Search results for: diagnostic accuracy

Authors: I. Touaїbia, E. Azzag, O. Narjes

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Power supply networks are growing continuously and their reliability is getting more important than ever. The complexity of the whole network comprises numerous components that can fail and interrupt the power supply for the end user. Underground distribution systems are normally exposed to permanent faults, due to specific construction characteristics. In these systems, visual inspection cannot be performed. In order to enhance service restoration, accurate fault location techniques must be applied. This paper describes the different faults that affect the underground distribution system of SONELGAZ (National Society of Electricity and Gas of Algeria), and cable fault location procedure with impulse reflection method (TDR), based in the analyses of the cable response of the electromagnetic impulse, allows cable fault prelocation. The results are obtained from real test in the underground distribution feeder from electrical network of energy distribution company of Souk-Ahras, in order to know the influence of cable characteristics in the types and frequency of faults.

Keywords: distribution networks, fault location, TDR, underground cable

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Authors: Bo Wang

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The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: M. Dinesh, V. Kenny Mark, Dharni Vasudhevan Venkatesan, B. Santhosh Kumar, R. Sree Radesh, V. R. Sanal Kumar

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Comprehensive numerical studies have been carried out to examine the best aerodynamic performance of subsonic aircraft at different winglet cant angles using a validated 3D k-ω SST model. In the parametric analytical studies, NACA series of airfoils are selected. Basic design of the winglet is selected from the literature and flow features of the entire wing including the winglet tip effects have been examined with different cant angles varying from 150 to 600 at different angles of attack up to 140. We have observed, among the cases considered in this study that a case with 150 cant angle the aerodynamics performance of the subsonic aircraft during takeoff was found better up to an angle of attack of 2.80 and further its performance got diminished at higher angles of attack. Analyses further revealed that increasing the winglet cant angle from 150 to 600 at higher angles of attack could negate the performance deterioration and additionally it could enhance the peak CL/CD on the order of 3.5%. The investigated concept of variable-cant-angle winglets appears to be a promising alternative for improving the aerodynamic efficiency of aircraft.

Keywords: aerodynamic efficiency, cant angle, drag reduction, flexible winglets

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Authors: Xavier Bertrand, Alexandre Cayrel

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The early phase of an aircraft development is instrumental as it really drives the potential of a new concept. Any weakness in the high-level design (wing planform, moveable surfaces layout etc.) will be extremely difficult and expensive to recover later in the aircraft development process. Aerodynamic evaluation in this very early development phase is driven by two main criteria: a short lead-time to allow quick iterations of the geometrical design, and a high quality of the calculations to get an accurate & reliable assessment of the current status. These two criteria are usually quite contradictory. Actually, short lead time of a couple of hours from end-to-end can be obtained with very simple tools (semi-empirical methods for instance) although their accuracy is limited, whereas higher quality calculations require heavier/more complex tools, which obviously need more complex inputs as well, and a significantly longer lead time. At this point, the choice has to be done between accuracy and lead-time. A brand new approach has been developed within Airbus, aiming at obtaining quickly high quality evaluations of the aerodynamic of an aircraft. This methodology is based on a joint use of Surrogate Modelling and a lifting line code. The Surrogate Modelling is used to get the wing sections characteristics (e.g. lift coefficient vs. angle of attack), whatever the airfoil geometry, the status of the moveable surfaces (aileron/spoilers) or the high-lift devices deployment. From these characteristics, the lifting line code is used to get the 3D effects on the wing whatever the flow conditions (low/high Mach numbers etc.). This methodology has been applied successfully to a concept of medium range aircraft.

