Search results for: depression detection

Authors: Hee-Jae Suh, Mi-Na Hong, Min-Ji Kim, Yeon-Seong Jeong, Ok-Hwan Lee, Jae-Wook Shin, Hyang-Sook Chun, Chan Lee

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Brown HT is a bis-azo dye which is permitted in EU as a food colorant. So far, many studies have focused on HPLC using diode array detection (DAD) analysis for detection of this food colorant with different columns and mobile phases. Even though these methods make it possible to detect Brown HT, low recovery, reproducibility, and linearity are still the major limitations for the application in foods. The purpose of this study was to compare various methods for the analysis of Brown HT and to develop an improved analytical methods including pretreatment. Among tested analysis methods, best resolution of Brown HT was observed when the following solvent was applied as a eluent; solvent A of mobile phase was 0.575g NH4H2PO4, and 0.7g Na2HPO4 in 500mL water added with 500mL methanol. The pH was adjusted using phosphoric acid to pH 6.9 and solvent B was methanol. Major peak for Brown HT appeared at the end of separation, 13.4min after injection. This method exhibited relatively high recovery and reproducibility compared with other methods. LOD (0.284 ppm), LOQ (0.861 ppm), resolution (6.143), and selectivity (1.3) of this method were better than those of ammonium acetate solution method which was most frequently used. Precision and accuracy were verified through inter-day test and intra-day test. Various methods for sample pretreatments were developed for different foods and relatively high recovery over 80% was observed in all case. This method exhibited high resolution and reproducibility of Brown HT compared with other previously reported official methods from FSA and, EU regulation.

Keywords: analytic method, Brown HT, food colorants, pretreatment method

Procedia PDF Downloads 481

Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

Procedia PDF Downloads 423

Authors: Sayed Obeidullah Abrar

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Nanosensor is the combination of two terms nanoparticles and sensors. These are chemical or physical sensor constructed using nanoscale components, usually microscopic or submicroscopic in size. These sensors are very sensitive and can detect single virus particle or even very low concentrations of substances that could be potentially harmful. Nanosensors have a large scope of research especially in the field of medical sciences, military applications, pharmaceuticals etc.

Keywords: HIV/AIDS, nanosensors, DNA, RNA

Procedia PDF Downloads 301

Authors: João Madureira, Ricardo Lagido, Inês Sousa, Fraunhofer Portugal

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Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.

Keywords: inertial measurement unit (IMU), global positioning system (GPS), smartphone, surfing performance

Procedia PDF Downloads 404

Authors: Fazeelat Duran, Darren Bishopp, Jessica Woodhams

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Negative emotions resulting from the breach of perceived obligations by an employer is called the psychological contract violation. Employees perceiving breach and feelings of negative emotions result in adverse outcomes for both the employee and employer. This paper aims to identify the relationships between contract breach, violation, stress and wellbeing and investigate whether fairness and self-efficacy mediate the relationships. A mixed method approach was used to analyze the online-surveys and semi-structured interviews with the police officers. It was identified that the psychological contract violation predicts stress and job-related well-being. Fairness and self-efficacy were identified as significant mediators to understand the underlying mechanisms of association. Whilst, in the interviews social support was identified as a popular mediator. Practical implications for employers are discussed.

Keywords: psychological contract violation and breach, stressors, depression, anxiety

Procedia PDF Downloads 248

Authors: Yuki Matsumoto, Yuma Ishimoto

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Cognitive Behavior Therapy (CBT) with children has shown effective application to various problems such as anxiety and depression. Although there are barriers to access to mental health services including lack of professional services in communities and parental concerns about stigma, school has a significant role to address children’s health problems. Schools are regarded as a suitable arena for prevention and early intervention of mental health problems. In this line, CBT can be adaptable to school education and useful to enhance students’ social and emotional skills. However, Japanese school curriculum is rigorous so as to limit available time for implementation of CBT in schools. This paper describes Brief Cognitive Behavior Therapy (BCBT) with children in a Japanese school setting. The program has been developed in order to facilitate acceptability of CBT in schools and aimed to enhance students’ skills to manage anxiety and difficult behaviors. The present research used a single arm design in which 30 students aged 9-10 years old participated. The authors provided teachers a CBT training workshop (two hours) at two primary schools in Tokyo metropolitan area and recruited participants in the research. A homeroom teacher voluntarily delivered a 6-session BCBT program (15 minutes each) in classroom periods which is called as Kaerinokai, a meeting before leaving school. Students completed a questionnaire sheet at pre- and post-periods under the supervision of the teacher. The sheet included the Spence Child Anxiety Scale (SCAS), the Depression Self-Rating Scale for Children (DSRS), and the Strengths and Difficulties Questionnaire (SDQ). The teacher was asked for feedback after the completion. Significant positive changes were found in the total and five of six sub-scales of the SCAS and the total difficulty scale of the SDQ. However, no significant changes were seen in Physical Injury Fear sub-scale of the SCAS, in the DSRS or the Prosocial sub-scale of the SDQ. The effect sizes are mostly between small and medium. The teacher commented that the program was easy to use and found positive changes in classroom activities and personal relationships. This preliminary research showed the feasibility of the BCBT in a school setting. The results suggest that the BCBT offers effective treatment for reduction in anxiety and in difficult behaviors. There is a good prospect of the BCBT suggesting that BCBT may be easier to be delivered than CBT by Japanese teachers to promote child mental health. The study has limitations including no control group, small sample size, or a short teacher training. Future research should address these limitations.

