to being exploited and experience barriers to exiting as a result of structural factors such as colonialism, poverty, and discrimination based on race and gender. Survivors also face numerous barriers within various systems such as child welfare and the legal system. In addition, survivors contend with multiple psychological and psychosocial factors when exiting including the trauma bond, complex trauma and mental health concerns, substance use, isolation, and adjusting to ‘mainstream’ life. In light of these factors, the service needs of survivors escaping sex trafficking are discussed, and promising practices, such as trauma-informed practice and working from a stages of change model are outlined. This paper is useful for service providers that work with survivors, policy makers, or anyone who has ever wondered why survivors that are not being physically detained don’t ‘just leave’ or escape their exploitative situations.

Keywords: Barriers, Exiting, Promising Practices, Sex Trafficking

Procedia PDF Downloads 100

Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

Procedia PDF Downloads 100

Authors: Charles Lee

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The Principal Orthogonal Decomposition (POD) technique has been used as a model reduction tool for many applications in engineering and science. In principle, one begins with an ensemble of data, called snapshots, collected from an experiment or laboratory results. The beauty of the POD technique is that when applied, the entire data set can be represented by the smallest number of orthogonal basis elements. It is the such capability that allows us to reduce the complexity and dimensions of many physical applications. Mathematical formulations and numerical schemes for the POD method will be discussed along with applications in NASA’s Deep Space Large Antenna Arrays, Satellite Image Reconstruction, Cancer Detection with DNA Microarray Data, Maximizing Stock Return, and Medical Imaging.

Keywords: reduced-order methods, principal component analysis, cancer detection, image reconstruction, stock portfolios

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Authors: K. Frahtia, I. Mihoubi, S. Picot

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Leishmaniasis is a group of parasitic disease with a varied clinical expression caused by flagellate protozoa of the Leishmania genus. These diseases are transmitted to humans and animals by the sting of a vector insect, the female sandfly. Among the groups of dipteral disease vectors, Phlebotominae occupy a prime position and play a significant role in human pathology, such as leishmaniasis that affects nearly 350 million people worldwide. The vector control operation launched by health services throughout the country proves to be effective since despite the prevalence of the disease remains high especially in rural areas, leishmaniasis appears to be declining in Algeria. In this context, this study mainly concerns molecular detection of Leishmania from the vector. Furthermore, a molecular diagnosis has also been made on skin samples taken from patients in the region of Constantine, located in the North-East of Algeria. Concerning the vector, 5858 sandflies were captured, including 4360 males and 1498 females. Male specimens were identified based on their morphological. The morphological identification highlighted the presence of the Phlebotomus genus with a prevalence of 93% against 7% represented by the Sergentomyia genus. About the identified species, P. perniciosus is the most abundant with 59.4% of the male identified population followed by P. longicuspis with 24.7% of the workforce. P. perfiliewi is poorly represented by 6.7% of specimens followed by P. papatasi with 2.2% and 1.5% S. dreyfussi. Concerning skin samples, 45/79 (56.96%) collected samples were found positive by real-time PCR. This rate appears to be in sharp decline compared to previous years (alert peak of 30,227 cases in 2005). Concerning the detection of Leishmania from sandflies by RT-PCR, the results show that 3/60 PCR performed genus are positive with melting temperatures corresponding to that of the reference strain (84.1 +/- 0.4 ° C for L. infantum). This proves that the vectors were parasitized. On the other side, identification by RT-PCR species did not give any results. This could be explained by the presence of an insufficient amount of leishmanian DNA in the vector, and therefore support the hypothesis of the regression of leishmaniasis in Constantine.