Keywords: aerodynamics, lifting line, surrogate model, CFD

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Authors: Patricia Huedo, Maria José Ruá, Raquel Agost-Felip

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Ensuring sustainable development of urban environments means guaranteeing adequate environmental conditions, being resilient and meeting conditions of safety and inclusion for all people, regardless of their condition. All existing buildings should meet basic safety conditions and be equipped with safe and accessible routes, along with visual, acoustic and tactile signals to protect their users or potential visitors, and regardless of whether they undergo rehabilitation or change of use processes. Moreover, from a social perspective, we consider the need to prioritize buildings occupied by the most vulnerable groups of people that currently do not have specific regulations tailored to their needs. Some residential models in operation are not only outside the scope of application of the regulations in force; they also lack a project or technical data that would allow knowing the fire behavior of the construction materials. However, the difficulty and cost involved in adapting the entire building stock to current regulations can never justify the lack of safety for people. Hence, this work develops a simplified model to assess compliance with the basic safety conditions in case of fire and its compatibility with the specific accessibility needs of each user. The purpose is to support the designer in decision making, as well as to contribute to the development of a basic fire safety certification tool to be applied in inclusive residential models. This work has developed a methodology to support designers in adapting Social Services Centers, usually intended to vulnerable people. It incorporates a checklist of 9 items and information from sources or standards that designers can use to justify compliance or propose solutions. For each item, the verification system is justified, and possible sources of consultation are provided, considering the possibility of lacking technical documentation of construction systems or building materials. The procedure is based on diagnosing the degree of compliance with fire conditions of residential models used by vulnerable groups, considering the special accessibility conditions required by each user group. Through visual inspection and site surveying, the verification model can serve as a support tool, significantly streamlining the diagnostic phase and reducing the number of tests to be requested by over 75%. This speeds up and simplifies the diagnostic phase. To illustrate the methodology, two different buildings in the Valencian Region (Spain) have been selected. One case study is a mental health facility for residential purposes, located in a rural area, on the outskirts of a small town; the other one, is a day care facility for individuals with intellectual disabilities, located in a medium-sized city. The comparison between the case studies allow to validate the model in distinct conditions. Verifying compliance with a basic security level can allow a quality seal and a public register of buildings adapted to fire regulations to be established, similarly to what is being done with other types of attributes such as energy performance.

Keywords: fire safety, inclusive housing, universal accessibility, vulnerable people

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Authors: Cher Rong Matthew Kong, Edmund Tian, Seng Poon Ong, Chee Sian Gan

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Gamma-aminobutyric acid (GABA) is a non-protein amino acid that is a functional constituent of certain rice varieties. When consumed, it decreases blood pressure and reduces the risk of hypertension-related diseases. This has led to more research dedicated towards the development of functional food products (e.g. germinated brown rice) with enhanced GABA content, and the development of these functional food products has led to increased demand for instrument-based methods that can efficiently and effectively determine GABA content. Current analytical methods require analyte derivatisation, and have significant disadvantages such as being labour intensive and time-consuming, and being subject to analyte loss due to the increased complexity of the sample preparation process. To address this, an LC-MS-MS method for the determination of GABA in rice products has been developed and validated. This developed method involves a relatively simple sample preparation process before analysis using HILIC LC-MS-MS. This method eliminates the need for derivatisation, thereby significantly reducing the labour and time associated with such an analysis. Using LC-MS-MS also allows for better differentiation of GABA from any potential co-eluting compounds in the sample matrix. Results obtained from the developed method demonstrated high linearity, accuracy, and precision for the determination of GABA (1ng/L to 8ng/L) in a variety of brown rice products. The method can significantly simplify sample preparation steps, improve the accuracy of quantitation, and increase the throughput of analyses, thereby providing a quick but effective alternative to established instrumental analysis methods for GABA in rice.