Keywords: brief cognitive behavior therapy, cognitive behavior therapy, mental health services in schools, teacher training workshop

Procedia PDF Downloads 336

Authors: Qiang Zhang, Xiaojian Hu

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In multi-vehicle tracking, based on feature selection, the tracking system efficiently tracks vehicles in a video with minimal error in combination with color attribution, which focuses on presenting a simple and fast, yet accurate and robust solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the intersection scenario. In this study, a real-time tracking system is proposed for multi-vehicle tracking in the intersection scene. Considering the complexity and application feasibility of the algorithm, in the object detection step, the detection result provided by virtual loops were post-processed and then used as the input for the tracker. For the tracker, lightweight methods were designed to extract and select features and incorporate them into the adaptive color tracking (ACT) framework. And the approbatory online feature selection algorithms are integrated on the mature ACT system with good compatibility. The proposed feature selection methods and multi-vehicle tracking method are evaluated on KITTI datasets and show efficient vehicle tracking performance when compared to the other state-of-the-art approaches in the same category. And the system performs excellently on the video sequences recorded at the intersection. Furthermore, the presented vehicle tracking system is suitable for surveillance applications.

Keywords: real-time, multi-vehicle tracking, feature selection, color attribution

Procedia PDF Downloads 166

Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

Procedia PDF Downloads 138

Authors: Web Huei Chou, Li Yi Chun, Wenwe Yu, Han Teng Weng, H. Yuan, T. Yang

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The prevalence of dementia is estimated to rise to 78 million by 2030 and 139 million by 2050. Among those affected, Alzheimer's disease is the most common form of dementia, contributing to 60–70% of cases. Addressing this growing challenge is crucial, especially considering the impact on older individuals and their caregivers. To reduce the behavioral and psychological symptoms of dementia, people with dementia use a variety of pharmaceutical and non-pharmacological treatments, and some studies have found the use of non-pharmacological interventions. Treatment of depression, cognitive function, and social activities has potential benefits. Butler developed reminiscence therapy as a method of treating dementia. Through ‘life review,’ individuals can recall their past events, activities, and experiences, which can reduce the depression of the elderly and improve their Quality of life to help give meaning to their lives and help them live independently. The life review process uses a variety of memory triggers, such as household items, past objects, photos, and music, and can be conducted collectively or individually and structured or unstructured. However, despite the advantages of nostalgia therapy, past research has always pointed out that current research lacks rigorous experimental evaluation and cannot describe clear research results and generalizability. Therefore, this study aims to study physiological sensing experiments to find a feasible experimental and verification method to provide clearer design and design specifications for reminiscence therapy and to provide a more widespread application for healthy aging. This study is an ongoing research project, a collaboration between the School of Design at Yunlin University of Science and Technology in Taiwan and the Department of Medical Engineering at Chiba University in Japan. We use traditional rice dishes from Taiwan and Japan as nostalgic content to construct a narrative structure for the elderly in the two countries respectively for life review activities, providing an easy-to-carry nostalgic therapy game with an intuitive interactive design. This experiment is expected to be completed in 36 months. The design team constructed and designed the game after conducting literary and historical data surveys and interviews with elders to confirm the nostalgic historical data in Taiwan and Japan. The Japanese team planned the Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) experimental environments and Data calculation model, and then after conducting experiments on elderly people in two places, the research results were analyzed and discussed together. The research has completed the first 24 months of pre-study, design work, and pre-study and has entered the project acceptance stage.

Keywords: reminiscence therapy, aging health, design research, life review

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Authors: Levan Sabauri

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In this paper, we discuss the deteriorated standing of engineering companies, some of the reasons behind it and the problems facing engineering enterprises during the financial crisis. We show the part that financial analysis plays in the detection of the main factors affecting the standing of a company, classify internal problems and the reasons influencing efficiency thereof. The publication contains the analysis of municipal engineering companies in post-Soviet transitional economies. In the wake of the 2008 world financial crisis the issue became even more poignant. It should be said though that even before the problem had been no less acute for some post-Soviet states caught up in a lengthy transitional period. The paper highlights shortcomings in the management of transportation companies, with new, more appropriate methods suggested. In analyzing the financial stability of a company, three elements need to be considered: current assets, investment policy and structural management of the funding sources leveraging the stability, should be focused on. Inappropriate management of the three may create certain financial problems, with timely and accurate detection thereof being an issue in terms of improved standing of an enterprise. In this connection, the publication contains a diagram reflecting the reasons behind the deteriorated financial standing of a company, as well as a flow chart thereof. The main reasons behind low profitability are also discussed.