Keywords: Algeria, molecular diagnostic, phlebotomus, real time PCR

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Authors: Anindita Sen

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Oral fluid is a promising diagnostic matrix for drugs of abuse compared to urine and serum. Detection of methamphetamine in oral fluid would pave way for the easy evaluation of impairment in drivers during roadside drug testing as well as ensure safe working environments by facilitating evaluation of impairment in employees at workplaces. A membrane-based point-of-care (POC) friendly pre-treatment technique has been developed which aided elimination of interferences caused by salivary proteins and facilitated the demonstration of methamphetamine detection in saliva using a gold nanoparticle based colorimetric aptasensor platform. It was found that the colorimetric response in saliva was always suppressed owing to the matrix effects. By navigating the challenging interfering issues in saliva, we were successfully able to detect methamphetamine at nanomolar levels in saliva offering immense promise for the translation of these platforms for on-site diagnostic systems. This subsequently motivated the development of a handheld portable light meter device that can reliably transduce the aptasensors colorimetric response into absorbance, facilitating quantitative detection of analyte concentrations on-site. This is crucial due to the prevalent unreliability and sensitivity problems of the conventional drug testing kits. The fabricated light meter device response was validated against a standard UV-Vis spectrometer to confirm reliability. The portable and cost-effective handheld detector device features sensitivity comparable to the well-established UV-Vis benchtop instrument and the easy-to-use device could potentially serve as a prototype for a commercial device in the future.

Keywords: aptasensors, colorimetric gold nanoparticle assay, point-of-care, oral fluid

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Authors: Yu Liang, Wei Wei

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Spatial representation of numbers and numerical magnitude are important for preschoolers’ mathematical ability. Mental number line, a typical index to measure numbers spatial representation, and numerical comparison are both related to arithmetic obviously. However, they seem to rely on different mechanisms and probably influence arithmetic through different mechanisms. In line with this idea, preschool children were trained with two tasks to investigate which one is more important for approximate arithmetic. The training of numerical processing and number line estimation were proved to be effective. They both improved the ability of approximate arithmetic. When the difficulty of approximate arithmetic was taken into account, the performance in number line training group was not significantly different among three levels. However, two harder levels achieved significance in numerical comparison training group. Thus, comparing spatial representation ability, symbolic approximation arithmetic relies more on numerical magnitude. Educational implications of the study were discussed.

Keywords: approximate arithmetic, mental number line, numerical magnitude, preschooler

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Authors: William L. Whitehouse, Louisa H. Y. Lo, Andrew B. Kinghorn, Simon C. C. Shiu, Julian. A. Tanner

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C-reactive protein (CRP) is an acute-phase reactant and sensitive indicator for sepsis and other life-threatening pathologies, including systemic inflammatory response syndrome (SIRS). Currently, clinical turn-around times for established CRP detection methods take between 30 minutes to hours or even days from centralized laboratories. Here, we report the development of an electrochemical biosensor using redox probe-tagged DNA aptamers functionalized onto cheap, commercially available screen-printed electrodes. Binding-induced conformational switching of the CRP-targeting aptamer induces a specific and selective signal-ON event, which enables single-step and reagentless detection of CRP in as little as 1 minute. The aptasensor dynamic range spans 5-1000nM (R=0.97) or 5-500nM (R=0.99) in 50% diluted human serum, with a LOD of 3nM, corresponding to 2-orders of magnitude sensitivity under the clinically relevant cut-off for CRP. The sensor is stable for up to one week and can be reused numerous times, as judged from repeated real-time dosing and dose-response assays. By decoupling binding events from the signal induction mechanism, structure-switching electrochemical aptamer-based sensors (SS-EABs) provide considerable advantages over their adsorption-based counterparts. Our work expands on the retinue of such sensors reported in the literature and is the first instance of an SS-EAB for reagentless CRP detection. We hope this study can inspire further investigations into the suitability of SS-EABs for diagnostics, which will aid translational R&D toward fully realized devices aimed at point-of-care applications or for use more broadly by the public.

Keywords: structure-switching, C-reactive protein, electrochemical, biosensor, aptasensor.