Keywords: functional food, gamma-aminobutyric acid, germinated brown rice, method development

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Authors: Berengere Vire, Nicolas Salvetat, Yoann Lannay, Guillaume Marcellin, Siem Van Der Laan, Franck Molina, Dinah Weissmann

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Predicting suicidal behaviors is one of the most complex challenges of daily psychiatric practices. Today, suicide risk prediction using biological tools is not validated and is only based on subjective clinical reports of the at-risk individual. Therefore, there is a great need to identify biomarkers that would allow early identification of individuals at risk of suicide. Alterations of adenosine-to-inosine (A-to-I) RNA editing of neurotransmitter receptors and other proteins have been shown to be involved in etiology of different psychiatric disorders and linked to suicidal behavior. RNA editing is a co- or post-transcriptional process leading to a site-specific alteration in RNA sequences. It plays an important role in the epi transcriptomic regulation of RNA metabolism. On postmortem human brain tissue (prefrontal cortex) of depressed suicide victims, Alcediag found specific alterations of RNA editing activity on the mRNA coding for the serotonin 2C receptor (5-HT2cR). Additionally, an increase in expression levels of ADARs, the RNA editing enzymes, and modifications of RNA editing profiles of prime targets, such as phosphodiesterase 8A (PDE8A) mRNA, have also been observed. Interestingly, the PDE8A gene is located on chromosome 15q25.3, a genomic region that has recurrently been associated with the early-onset major depressive disorder (MDD). In the current study, we examined whether modifications in RNA editing profile of prime targets allow identifying disease-relevant blood biomarkers and evaluating suicide risk in patients. To address this question, we performed a clinical study to identify an RNA editing signature in blood of depressed patients with and without the history of suicide attempts. Patient’s samples were drawn in PAXgene tubes and analyzed on Alcediag’s proprietary RNA editing platform using next generation sequencing technology. In addition, gene expression analysis by quantitative PCR was performed. We generated a multivariate algorithm comprising various selected biomarkers to detect patients with a high risk to attempt suicide. We evaluated the diagnostic performance using the relative proportion of PDE8A mRNA editing at different sites and/or isoforms as well as the expression of PDE8A and the ADARs. The significance of these biomarkers for suicidality was evaluated using the area under the receiver-operating characteristic curve (AUC). The generated algorithm comprising the biomarkers was found to have strong diagnostic performances with high specificity and sensitivity. In conclusion, we developed tools to measure disease-specific biomarkers in blood samples of patients for identifying individuals at the greatest risk for future suicide attempts. This technology not only fosters patient management but is also suitable to predict the risk of drug-induced psychiatric side effects such as iatrogenic increase of suicidal ideas/behaviors.

Keywords: blood biomarker, next-generation-sequencing, RNA editing, suicide

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Authors: Qudsiya Baig

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Empathy yields an unparalleled therapeutic value within patient physician interactions. Medical research is inundated with evidence to support that a physician’s ability to empathise with patients leads to a greater willingness to report symptoms, an improvement in diagnostic accuracy and safety, and a better adherence and satisfaction with treatment plans. Furthermore, the Institute of Medicine states that empathy leads to a more patient-centred care, which is one of the six main goals of a 21st century health system. However, there is a paradox between the theoretical significance of empathy and its presence, or lack thereof, in clinical practice. Recent studies have reported that empathy declines amongst students and physicians over time. The three most impactful contributors to this decline are: (1) disagreements over the definitions of empathy making it difficult to implement it into practice (2) poor consideration or regulation of empathy leading to burnout and thus, abandonment altogether, and (3) the lack of diversity in the curriculum and the influence of medical culture, which prioritises science over patient experience, limiting some physicians from using ‘too much’ empathy in the fear of losing clinical objectivity. These issues were investigated by conducting a fully inductive thematic narrative analysis of patient narratives in the UK to evaluate the behaviours and attitudes that patients associate with empathy. The principal enquiries underpinning this study included uncovering the factors that affected experience of empathy within provider-patient interactions and to analyse their effects on patient care. This research contributes uniquely to this discourse by examining the phenomenon of empathy directly from patients’ experiences, which were systematically extracted from a repository of online patient narratives of care titled ‘CareOpinion UK’. Narrative analysis was specifically chosen as the methodology to examine narratives from a phenomenological lens to focus on the particularity and context of each story. By enquiring beyond the superficial who-whatwhere, the study of narratives prescribed meaning to illness by highlighting the everyday reality of patients who face the exigent life circumstances created by suffering, disability, and the threat of life. The following six themes were found to be the most impactful in influencing the experience of empathy: dismissive behaviours, judgmental attitudes, undermining patients’ pain or concerns, holistic care and failures and successes of communication or language. For each theme there were overarching themes relating to either a failure to understand the patient’s perspective or a success in taking a person-centred approach. An in-depth analysis revealed that a lack of empathy was greatly associated with an emotive-cognitive imbalance, which disengaged physicians with their patients’ emotions. This study hereby concludes that competent providers require a combination of knowledge, skills, and more importantly empathic attitudes to help create a context for effective care. The crucial elements of that context involve (a) identifying empathy clues within interactions to engage with patients’ situations, (b) attributing a perspective to the patient through perspective-taking and (c) adapting behaviour and communication according to patient’s individual needs. Empathy underpins that context, as does an appreciation of narrative, and the two are interrelated.