Keywords: efficiency, financial management, financial analysis funding structure, financial sustainability, investment policy, profitability, solvency, working capital

Procedia PDF Downloads 306

Authors: Regina Rekuviene, Vykintas Samaitis, Liudas Mažeika, Audrius Jankauskas, Virginija Jankauskaitė, Laura Gegeckienė, Abdolali Sadaghiani, Shaghayegh Saeidiharzand

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Icing brings significant damage to aviation and renewable energy installations. Air-conditioning, refrigeration, wind turbine blades, airplane and helicopter blades often suffer from icing phenomena, which cause severe energy losses and impair aerodynamic performance. The icing process is a complex phenomenon with many different causes and types. Icing mechanisms, distributions, and patterns are still relevant to research topics. The adhesion strength between ice and surfaces differs in different icing environments. This makes the task of anti-icing very challenging. The techniques for various icing environments must satisfy different demands and requirements (e.g., efficient, lightweight, low power consumption, low maintenance and manufacturing costs, reliable operation). It is noticeable that most methods are oriented toward a particular sector and adapting them to or suggesting them for other areas is quite problematic. These methods often use various technologies and have different specifications, sometimes with no clear indication of their efficiency. There are two major groups of anti-icing methods: passive and active. Active techniques have high efficiency but, at the same time, quite high energy consumption and require intervention in the structure’s design. It’s noticeable that vast majority of these methods require specific knowledge and personnel skills. The main effect of passive methods (ice-phobic, superhydrophobic surfaces) is to delay ice formation and growth or reduce the adhesion strength between the ice and the surface. These methods are time-consuming and depend on forecasting. They can be applied on small surfaces only for specific targets, and most are non-biodegradable (except for anti-freezing proteins). There is some quite promising information on ultrasonic ice mitigation methods that employ UGW (Ultrasonic Guided Wave). These methods are have the characteristics of low energy consumption, low cost, lightweight, and easy replacement and maintenance. However, fundamental knowledge of ultrasonic de-icing methodology is still limited. The objective of this work was to identify the ice formation processes and its progress by employing ultrasonic guided wave technique. Throughout this research, the universal set-up for acoustic measurement of ice formation in a real condition (temperature range from +240 C to -230 C) was developed. Ultrasonic measurements were performed by using high frequency 5 MHz transducers in a pitch-catch configuration. The selection of wave modes suitable for detection of ice formation phenomenon on copper metal surface was performed. Interaction between the selected wave modes and ice formation processes was investigated. It was found that selected wave modes are sensitive to temperature changes. It was demonstrated that proposed ultrasonic technique could be successfully used for the detection of ice layer formation on a metal surface.

Keywords: ice formation processes, ultrasonic GW, detection of ice formation, ultrasonic testing

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Authors: Najib Ahmad Marzuki, Che Su Mustaffa, Johana Johari, Nur Haffiza Rahaman

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The purpose of this paper is to examine the effects and relationship of stress and social support towards the quality of life among flood victims in Malaysia. A total of 764 respondents took part in the survey via random sampling. The depression, anxiety, and stress scales were utilized to measure stress while The Multidimensional Scale of Perceived Social Support was used to measure the quality of life. The findings of this study indicate that there were significant correlations between variables in the study. The findings show a significant negative relation between stress and quality of life, and significant positive correlations between support from family as well as support from friends with the quality of life. Stress and support from family were found to be significant predictors and influences the quality of life among flood victims.

Keywords: stress, social support, quality of life, flood victims

Procedia PDF Downloads 558

Authors: Abdelkarim M. Ertiame, D. W. Yu, D. L. Yu, J. B. Gomm

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In this paper, a new robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics and using the weighted sum-squared prediction error as the residual. The recursive orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. The several actuator and sensor faults are simulated in a nonlinear simulation of the reactor in Simulink. The scheme is used to detect and isolate the faults on-line. The simulation results show the effectiveness of the scheme even the process is subjected to disturbances and uncertainties including significant changes in the monomer feed rate, fouling factor, impurity factor, ambient temperature and measurement noise. The simulation results are presented to illustrate the effectiveness and robustness of the proposed method.