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Authors: Christine Eickhoff, Alyssa Adams, Alaine Duncan

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The Washington, DC Veterans Affairs Medical Center (DC VAMC) offers complementary and integrative health (CIH) services such as acupuncture, yoga, meditation, and nutrition education through a coordinated outpatient clinic. The primary population utilizing CIH services are veterans with chronic pain. Acupuncture is one of the most popular of the CIH services available at the DC VAMC. As interest and availability grows, it is important to measure health outcomes associated with CIH service utilization. The purpose of this study was to investigate pain and mental health outcomes for veterans with chronic pain enrolled in individual acupuncture services in the DC VAMC. Veterans at the DC VAMC with self-identified chronic pain and no prior acupuncture experience were recruited for the study (n=70). Veterans were referred for services by a medical provider and completed baseline assessments at the program orientation prior to participating in any CIH services. Veterans received four individual, full-body acupuncture appointments within four weeks of study enrollment. After the first month, participants were scheduled for six appointments that occurred every two weeks and then eight more sessions that were scheduled one month apart. Follow-up assessments were administered at 2, 4, 6, 8, and 12 months. The findings reported will include completed time points at two and four months. Measures include a demographics survey, the Measure Yourself Medical Outcome Profile-2 (MYMOP-2), The Beck Depression Inventory (BDI-II), the Defense Veterans Pain Rating Scale (DVPRS), and the Pain Disability Questionnaire (PDQ). In this sample, 67% identified a pain condition as their primary health concern. Between baseline and two-month follow-up, there were significant improvements in participants’ primary health concern (MYMOP-2 p=0.010), general wellbeing (MYMOP-2 p=0.011), and a significant decrease in the use of medication (MYMOP-2 p<0.000). Between 2 and 4-month follow-up, pain disability (PDQ p=0.035), pain rating (DVPRS p=0.027), and depression (BDI-II p=0.003) significantly improved. Preliminary findings indicate that individual acupuncture therapy can be effective at improving health outcomes, well-being, and decreasing medication use in U.S. military veterans with chronic pain. Findings also suggest that individual acupuncture therapy can improve pain ratings, pain disability, and depression in veterans with chronic pain.

Keywords: acupuncture, chronic pain, depression, integrative health, medication use, military, pain, veterans, wellbeing

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

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Authors: Titap Yazicioglu

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Purpose: Losing an eye due to trauma is a challenging situation that reduces the quality of life by causing physical, cosmetic, and serious psychological problems. This study aimed to evaluate the effect of aesthetic rehabilitation on the change in psychological status and quality of life of patients with eye loss resulting in phthisis bulbi. Materials and Methods: The files of 25 males and 15 females with an average age of 27.5 years who had trauma-related phthisic eye and had applied to the Department of Ophthalmology at the Dr. Lütfi Kırdar Kartal Education and Research Hospital, Istanbul, Turkey, for aesthetic rehabilitation during the years 2015-19 were retrospectively analyzed. The Beck Depression Inventory (BDI), Beck Anxiety Scale(BAS), and Short Form 36 quality-of-life survey (SF-36) were used to collect data on admission and one-year follow-up. Results: Of the patients reviewed, 65% stated that eye loss had caused their avoidance of family and social environments; 30% had moderate anxiety and depression. The post-operative sub-dimension scores of general health, emotional role function, and physical and social functions were statistically significant (p = 0.001; p<0.01), but the variations in the sub-dimension scores of vitality, mental health, and total physical health were not (p>0.05). Conclusion: Lossing an eye is a traumatic event that can affect all aspects of a person’s social and professional life. A patient who has suffered from the psychological damage of physical loss of eye needs a prosthesis that can give the desired function and appearance in different aspects of life.

Keywords: eye loss, phthisis bulbi, quality of life, psychological trauma

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Authors: Sethu Lekshmi, Shahanas, Prettha P.

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All existing protocols in wireless networks are mainly based on Carrier Sense Multiple Access with Collision avoidance. By applying collision detection in wireless networks, the time spent on collision can be reduced and thus improves system throughput. However in a real WLAN scenario due to the use of nonlinear modulation techniques only receiver can decided whether a packet loss take place, even there are multiple transmissions. In this proposed method, the receiver or Access Point detects the collision when multiple data packets are transmitted from different wireless stations. Whenever the receiver detects a collision, it transmits a jamming signal to all the transmitting stations so that they can immediately stop their on-going transmissions. We also provide preferential access to all collided packet to reduce unfairness and to increase system throughput by reducing contention. However, this preferential access will not block the channel for the long time. Here, an in-band transmission is considered in which both the data frames and control frames are transmitted in the same channel. We also provide a simple mathematical model for the proposed protocol and give the simulation result of WLAN scenario under various capture thresholds.