Keywords: empathy, narratives, person-centred, perspective, perspective-taking

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Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

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Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

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Authors: Lan Nguyen, Lam Cao Van

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Thuan Phuoc is a suspension bridge built in Danang city, Vietnam. Because this bridge locates near the estuary, its structure has degraded rapidly. Many cracks have currently occurred on most of the concrete piers of the curved approach spans. This paper aims to present the results of diagnostic analysis of causes for cracks as well as some calculations for strengthening piers by carbon fiber reinforced polymer (CFRP). Besides, it describes how to use concrete nonlinear analysis software ATENA to diagnostically analyze cracks, strengthening designs. Basing on the results of studying the map of distributing crack on Thuan Phuoc bridge’s concrete piers is analyzed by the software ATENA is suitable for the real conditions and CFRP would be the best solution to strengthen piers in a sound and fast way.

Keywords: ATENA, bridge pier strengthening, carbon fiber reinforced polymer (CFRP), crack prediction analysis

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Authors: Nitin Dwivedi, Jigna Shah

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Brain tumor is metastatic neoplasm of central nervous system, in most of cases it is life threatening disease with low survival rate. Despite of enormous efforts in the development of therapeutics and diagnostic tools, the treatment of brain tumors and gliomas remain a considerable challenge in the area of neuro-oncology. The most reason behind of this the presence of physiological barriers including blood brain barrier and blood brain tumor barrier, lead to insufficient reach ability of therapeutic agents at the site of tumor, result of inadequate destruction of gliomas. So there is an indeed need empowerment of brain tumor imaging for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional different generations of dendrimer offer an improved effort for potentiate drug delivery at the site of brain tumor and gliomas. So this article emphasizes the innovative dendrimer approaches in tumor targeting, tumor imaging and delivery of therapeutic agent.

Keywords: blood brain barrier, dendrimer, gliomas, nanotechnology

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Authors: Chamseddine Barki, Salam Labidi, Hanen Boussi Rahmouni

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In this article, we present the principle and suitable methodology for the design of a medical ontology that highlights the radiological and dosimetric knowledge, applied in diagnostic radiology and radiation-therapy. Our ontology, which we named «Onto.Rap», is the subject of radiation protection in medical and radiology centers by providing a standardized regulatory oversight. Thanks to its added values of knowledge-sharing, reuse and the ease of maintenance, this ontology tends to solve many problems. Of which we name the confusion between radiological procedures a practitioner might face while performing a patient radiological exam. Adding to it, the difficulties they might have in interpreting applicable patient radioprotection standards. Here, the ontology, thanks to its concepts simplification and expressiveness capabilities, can ensure an efficient classification of radiological procedures. It also provides an explicit representation of the relations between the different components of the studied concept. In fact, an ontology based-radioprotection expert system, when used in radiological center, could implement systematic radioprotection best practices during patient exam and a regulatory compliance service auditing afterwards.