Keywords: Robust fault detection, cascade control, independent RBF model, RBF neural networks, Chylla-Haase reactor, FDI under closed-loop control

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Authors: Mubashra Khalid, Saadia Amir

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This study initiated to explore the empirical relationship between psychological effects of economic recession on the educated youth in Pakistan. The diminishing economic resources during recession can create certain psychological consequences on the physical and cognitive aspects of the individuals. It may generate symptoms like aggression, depression, anxiety, frustration, stress and physical health related problems among the young generation. The sample of the study was consisted of 300 students belonging to six public sector universities of the Punjab province of Pakistan. Two hypotheses were advanced in this study regarding the relationship between recession and its effects on educated youth. The findings of the research represent that a significant relationship exists between decrease in employment opportunities and growing rate of aggression among educated youth and a significant association was found between economic instability and its influence on the learning abilities of the students during recession.

Keywords: psychological effects, recession, educated youth

Procedia PDF Downloads 512

Authors: W. Swiderski

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In the paper introduced the diagnostic technique making possible the research of internal structures in composite materials reinforced fibres using in different applications. The main reason of damages in structures of these materials is the changing distribution of load in constructions in the lifetime. Appearing defect is largely complicated because of the appearance of disturbing of continuity of reinforced fibres, binder cracks and loss of fibres adhesiveness from binders. Defect in composite materials is usually more complicated than in metals. At present, infrared thermography is the most effective method in non-destructive testing composite. One of IR thermography methods used in non-destructive evaluation is vibrothermography. The vibrothermography is not a new non-destructive method, but the new solution in this test is use ultrasonic waves to thermal stimulation of materials. In this paper, both modelling and experimental results which illustrate the advantages and limitations of ultrasonic IR thermography in inspecting composite materials will be presented. The ThermoSon computer program for computing 3D dynamic temperature distribuions in anisotropic layered solids with subsurface defects subject to ulrasonic stimulation was used to optimise heating parameters in the detection of subsurface defects in composite materials. The program allows for the analysis of transient heat conduction and ultrasonic wave propagation phenomena in solids. The experiments at MIAT were fulfilled by means of FLIR SC 7600 IR camera. Ultrasonic stimulation was performed with the frequency from 15 kHz to 30 kHz with maximum power up to 2 kW.

Keywords: composite material, ultrasonic, infrared thermography, non-destructive testing

Procedia PDF Downloads 298

Authors: Agnieszka Fryźlewicz, Jowita Kras, Mikołaj Sadowski, Agnieszka Łapczuk-Krygier, Agnieszka Kącka-Zych Radomir Jasiński

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Nicotines are a group of compounds containing conjugated pyridine and pyrrolidine molecular segments. They are widely applied in medicine, pharmacy, and agriculture. Namely as researched treatment of Alzheimer, depression, Parkinson's, Tourette syndrome, general nervous and mental disorders. Furthermore, nicotine itself is used as a stimulant, animal repellent and was widely applied as an insecticide. In our work, we obtained nicotine analogues with possible applications in agriculture. The synthesis employed [3+2] cycloaddition (32CA) reactions, occurring between pirydyl-functionalised nitrones and conjugated nitroalkenes, that allowed us to fully regio- and stereoselectively obtain product. Moreover, cycloaddition reaction realizes rapidly in mild conditions with the full atomic economy, thus fitting into “green chemistry” trends.

Keywords: nicotine, isoxazolidine, 1-3-dipolar cycloaddition, green chemistry, biological and pharmacological activity

Procedia PDF Downloads 91

Authors: A. Casale, J. Alessi, C. N. Bianchi, G. Bozzini, M. Brunoldi, V. Cappanera, P. Corvisiero, G. Fanciulli, D. Grosso, N. Magnoli, A. Mandich, C. Melchiorre, C. Morri, P. Povero, N. Stasi, M. Taiuti, G. Viano, M. Wurtz

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This work describes a real-time algorithm for detecting, tracking and classifying single motorboats, developed using the acoustic data recorded by a hydrophone array within the framework of EU LIFE + project ARION (LIFE09NAT/IT/000190). The project aims to improve the conservation status of bottlenose dolphins through a real-time simultaneous monitoring of their population and surface ship traffic. A Passive Acoustic Monitoring (PAM) system is installed on two autonomous permanent marine buoys, located close to the boundaries of the Marine Protected Area (MPA) of Portofino (Ligurian Sea- Italy). Detecting surface ships is also a necessity in many other sensible areas, such as wind farms, oil platforms, and harbours. A PAM system could be an effective alternative to the usual monitoring systems, as radar or active sonar, for localizing unauthorized ship presence or illegal activities, with the advantage of not revealing its presence. Each ARION buoy consists of a particular type of structure, named meda elastica (elastic beacon) composed of a main pole, about 30-meter length, emerging for 7 meters, anchored to a mooring of 30 tons at 90 m depth by an anti-twist steel wire. Each buoy is equipped with a floating element and a hydrophone tetrahedron array, whose raw data are send via a Wi-Fi bridge to a ground station where real-time analysis is performed. Bottlenose dolphin detection algorithm and ship monitoring algorithm are operating in parallel and in real time. Three modules were developed and commissioned for ship monitoring. The first is the detection algorithm, based on Time Difference Of Arrival (TDOA) measurements, i.e., the evaluation of angular direction of the target respect to each buoy and the triangulation for obtaining the target position. The second is the tracking algorithm, based on a Kalman filter, i.e., the estimate of the real course and speed of the target through a predictor filter. At last, the classification algorithm is based on the DEMON method, i.e., the extraction of the acoustic signature of single vessels. The following results were obtained; the detection algorithm succeeded in evaluating the bearing angle with respect to each buoy and the position of the target, with an uncertainty of 2 degrees and a maximum range of 2.5 km. The tracking algorithm succeeded in reconstructing the real vessel courses and estimating the speed with an accuracy of 20% respect to the Automatic Identification System (AIS) signals. The classification algorithm succeeded in isolating the acoustic signature of single vessels, demonstrating its temporal stability and the consistency of both buoys results. As reference, the results were compared with the Hilbert transform of single channel signals. The algorithm for tracking multiple targets is ready to be developed, thanks to the modularity of the single ship algorithm: the classification module will enumerate and identify all targets present in the study area; for each of them, the detection module and the tracking module will be applied to monitor their course.