Keywords: 802.11, WLAN, capture effect, collision detection, collision resolution, receiver initiated

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Authors: Olomon Wu, Hung Lu, Nick Wilkins, Daniel Kerr, Zekeriya Aliyazicioglu, H. K. Hwang

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Multiple Input Multiple Output (MIMO) radar has received increasing attention in recent years. MIMO radar has many advantages over conventional phased array radar such as target detection, resolution enhancement, and interference suppression. In this paper, the results are presented from a simulation study of MIMO Uniformly-Spaced Linear Array (ULA) antennas. The performance is investigated under varied parameters, including varied array size, Pseudo Random (PN) sequence length, number of snapshots, and Signal to Noise Ratio (SNR). The results of MIMO are compared to a traditional array antenna.

Keywords: MIMO radar, phased array antenna, target detection, radar signal processing

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Authors: Mutaz Shurahabeel Ahmed Ombada

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This paper investigates potential health and financial settlement of health information technology, this paper evaluates health care with the use of IT and other associated industries. It assesses prospective savings and costs of extensive acceptance of Electronic Medical Record Systems (EMRS), models significant to health as well as safety remuneration, and conclude that efficient EMRS execution and networking could ultimately save more than US $55 billion annually through recuperating health care effectiveness and that Health Information Technology -enabled prevention and administration of chronic disease could eventually double those savings while rising health and other social remuneration. On the contrary, this is improbable to be realized without related to significant modifications to the health care system.

Keywords: electronic medical record systems, health care economics, EMRS

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Authors: Amir Abbas Fatemi, Zahra Tabrizian, Kabir Sadeghi

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To find the location and severity of damage that occurs in a structure, characteristics changes in dynamic and static can be used. The non-destructive techniques are more common, economic, and reliable to detect the global or local damages in structures. This paper presents a non-destructive method in structural damage detection and assessment using GA and static data. Thus, a set of static forces is applied to some of degrees of freedom and the static responses (displacements) are measured at another set of DOFs. An analytical model of the truss structure is developed based on the available specification and the properties derived from static data. The damages in structure produce changes to its stiffness so this method used to determine damage based on change in the structural stiffness parameter. Changes in the static response which structural damage caused choose to produce some simultaneous equations. Genetic Algorithms are powerful tools for solving large optimization problems. Optimization is considered to minimize objective function involve difference between the static load vector of damaged and healthy structure. Several scenarios defined for damage detection (single scenario and multiple scenarios). The static damage identification methods have many advantages, but some difficulties still exist. So it is important to achieve the best damage identification and if the best result is obtained it means that the method is Reliable. This strategy is applied to a plane truss. This method is used for a plane truss. Numerical results demonstrate the ability of this method in detecting damage in given structures. Also figures show damage detections in multiple damage scenarios have really efficient answer. Even existence of noise in the measurements doesn’t reduce the accuracy of damage detections method in these structures.

Keywords: damage detection, finite element method, static data, non-destructive, genetic algorithm

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Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

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Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

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Authors: Abdulsallam A. Bakdash, Aiyshah M. Alshehri, Hind M. Alenzi

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Benzatropine (benztropine) is used to treat symptoms of Parkinson's disease or involuntary movements due to the side effects of certain psychiatric drugs. We report in this study, results of a procedure for the determination of benzatropine in hair using LLE, once with methanol and second with phosphate buffer (pH 6.0), followed by filtration and then re-extraction with dichloromethane. A GC/MS method was developed and validated for this determination using selected ion monitoring (SIM) detection without derivatization. Linearity established over the concentration range 0.1-20.0 ng/mg hair, and the correlation coefficients were greater than 0.99. Recoveries were 52.2% and 21.1% using methanol and phosphate buffer extraction, respectively. Detection limits of benzatropine in hair were between 0.65 and 3.0 ng/mg hair, while the accuracy were 10.4% and 18.5% (RSD), respectively. We also applied this method to the analysis of soaked hair samples and demonstrated that the LLE using methanol meets the requirement for the analysis of benzatropine in hair.