Keywords: knowledge, ontology, radiation protection, radiology

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Authors: Bo Ram Choi, Ji Su Kim, Juyeon Cho, Hyukjin Lee

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Food contaminated by bacteria (E.coli), causes food poisoning, which occurs to many patients worldwide annually. We have introduced an application of rolling circle amplification (RCA) as a versatile biosensor and developed a diagnostic platform composed of capillary tube and microbeads for rapid and easy detection of Escherichia coli (E. coli). When specific mRNA of E.coli is extracted from cell lysis, rolling circle amplification (RCA) of DNA template can be achieved and can be visualized by beads aggregation in capillary tube. In contrast, if there is no bacterial pathogen in sample, no beads aggregation can be seen. This assay is possible to detect visually target gene without specific equipment. It is likely to the development of a genetic kit for point of care testing (POCT) that can detect target gene using microbeads.

Keywords: rolling circle amplification (RCA), Escherichia coli (E. coli), point of care testing (POCT), beads aggregation, capillary tube

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Authors: Longxiang Guo, Haoyan Hao, Xueli Sheng, Hanjun Yu, Jingwei Yin

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In order to prevent target splitting and ensure the accuracy of fusion, system error registration is an important step in multi-static sonar fusion detection system. To eliminate the inherent system errors including distance error and angle error of each sonar in detection, this paper uses offline estimation method for error registration. Suppose several sonars from different platforms work together to detect a target. The target position detected by each sonar is based on each sonar’s own reference coordinate system. Based on the two-dimensional stereo projection method, this paper uses real-time quality control (RTQC) method and least squares (LS) method to estimate sensor biases. The RTQC method takes the average value of each sonar’s data as the observation value and the LS method makes the least square processing of each sonar’s data to get the observation value. In the underwater acoustic environment, matlab simulation is carried out and the simulation results show that both algorithms can estimate the distance and angle error of sonar system. The performance of the two algorithms is also compared through the root mean square error and the influence of measurement noise on registration accuracy is explored by simulation. The system error convergence of RTQC method is rapid, but the distribution of targets has a serious impact on its performance. LS method can not be affected by target distribution, but the increase of random noise will slow down the convergence rate. LS method is an improvement of RTQC method, which is widely used in two-dimensional registration. The improved method can be used for underwater multi-target detection registration.

Keywords: data fusion, multi-static sonar detection, offline estimation, sensor registration problem

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Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

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Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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Authors: Nemes Alexandra

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The aim of the research is to develop an optimum microstructure of steel coatings on aluminum surfaces for application on the crankcase cylinder bores. For the proper design of the microstructure of the coat, it is important to control the plasma gun unit properly. The maximum operating time was determined while the plasma gun could optimally work before its destruction. Objectives: The aim of the research is to determine the optimal operating time of the plasma gun between renovations (the renovation shall involve the replacement of the test components of the plasma gun: electrode, nozzle plate, powder injector. Methodology: Plasma jet and particle flux analysis with PFI (PFI is a diagnostic tool for all kinds of thermal spraying processes), CT reconstruction and analysis on the new and the used plasma guns, failure analysis of electrodes, nozzle plates, and powder injectors, microscopic examination of the microstructure of the coating. Contributions: As the result of the failure analysis detailed above, the use of the plasma gun was maximized at 100 operating hours in order to get optimal microstructure for the coat.

Keywords: APS, air plasma spray, failure analysis, electrode, nozzle plate, powder injector

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Authors: Awais Ahmed, Atif Rana, Haseeb Zia, Maham Jahangir, Rashed Nazir, Faisal Dar