Keywords: acoustic-noise, bottlenose-dolphin, hydrophone, motorboat

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Authors: Mary S. Ruppert-Stroescu, Minh Pham, Bruce Benjamin

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Sleep apnea is a condition where a person stops breathing and can lead to cardiovascular disease, hypertension, and stroke. In the United States, approximately forty percent of overnight sleep apnea detection tests are cancelled. The purpose of this study was to develop a textile-based sensing system that acquires biometric signals relevant to cardiovascular health, to transmit them wirelessly to a computer, and to quantitatively assess the signals for sleep apnea detection. Patient interviews, literature review and market analysis defined a need for a device that ubiquitously integrated into the patient’s lifestyle. A multi-disciplinary research team of biomedical scientists, apparel designers, and computer engineers collaborated to design a textile-based sensing system that gathers EKG, Sp02, and respiration, then wirelessly transmits the signals to a computer in real time. The electronic components were assembled from existing hardware, the Health Kit which came pre-set with EKG and Sp02 sensors. The respiration belt was purchased separately and its electronics were built and integrated into the Health Kit mother board. Analog ECG signals were amplified and transmitted to the Arduino™ board where the signal was converted from analog into digital. By using textile electrodes, ECG lead-II was collected, and it reflected the electrical activity of the heart. Signals were collected when the subject was in sitting position and at sampling rate of 250 Hz. Because sleep apnea most often occurs in people with obese body types, prototypes were developed for a man’s size medium, XL, and XXL. To test user acceptance and comfort, wear tests were performed on 12 subjects. Results of the wear tests indicate that the knit fabric and t-shirt-like design were acceptable from both lifestyle and comfort perspectives. The airflow signal and respiration signal sensors return good signals regardless of movement intensity. Future study includes reconfiguring the hardware to a smaller size, developing the same type of garment for the female body, and further enhancing the signal quality.

Keywords: sleep apnea, sensors, electronic textiles, wearables

Procedia PDF Downloads 276

Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

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The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

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Authors: Jeong Woo Kang, MiYoung Baek, Ki-Suk Kim, Kwang-Jick Lee, ByungJae So

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Introduction: A fast, easy and sensitive detection method was developed and validated by liquid chromatography tandem mass spectrometry for the determination of marbofloxacin in pig plasma which was further applied to study the pharmacokinetics of marbofloxacin. Materials and Methods: The plasma sample (500 μL) was mixed with 1.5 ml of 0.1% formic acid in MeCN to precipitate plasma proteins. After shaking for 20 min, The mixture was centrifuged at 5,000 × g for 30 min. It was dried under a nitrogen flow at 50℃. 500 μL aliquot of the sample was injected into the LC-MS/MS system. Chromatographic analysis was carried out mobile phase gradient consisting 0.1% formic acid in D.W. (A) and 0.1% formic acid in MeCN (B) with C18 reverse phase column. Mass spectrometry was performed using the positive ion mode and the selected ion monitoring (MRM). Results and Conclusions: The method validation was performed in the sample matrix. Good linearities (R2>0.999) were observed and the quantified average recoveries of marbofloxacin were 87 - 92% at level of 10 ng g-1 -100 ng g-1. The percent of coefficient of variation (CV) for the described method was less than 10 % over the range of concentrations studied. The limits of detection (LOD) and quantification (LOQ) were 2 and 5 ng g-1, respectively. This method has also been applied successfully to pharmacokinetic analysis of marbofloxacin after intravenous (IV), intramuscular (IM) and oral administration (PO). The mean peak plasma concentration (Cmax) was 2,597 ng g-1at 0.25 h, 2,587 ng g-1at 0.44 h and 2,355 ng g-1at 1.58 h for IV, IM and PO, respectively. The area under the plasma concentration-time curve (AUC0–t) was 24.8, 29.0 and 25.2 h μg/mL for IV, IM and PO, respectively. The elimination half-life (T1/2) was 8.6, 13.1 and 9.5 for IV, IM and PO, respectively. Bioavailability (F) of the marbofloxacin in pig was 117 and 101 % for IM and PO, respectively. Based on these result, marbofloxacin does not have any obstacles as therapeutics to develop the oral formulations such as tablets and capsules.