Keywords: hair analysis, benzatropine, liquid-liquid extraction, GC/MS

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Authors: Hajnalka Szarvas

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The research examines the effectiveness of e-participation tools and methods from a point of view of cultural fitting to the Hungarian community traditions. Participation can have very different meanings depending on the local cultural and historical traditions, experiences of the certain societies. Generally when it is about e-democracy or e-participation tools most of the researches are dealing with its technological sides and novelties, but there is not much said about the cultural and social context of the different platforms. However from the perspective of their success it would be essential to look at the human factor too, the actual users, how the certain DMS or any online platform is fitting to the way of thought, the way of functioning of the certain society. Therefore the paper will explore that to what extent the different online platforms like Loomio, Democracy OS, Your Priorities EVoks, Populus, miutcank.hu, Liquid Democracy, Brain Bar Budapest Lab are compatible with the Hungarian mental structures and community traditions, the contents of collective mind about community functioning. As a result the influence of cultural embeddedness of the logic of e-participation development tools on success of these methods will be clearly seen. Furthermore the most crucial factors in general which determine the efficiency of e-participation development tools in Hungary will be demonstrated.

Keywords: cultural embeddedness, e-participation, local community traditions, mental structures

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Authors: Muhammad Solikhudin Nafi, Wahyu Afif Mufida, Mita Erna Wati, Fitri Setyani Rokim, M. Al-Rizqi Dharma Fauzi

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High dose activity without any pre-exercise will impact Delayed Onset Muscle Soreness (DOMS). DOMS known as delayed pain post-exercise and induce skeletal injury which will decrease athletes’ performances. From now on, post-exercise muscle damage can be detected by measuring skeletal troponin I (sTnI) concentration in serum using ELISA but this method needs more time and cost. To prevent decreased athletes performances, screening need to be done rapidly. We want to introduce our new prototype to detect DOMS acutely. Rapid detection tests are based on immunological reaction between skeletal troponin I antibodies and sTnI in human serum or whole blood. Chemical methods that are used in the manufacture of diagnostic test is lateral flow immunoassay. The material used is rat monoclonal antibody sTnI, colloidal gold, anti-mouse IgG, nitrocellulose membrane, conjugate pad, sample pad, wick and backing card. The procedure are made conjugate (colloidal gold and mAb sTnI) and insert into the conjugate pad, gives spray sTnI mAb and anti-mouse IgG into nitrocellulose membrane, and assemble RDT. RDT had been evaluated by measuring the sensitivity of positive human serum (n = 30) and negative human serum (n = 30). Overall sensitivity value was 93% and specificity value was 90%. ADCOR as the first rapid detection test qualitatively showed antigen-antibody reaction and showed good overall performances for screening of muscle damage. Furthermore, these finding still need more improvements to get best results.

Keywords: DOMS, sTnI, rapid detection test, ELISA

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Authors: Ernest Tambo, Ghislaine Madjou, Jeanne Y. Ngogang, Shenglan Tang, Zhou XiaoNong