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Purpose: Biliary complications represent the most common cause of morbidity in living related liver donor transplantation and detailed preoperative evaluation of biliary anatomic variants is crucial for safe patient selection and improved surgical outcomes. Purpose of this study is to determine the accuracy of preoperative MRCP in predicting biliary variations when compared to intraoperative cholangiography in living related liver donors. Materials and Methods: From 44 potential donors, 40 consecutive living related liver donors (13 females and 28 males) underwent donor hepatectomy at our centre from April 2012 to August 2013. MRCP and IOC of all patients were retrospectively reviewed separately by two radiologists and a transplant surgeon.MRCP was performed on 1.5 Tesla MR magnets using breath-hold heavily T2 weighted radial slab technique. One patient was excluded due to suboptimal MRCP. The accuracy of MRCP for variant biliary anatomy was calculated. Results: MRCP accurately predicted the biliary anatomy in 38 of 39 cases (97 %). Standard biliary anatomy was predicted by MRCP in 25 (64 %) donors (100% sensitivity). Variant biliary anatomy was noted in 14 (36 %) IOCs of which MRCP predicted precise anatomy of 13 variants (93 % sensitivity). The two most common variations were drainage of the RPSD into the LHD (50%) and the triple confluence of the RASD, RPSD and LHD (21%). Conclusion: MRCP is a sensitive imaging tool for precise pre-operative mapping of biliary variations which is critical to surgical decision making in living related liver transplantation.

Keywords: intraoperative cholangiogram, liver transplantation, living related donors, magnetic resonance cholangio-pancreaticogram (MRCP)

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Authors: Michelle Nighswander

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Suicide can be a tragic response to mental illness. It is difficult for people to disclose or discuss suicidal impulses. The stigma surrounding mental health can create a reluctance to seek help for mental illness. Patients may feel pressure to exhibit a socially desirable demeanor rather than reveal these issues, especially if they sense their healthcare provider is pressed for time or does not have an extensive history with their provider. Overcoming these barriers can be challenging. Although there are several validated depression and suicide risk instruments, varying processes used to administer these tools may impact the truthfulness of the responses. A literature review was conducted to find evidence of the impact of the environment on the accuracy of depression screening. Many investigations do not describe the environment and fewer studies use a comparison design. However, three studies demonstrated that computerized self-reporting might be more likely to elicit truthful and accurate responses due to increased privacy when responding compared to a face-to-face interview. These studies showed patients reported positive reactions to computerized screening for other stigmatizing health conditions such as alcohol use during pregnancy. Computerized self-screening for depression offers the possibility of more privacy and patient reflection, which could then send a targeted message of risk to the healthcare provider. This could potentially increase the accuracy while also increasing time efficiency for the clinic. Considering the persistent effects of mental health stigma, how these screening questions are posed can impact patients’ responses. This literature review analyzes trends in depression screening methodologies, the impact of setting on the results and how this may assist in overcoming one barrier caused by stigma.

Keywords: computerized self-report, depression, mental health stigma, suicide risk

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Authors: Maryam Panahiazar, Andrew M. Bishara, Yorick Chern, Roohallah Alizadehsani, Dexter Hadleye, Ramin E. Beygui

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Early detection plays a crucial role in enhancing the outcome for a patient with coronary artery disease (CAD). We utilized a big data analytics platform on ~23,000 patients with CAD from a total of 960,129 UCSF patients in 8 years. We traced the patients from their first encounter with a physician to diagnose and treat CAD. Characteristics such as demographic information, comorbidities, vital, lab tests, medications, and procedures are included. There are statistically significant gender-based differences in patients younger than 60 years old from the time of the first physician encounter to coronary artery bypass grafting (CABG) with a p-value=0.03. There are no significant differences between the patients between 60 and 80 years old (p-value=0.8) and older than 80 (p-value=0.4) with a 95% confidence interval. This recognition would affect significant changes in the guideline for referral of the patients for diagnostic tests expeditiously to improve the outcome by avoiding the delay in treatment.