Keywords: marbofloxacin, LC-MS/MS, pharmacokinetics, chromatographic

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Authors: Samantha Vennice G. Sarcia

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A person dies every 40 seconds throughout the world due to suicide-related behaviors. Suicidal ideation is a strong precursor to suicide completion. It is one of the major health challenges faced by the world today thus, it is highly substantial. The present study investigated the influence of personality traits and socio-demographic characteristics in predicting suicidal ideation. Using the Suicide Behaviors Questionnaire-Revised and the Big Five Inventory, the degree of suicidal ideation and the associated personality traits were identified. Out of 194 students from the allied health courses, the findings suggest that the college students are at-risk and have passive thoughts about suicide. Using multiple regression analysis, there was an identified significant relationship among the factors associated with suicidal ideation, particularly the number of persons in the household, living arrangement, attendance in church activities, agreeableness, conscientiousness, and neuroticism. Findings can help in the development of campus-based suicide prevention programs.

Keywords: depression, personality traits, suicidal ideation, suicide

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Authors: Jenny Kwon

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Following the outbreak of the COVID-19 pandemic globally, another type of pandemic, ageism, appeared on the surface. Ageism, the stereotypes, prejudice, and discrimination directed towards others or oneself based on chronological age, has adversely impacted older adults' lives during the pandemic. In the short term, older adults struggled with health issues (e.g., high rate of infection and mortality) and experienced social disconnection (e.g., loneliness and depression). Ultimately, older adults' self-perceptions of aging, self-esteem and intergenerational relationships were negatively influenced. To closely look into the impact of ageism during the pandemic on U.S. older adults' aging process, the current study has three specific purposes. First, the study introduces a theoretical foundation (i.e., stereotype embodiment theory) in the development of ageism research. Second, the study reports on examples of ageism toward U.S. older adults manifested in the context of COVID-19. Finally, collective responsibilities and future research directions are proposed to fight against ageism.

Keywords: ageism, COVID-19, older adults, pandemic, stereotype embodiment

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Authors: Alaa Ashraf Khaleel Abdallah

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Background: The global landscape has witnessed an alarming rise in armed conflicts, further devastating populations through enforced displacement, compromised infrastructure, and strained healthcare systems. In Sudan, the situation is particularly dire, with conflict exacerbating shortages in medical supplies and personnel, pushing the already fragile healthcare system into crisis, especially affecting surgical care. Initially, war impacts were significant in conflict zones like Khartoum, but since mid-April 2023, the entire country has descended into chaos. Weak monitoring and health information systems hinder accurate assessment of surgical care in conflict zones, leading to inadequate resource allocation, suboptimal care, and missed opportunities for global learning. This study investigates the impact of the Sudanese conflict on postoperative complications, exploring prevalence, types, outcomes, and psychological effects in conflict and safe areas. Methods: Conducted across 10 Sudanese states—5 in conflict zones such as Khartoum and West Darfur, and 5 in safer regions like River Nile and Kassala—this study analyzed data from 1,457 patients who underwent surgery post-April 2023. Data were collected using a pretested, mixed-mode questionnaire that incorporated elements from validated frameworks and tailored questions specific to the study's context. Hospital records and surgical logs were also used, with data analyzed via SPSS. Results: The overall prevalence of postoperative complications was 35.89%, with a higher rate in conflict zones (57.5%) compared to safe areas (26.4%). Surgical site infections predominated in conflict zones (24.7%) and higher than its prevalence in safe areas, and while fever was prevalent in safer regions even though much less compared to conflict areas, bleeding from surgical site was very frequent in conflict areas. Most patients recovered within two months at a rate higher in safe areas, but most of them required further medical or surgical management within the first month, but psychological impacts were more pronounced in conflict zones with 22.22% reported anxiety among injuries patients, and 20.6% experienced depression, 13.5% and 16.9% respectively, in those had surgeries for other medical conditions, compared to 0.22%anxiety rates and 8.1%for depression in safer regions. Risk factors included age, travel to conflict zones, access to care, delays, and comorbidities. Conclusion: Strengthening healthcare systems and ensuring accessible surgical care are critical in both conflict and safe areas. Specific attention must be given to addressing patient suffering and demographic shifts caused by armed conflict. Further research is needed to refine the predictive model for postoperative complications in conflict zones.