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Implementing national to community-based 'One Health' surveillance approach for human, animal and environmental consequences mitigation offers great opportunities and value-added in sustainable development and wellbeing. 'One Health' surveillance approach global partnerships, policy commitment and financial investment are much needed in addressing the evolving threats and epidemics crises mitigation in African countries. The paper provides insights onto how China-Africa health development cooperation in promoting “One Health” surveillance approach in response advocacy and mitigation. China-Africa health development initiatives provide new prospects in guiding and moving forward appropriate and evidence-based advocacy and mitigation management approaches and strategies in attaining Universal Health Coverage (UHC) and Sustainable Development Goals (SDGs). Early and continuous quality and timely surveillance data collection and coordinated information sharing practices in malaria and other diseases are demonstrated in Comoros, Zanzibar, Ghana and Cameroon. Improvements of variety of access to contextual sources and network of data sharing platforms are needed in guiding evidence-based and tailored detection and response to unusual hazardous events. Moreover, understanding threats and diseases trends, frontline or point of care response delivery is crucial to promote integrated and sustainable targeted local, national “One Health” surveillance and response approach needs implementation. Importantly, operational guidelines are vital in increasing coherent financing and national workforce capacity development mechanisms. Strengthening participatory partnerships, collaboration and monitoring strategies in achieving global health agenda effectiveness in Africa. At the same enhancing surveillance data information streams reporting and dissemination usefulness in informing policies decisions, health systems programming and financial mobilization and prioritized allocation pre, during and post threats and epidemics crises programs strengths and weaknesses. Thus, capitalizing on “One Health” surveillance and response approach advocacy and mitigation implementation is timely in consolidating Africa Union 2063 agenda and Africa renaissance capabilities and expectations.

Keywords: Africa, one health approach, surveillance, response

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Authors: Kristina Colovic, Mari Janikian, Nikolaos Takis, Fotini-Sonia Apergi

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A considerable number of Afghan asylum seekers in Greece are still waiting for answers about their future and status for personal, social and societal advancement. Most have been trapped in a stalemate of continuously postponed or temporarily progressed levels of integration into the EU/Greek process of asylum. Limited quantitative research exists investigating the psychological effects of long-term displacement among Afghans refugees in Greece. The purpose of this study is to investigate factors that are associated with and predict psychological distress symptoms among this population. Data from a sample of native Afghan nationals (N > 70) living in Greece for approximately the last ten years will be collected from May to July 2016. Criteria for participation include the following: being 18 years of age or older, and emigration from Afghanistan to Greece from 2003 onwards (i.e., long-term refugees or part of the 'old system of asylum'). Snowball sampling will be used to recruit participants, as this is considered the most effective option when attempting to study refugee populations. Participants will complete self-report questionnaires, consisting of the Afghan Symptom Checklist (ASCL), a culturally validated measure of psychological distress, the World Health Organization Quality of Life scale (WHOQOL-BREF), an adapted version of the Comprehensive Trauma Inventory-104 (CTI-104), and a modified Psychological Acculturation Scale. All instruments will be translated in Greek, through the use of forward- and back-translations by bilingual speakers of English and Greek, following WHO guidelines. A pilot study with 5 Afghan participants will take place to check for discrepancies in understanding and for further adapting the instruments as needed. Demographic data, including age, gender, year of arrival to Greece and current asylum status will be explored. Three different types of analyses (descriptive statistics, bivariate correlations, and multivariate linear regression) will be used in this study. Descriptive findings for respondent demographics, psychological distress symptoms, traumatic life events and quality of life will be reported. Zero-order correlations will assess the interrelationships among demographic, traumatic life events, psychological distress, and quality of life variables. Lastly, a multivariate linear regression model will be estimated. The findings from the study will contribute to understanding the determinants of acculturation, distress and trauma on daily functioning for Afghans in Greece. The main implications of the current study will be to advocate for capacity building and empower communities through effective program evaluation and design for mental health services for all refugee populations in Greece.