Keywords: electronic medical records, coronary artery disease, data analytics, young women

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Authors: Colin S. Chen, Chien-Jung Tien, Hsin-Jan Huang

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For weathering studies, the change of chemical constituents by biodegradation effect in diesel-contaminated soils are important factors to be considered, especially when there is a prolonged period of weathering processes. The objective was to evaluate biodegradation effects onto hydrocarbon fingerprinting and distribution patterns of diesel fuels, fuel source screening and differentiation, source-specific marker compounds, and diagnostic ratios of diesel fuel constituents by laboratory and field studies. Biodegradation processes of diesel contaminated soils were evaluated by experiments lasting for 15 and 12 months, respectively. The degradation of diesel fuel in top soils was affected by organic carbon content and biomass of microorganisms in soils. Higher depletion of total petroleum hydrocarbon (TPH), n-alkanes, and polynuclear aromatic hydrocarbons (PAHs) and their alkyl homologues was observed in soils containing higher organic carbon content and biomass. Decreased ratio of selected isoprenoids (i.e., pristane (Pr) and phytane (Ph)) including n-C17/pristane and n-C18/phytane was observed. The ratio of pristane/phytane was remained consistent for a longer period of time. At the end of the experimental period, a decrease of pristane/phytane was observed. Biomarker compounds of bicyclic sesquiterpanes (BS) were less susceptible to the effects of biodegradation. The ratios of characteristic factors such as C15 sesquiterpane/ 8β(H)-drimane (BS3/BS5), C15 sesquiterpane/ 8β(H)-drimane (BS4/BS5), 8β(H)-drimane/8β(H)-homodrimane (BS5/BS10), and C15 sesquiterpane/8β(H)-homodrimane (BS3/BS10) could be adopted for source identification of diesel fuels in top soil. However, for biodegradation processes lasted for six months but shorter than nine months, only BS3/BS5 and BS3/BS10 could be distinguished in two diesel fuels. In subsoil experiments (contaminated soil located 50 cm below), the ratios of characteristic factors including BS3/BS5, BS4/BS5, and BS5/BS10 were valid for source identification of two diesel fuels for nine month biodegradation. At the early stage of contamination, biomass of soil decreased significantly. However, 6 and 7 dominant species were found in soils in top soil experiments, respectively. With less oxygen and nutrients in subsoil, less biomass of microorganisms was observed in subsoils. Only 2 and 4 diesel-degrading species of microorganisms were identified in two soils, respectively. Parameters of double ratio such as fluorene/C1-fluorene: C2-phenanthrene/C3-phenanthrene (C0F/C1F:C2P/C3P) in both top and subsoil, C2-naphthalene/C2-phenanthrene: C1-phenanthrene/C3-phenanthrene (C2N/C2P:C1P/C3P), and C1-phenanthrene/C1-fluorene: C3-naphthalene/C3-phenanthrene (C1P/C1F:C3N/C3P) in subsoil could serve as forensic indicators in diesel contaminated sites. BS3/BS10:BS4/BS5 could be used in 6 to 9 months of biodegradation processes. Results of principal component analysis (PCA) indicated that source identification of diesel fuels in top soil could only be perofrmed for weathering process less than 6 months. For subsoil, identification can be conducted for weathering process less than 9 months. Ratio of isoprenoids (pristane and phytane) and PAHs might be affected by biodegradation in spilled sites. The ratios of bicyclic sesquiterpanes could serve as forensic indicators in diesel-contaminated soils. Finally, source identification was attemped for samples collected from different fuel contaminated sites by using the unique pattern of sesquiterpanes. It was anticipated that the information generated from this study would be adopted by decision makers to evaluate the liability of cleanup in diesel contaminated sites.

Keywords: biodegradation, diagnostic ratio, diesel fuel, environmental forensics

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Authors: Karthikeyan M., Paul M. J.

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This case report discusses a 54-year-old woman from Salem, Tamilnadu, who presented with a rare case of papillary thyroid carcinoma (PTC), manifesting as a hypervascular mass in the left carotid sheath. The patient had a two-and-a-half-month history of non-progressive neck swelling, with symptoms including dysphagia and a choking sensation. Clinical examination and investigations such as FNAC and CECT revealed a large vascular mass in the left neck region, initially perplexing the diagnosis. The patient underwent total thyroidectomy and excision of the left carotid sheath mass. Histopathology confirmed PTC. Postoperatively, the patient received Iodine-131 ablation and showed good recovery with no recurrence. This case highlights the diagnostic challenge and atypical presentation of PTC as a vascular neck mass, emphasizing the importance of a comprehensive approach in evaluating thyroid and neck lesions.