Keywords: postoperative complications, conflict zones, risk factors, surgical outcomes, Sudan

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Authors: Muhammad Akbar Hussain, Muhammad Mehdi Hussain, Waqar Haider

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There are tens of thousands of undetected and uncatalogued pieces of space debris in the Low Earth Orbit (LEO). They are not only difficult to be detected and tracked, their sheer number puts active satellites and humans in orbit around Earth into danger. With the entry of more governments and private companies into harnessing the Earth’s orbit for communication, research and military purposes, there is an ever-increasing need for not only the detection and cataloguing of these pieces of space debris, it is time to take measures to take them out and clean up the space around Earth. Current optical and radar-based Space Situational Awareness initiatives are useful mostly in detecting and cataloguing larger pieces of debris mainly for avoidance measures. Smaller than 10 cm pieces are in a relatively dark zone, yet these are deadly and capable of destroying satellites and human missions. A network of mobile observatories, connected to each other in real time and working in unison as a single instrument, may be able to detect small pieces of debris and achieve effective triangulation to help create a comprehensive database of their trajectories and parameters to the highest level of precision. This data may enable ground-based laser systems to help deorbit individual debris. Such a network of observatories can join current efforts in detection and removal of space debris in Earth’s orbit.

Keywords: space debris, low earth orbit, mobile observatories, triangulation, seamless operability

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Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

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Authors: Nusrat Hasanov, Khalil Aryanfar

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Introduction: Epilepsy is one of the major diseases of the human nervous system. People with epilepsy are exposed to a range of psychological disorders. Psychological factors play an important role in the exacerbation or treatment of epilepsy. The first step in the treatment of epilepsy is drug therapy. Most psychological treatments aim to improve psychological well-being and control seizures. Behavioral approaches, cognitive-behavioral therapy, mind-body interventions, and acceptance and commitment therapy are widely used for the treatment of epilepsy. Psychosocial consequences such as general negative attitudes towards epilepsy and problems with stigma, family relationships, quality of life, marriage, and personality traits (such as stress, anxiety, depression, cognitive processes, and theory of mind) are usually associated with epilepsy. These points have addressed the key role of psychological treatments in addition to conventional treatment. Conclusion: Epileptic patients are expected to have more psychological problems. This emphasizes the necessity of psychological treatment along with drug treatment.

Keywords: epilepsy, personality, cognitive therapy, neurology

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Authors: Gabriel Acha Ekobi

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Fathers play a crucial role in meeting family needs such as affection, protection, and socio-economic needs of children in the world in general and South Africa in particular. Fathers’ role in children’s lives is important in providing socialization, leadership skills, and teaching societal norms. Fathers influence is very significant for children’s well-being and development as it provides the child with moral lessons, guidance, and economic support. However, there is a paucity of information regarding the effects of fathers on children. In addition, despite legal frameworks such as the African Charter on the Rights and Welfare of the child (1999) introduced by the African Union to promote child rights nevertheless, it appears maltreatment, abuse, and poor health care continue to face children. Also, the Constitution of 1996 of the Republic of South Africa (Section 28 of the Bill of Rights) and the Children’s Act 38 of 2005 were introduced by the South African government to foster the rights of children. Nevertheless, these legal frameworks remain ineffective as children’s rights are still neglected by resident fathers. This paper explores the impact of resident fathers on children in the Golf View, Alice town of the Eastern Cape Province, South Africa. A qualitative research method and an exploratory research design were utilized, and 30 participants took part in the study. The participants comprised of single mothers or caregivers of children, resident fathers and social workers. Eighteen (18) single mothers or caregivers, 10 resident fathers, and two (2) social workers participated in the study. Data was collected using semi-structured and unstructured interviews and analysed thematically. Two main themes were identified: the role of fathers on children and the effects of resident fathers on children. The study found that the presence of fathers in the lives of children prevented psychosocial issues such as stress, depression, violence, and substance abuse. A father’s presence in a household was crucial in instilling moral values in children. This allowed them to build positive characters such as respect, kindness, humility, and compassion. Children with more involved fathers tend to have fewer impulse control problems, longer attention spans, and a higher level of sociability. The study concludes that the fathers’ role prevented anxiety, depression, and stress and led to the improvement of children’s education performance. Nevertheless, the absence of a father as a role model to act as a leader by instilling moral values hinders positive behaviours in children. This study recommended that occupational training and life skills programmes should be introduced by the government and other stakeholders to empower the fathers as this might provide the platform for them to bring up their children properly.

Keywords: children, fathering, household, resident, single parent

Procedia PDF Downloads 56

Authors: Ayat E. Ali, Christeen R. Aziz, Merna A. Helmy, Mohammed M. Malek, Sherif H. El-Gohary

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Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.