Keywords: Afghan refugees, evaluation, Greece, mental health, quality of life

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: David A. Scott, Michelle G. Scott

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The problem of juvenile crime, school suspensions and oppositional behaviors indicates a need for a wide range of intervention programs for at-risk youth. Juvenile court systems and mental health agencies are examining alternative ways to deal with at-risk youth that will allow the adolescent to live within their home community. The previous trend that treatment away from home is more effective than treatment near one's community has shifted. Research now suggests that treatment be close to home for several reasons, such as increased treatment success, parental involvement, and reduced costs. Treatment options consist of a wide range of interventions, including outpatient, inpatient, and community-based services (therapeutic group homes, foster care and in-home preservation services). The juvenile justice system, families and other mental health agencies continue to seek the most effective treatment for at-risk youth in their communities. This research examines two possible treatment modalities, a multi-systemic outpatient program and a residential program. Research examining effective, evidence- based counseling will be discussed during this presentation. The presenter recently completed a three-year research grant examining effective treatment modalities for at-risk youth participating in a multi-systemic program. The presenter has also been involved in several research activities gathering data on effective techniques used in residential programs. The data and discussion will be broken down into two parts, each discussing one of the treatment modalities mentioned above. Data on the residential programs was collected on both a sample of 740 at- risk youth over a five-year period and also a sample of 63 participants during a one-year period residing in a residential programs. The effectiveness of these residential services was measured in three ways: services are evaluated by primary referral sources; follow-up data is obtained at various intervals after program participation to measure recidivism (what percentage got back into trouble with the Department of Juvenile Justice); and a more sensitive, "Offense Seriousness Score", has been computed and analyzed prior to, during and after treatment in the residential program. Data on the multi-systemic program was gathered over the past three years on 190 participants. Research will discuss pre and post test results, recidivism rates, academic performance, parental involvement, and effective counseling treatment modalities.

Keywords: at-risk youth, group homes, therapeutic group homes, recidivism rates

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Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Ayse Arslan

Abstract:

One of the important elements underpinning a conscious design is the degree of toxicity in communication. This study explores the mechanisms and strategies for identifying toxic content by avoiding dark patterns. Given the breadth of hate and harassment attacks, this study explores a threat model and taxonomy to assist in reasoning about strategies for detection, prevention, mitigation, and recovery. In addition to identifying some relevant techniques such as nudges, automatic detection, or human-ranking, the study suggests the use of major metrics such as the overhead and friction of solutions on platforms and users or balancing false positives (e.g., incorrectly penalizing legitimate users) against false negatives (e.g., users exposed to hate and harassment) to maintain a conscious design towards fairness.

Keywords: AI, ML, algorithms, policy, system design

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Authors: Seyed Alireza Mirmohammadi

Abstract:

Tehran Metro vendors are one of the working minorities in the capital, which is an essential cross-cultural case study. Today, with difficult economic conditions, subway vendors are increasing. Tehran metro vendors are in daily contact with many people in different metro stations. Due to the ban on their activities in this place and sometimes the humiliating look of some people, they experience special conditions compared to other people in the community. One of the most critical sources of shaping people's mentality toward their social status and identity in the media and, in the meantime, virtual social networks, due to various communication facilities such as Dualism and the possibility of high activity of users have a special place. Statistics have shown that virtual social networks have become an indispensable source of communication, information, and entertainment today. In this study, 15 semi-structured interviews were conducted with 15 metro vendors in Tehran about their membership in various virtual social networks and their mental perception of using them. The research results indicate that the obtained mentality of metro peddlers towards themselves is negative in virtual social networks, and they do not receive a good image of themselves in these networks.

Keywords: metro, tehran, intercultural communication, metro vendors, self image

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Authors: Muhamad Azuddin Hassan, Siti Shafura Karim, Ambri Mohamed, Iskandar Yahya

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Human serum albumin plays a significant part in the physiological functions of the human body system (HSA).HSA level monitoring is critical for early detection of HSA-related illnesses. The goal of this study is to show that a field effect transistor (FET)-based immunosensor can assess HSA using high aspect ratio carbon nanotubes network (CNT) as a transducer. The CNT network were deposited using air brush technique, and the FET device was made using a shadow mask process. Field emission scanning electron microscopy and a current-voltage measurement system were used to examine the morphology and electrical properties of the CNT network, respectively. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy were used to confirm the surface alteration of the CNT. The detection process is based on covalent binding interactions between an antibody and an HSA target, which resulted in a change in the manufactured biosensor's drain current (Id).In a linear range between 1 ng/ml and 10zg/ml, the biosensor has a high sensitivity of 0.826 mA (g/ml)-1 and a LOD value of 1.9zg/ml.HSA was also identified in a genuine serum despite interference from other biomolecules, demonstrating the CNT-FET immunosensor's ability to quantify HSA in a complex biological environment.