Keywords: k-nearest neighbor (knn), face detection, vitiligo, bone deformity

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Authors: Bal Chandra Yadav, Rakesh K. Sonker, Anjali Sharma, Punit Tyagi, Vinay Gupta, Monika Tomar

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In the present study, ZnO doped SnO2 thin films of various compositions were deposited on the surface of a corning substrate by dropping the two sols containing the precursors for composite (ZSO) with subsequent heat treatment. The sensor materials used for selective detection of nitrogen dioxide (NO2) were designed from the correlation between the sensor composition and gas response. The available NO2 sensors are operative at very high temperature (150-800 °C) with low sensing response (2-100) even in higher concentrations. Efforts are continuing towards the development of NO2 gas sensor aiming with an enhanced response along with a reduction in operating temperature by incorporating some catalysts or dopants. Thus in this work, a novel sensor structure based on ZSO nanocomposite has been fabricated using chemical route for the detection of NO2 gas. The structural, surface morphological and optical properties of prepared films have been studied by using X-ray diffraction (XRD), Atomic force microscopy (AFM), Transmission electron microscope (TEM) and UV-visible spectroscopy respectively. The effect of thickness variation from 230 nm to 644 nm of ZSO composite thin film has been studied and the ZSO thin film of thickness ~ 460 nm was found to exhibit the maximum gas sensing response ~ 2.1×103 towards 20 ppm NO2 gas at an operating temperature of 90 °C. The average response and recovery times of the sensor were observed to be 3.51 and 6.91 min respectively. Selectivity of the sensor was checked with the cross-exposure of vapour CO, acetone, IPA, CH4, NH3 and CO2 gases. It was found that besides the higher sensing response towards NO2 gas, the prepared ZSO thin film was also highly selective towards NO2 gas.

Keywords: ZSO nanocomposite thin film, ZnO tetrapod structure, NO2 gas sensor, sol-gel method

Procedia PDF Downloads 342

Authors: Shuo Li, Yannick Coffinier, Chann Lagadec, Fabrizio Cleri, Katsuhiko Nishiguchi, Akira Fujiwara, Soo Hyeon Kim, Nicolas Clément

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The need for single cell detection and analysis techniques has increased in the past decades because of the heterogeneity of individual living cells, which increases the complexity of the pathogenesis of malignant tumors. In the search for early cancer detection, high-precision medicine and therapy, the technologies most used today for sensitive detection of target analytes and monitoring the variation of these species are mainly including two types. One is based on the identification of molecular differences at the single-cell level, such as flow cytometry, fluorescence-activated cell sorting, next generation proteomics, lipidomic studies, another is based on capturing or detecting single tumor cells from fresh or fixed primary tumors and metastatic tissues, and rare circulating tumors cells (CTCs) from blood or bone marrow, for example, dielectrophoresis technique, microfluidic based microposts chip, electrochemical (EC) approach. Compared to other methods, EC sensors have the merits of easy operation, high sensitivity, and portability. However, despite various demonstrations of low limits of detection (LOD), including aptamer sensors, arrayed EC sensors for detecting single-cell have not been demonstrated. In this work, a new technique based on 20-nm-thick nanopillars array to support cells and keep them at ideal recognition distance for redox-labeled aptamers grafted on the surface. The key advantages of this technology are not only to suppress the false positive signal arising from the pressure exerted by all (including non-target) cells pushing on the aptamers by downward force but also to stabilize the aptamer at the ideal hairpin configuration thanks to a confinement effect. With the first implementation of this technique, a LOD of 13 cells (with5.4 μL of cell suspension) was estimated. In further, the nanosupported cell technology using redox-labeled aptasensors has been pushed forward and fully integrated into a single-cell electrochemical aptasensor array. To reach this goal, the LOD has been reduced by more than one order of magnitude by suppressing parasitic capacitive electrochemical signals by minimizing the sensor area and localizing the cells. Statistical analysis at the single-cell level is demonstrated for the recognition of cancer cells. The future of this technology is discussed, and the potential for scaling over millions of electrodes, thus pushing further integration at sub-cellular level, is highlighted. Despite several demonstrations of electrochemical devices with LOD of 1 cell/mL, the implementation of single-cell bioelectrochemical sensor arrays has remained elusive due to their challenging implementation at a large scale. Here, the introduced nanopillar array technology combined with redox-labeled aptamers targeting epithelial cell adhesion molecule (EpCAM) is perfectly suited for such implementation. Combining nanopillar arrays with microwells determined for single cell trapping directly on the sensor surface, single target cells are successfully detected and analyzed. This first implementation of a single-cell electrochemical aptasensor array based on Brownian-fluctuating redox species opens new opportunities for large-scale implementation and statistical analysis of early cancer diagnosis and cancer therapy in clinical settings.

Keywords: bioelectrochemistry, aptasensors, single-cell, nanopillars

Procedia PDF Downloads 120