Keywords: carbon nanotubes network, biosensor, human serum albumin

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Authors: Paolo Pilloni, Fabrizio Mulas, Salvatore Carta

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Nowadays, several research studies point up that an active lifestyle is essential for physical and mental health benefits. Mobile phones have greatly influenced people’s habits and attitudes also in the way they exercise. Our research work is mainly focused on investigating how to exploit mobile technologies to favour people’s exertion experience. To this end, we developed an exertion framework users can exploit through a real world mobile application, called BLINDED, designed to act as a virtual personal trainer to support runners during their trainings. In this work, inspired by both previous findings in the field of interaction design for people with visual impairments, feedback gathered from real users of our framework, and positive results obtained from two experimentations, we present some new interaction facilities we designed to enhance the interaction experience during a training. The positive obtained results helped us to derive some interaction design recommendations we believe will be a valid support for designers of future mobile systems conceived to be used in circumstances where there are limited possibilities of interaction.

Keywords: human computer interaction, interaction design guidelines, persuasive mobile technologies for sport and health

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Authors: Mustafa M. Donma, Orkide Donma

Abstract:

The impact of metabolic syndrome (MetS) on cognition and functions of the brain is being investigated. Iron deficiency and deficiencies of B9 (folate) as well as B12 (cobalamin) vitamins are best-known nutritional anemias. They are associated with cognitive disorders and learning difficulties. The antidepressant effects of vitamin D are known and the deficiency state affects mental functions negatively. The aim of this study is to investigate possible correlations of MetS with serum brain-derived neurotrophic factor (BDNF), iron, folate, cobalamin and vitamin D in pediatric patients. 30 children, whose age- and sex-dependent body mass index (BMI) percentiles vary between 85 and 15, 60 morbid obese children with above 99^th percentiles constituted the study population. Anthropometric measurements were taken. BMI values were calculated. Age- and sex-dependent BMI percentile values were obtained using the appropriate tables prepared by the World Health Organization (WHO). Obesity classification was performed according to WHO criteria. Those with MetS were evaluated according to MetS criteria. Serum BDNF was determined by enzyme-linked immunosorbent assay. Serum folate was analyzed by an immunoassay analyzer. Serum cobalamin concentrations were measured using electrochemiluminescence immunoassay. Vitamin D status was determined by the measurement of 25-hydroxycholecalciferol [25-hydroxy vitamin D3, 25(OH)D] using high performance liquid chromatography. Statistical evaluations were performed using SPSS for Windows, version 16. The p values less than 0.05 were accepted as statistically significant. Although statistically insignificant, lower folate and cobalamin values were found in MO children compared to those observed for children with normal BMI. For iron and BDNF values, no alterations were detected among the groups. Significantly decreased vitamin D concentrations were noted in MO children with MetS in comparison with those in children with normal BMI (p ≤ 0.05). The positive correlation observed between iron and BDNF in normal-BMI group was not found in two MO groups. In THE MetS group, the partial correlation among iron, BDNF, folate, cobalamin, vitamin D controlling for waist circumference and BMI was r = -0.501; p ≤ 0.05. None was calculated in MO and normal BMI groups. In conclusion, vitamin D should also be considered during the assessment of pediatric MetS. Waist circumference and BMI should collectively be evaluated during the evaluation of MetS in children. Within this context, BDNF appears to be a key biochemical parameter during the examination of obesity degree in terms of mental functions, cognition and learning capacity. The association observed between iron and BDNF in children with normal BMI was not detected in MO groups possibly due to development of inflammation and other obesity-related pathologies. It was suggested that this finding may contribute to mental function impairments commonly observed among obese children.

Keywords: brain-derived neurotrophic factor, iron, vitamin B9, vitamin B12, vitamin D

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