Keywords: lateral neck vascular mass, lateral aberrant thyroid, thyroid vascular swelling, smooth post op recovery

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Authors: Jonida Hasanaj

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Abstract: Four years after the pandemic, numerous studies discuss the long-term effects of COVID-19 on patients, with chronic fatigue syndrome being a prominent concern. Understanding the mechanisms behind this syndrome is crucial for developing prevention, treatment, and rehabilitation strategies. The appropriateness of physiotherapeutic treatment in covid 19 and post-COVID-19 patients has remained uncertain due to inconsistent diagnostic criteria, highlighting the need for further research. This paper intends to offer guidelines and specific suggestions for hospital-based physical therapists managing COVID-19 hospitalized patients at ‘’Expert Physiotherapy’ and ’American Hospital’ in Albania using a national approach in accordance with worldwide initiatives. Several studies indicate that chronic tiredness syndrome and high intracranial pressure could result from failure of the post-Covid-19 lymphatic system. Enabling the patient to intensify their physical activity and enhance their ability to move, exercise, and even resume a regular life cycle is the aim of physiotherapy treatment.

Keywords: mobility, physiotherapy, post-covid 19, rehabilitation, results

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Authors: Banafsheh Nikmehr, Mohsen Bahrami, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Mallory Pitts, Tolga B. Mesen, Tamer M. Yalcinkaya

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The number of people who receive in vitro fertilization (IVF) treatment has increased on a startling trajectory over the past two decades. Despite advances in this field, particularly the introduction of intracytoplasmic sperm injection (ICSI) and the preimplantation genetic screening (PGS), the IVF success remains low. A major factor contributing to IVF failure is embryonic aneuploidy (abnormal chromosome content), which often results in miscarriage and birth defects. Although PGS is often used as the standard diagnostic tool to identify aneuploid embryos, it is an invasive approach that could affect the embryo development, and yet inaccessible to many patients due its high costs. As such, there is a clear need for a non-invasive cost-effective approach to identify euploid embryos for single embryo transfer (SET). The reported differences between morphokinetic behaviors of aneuploid and euploid embryos has shown promise to address this need. However, current literature is inconclusive and further research is urgently needed to translate current findings into clinical diagnostics. In this ongoing study, we found significant differences between morphokinetic behaviors of euploid and aneuploid embryos that provides important insights and reaffirms the promise of such behaviors for developing non-invasive methodologies. Methodology—A total of 242 embryos (euploid: 149, aneuploid: 93) from 74 patients who underwent IVF treatment in Carolinas Fertility Clinics in Winston-Salem, NC, were analyzed. All embryos were incubated in an EmbryoScope incubator. The patients were randomly selected from January 2019 to June 2021 with most patients having both euploid and aneuploid embryos. All embryos reached the blastocyst stage and had known PGS outcomes. The ploidy assessment was done by a third-party testing laboratory on day 5-7 embryo biopsies. The morphokinetic variables of each embryo were measured by the EmbryoViewer software (Uniesense FertiliTech) on time-lapse images using 7 focal depths. We compared the time to: pronuclei fading (tPNf), division to 2,3,…,9 cells (t2, t3,…,t9), start of embryo compaction (tSC), Morula formation (tM), start of blastocyst formation (tSC), blastocyst formation (tB), and blastocyst expansion (tEB), as well as intervals between them (e.g., c23 = t3 – t2). We used a mixed regression method for our statistical analyses to account for the correlation between multiple embryos per patient. Major Findings— The average age of the patients was 35.04 yrs. The average patient age associated with euploid and aneuploid embryos was not different (P = 0.6454). We found a significant difference in c45 = t5-t4 (P = 0.0298). Our results indicated this interval on average lasts significantly longer for aneuploid embryos - c45(aneuploid) = 11.93hr vs c45(euploid) = 7.97hr. In a separate analysis limited to embryos from the same patients (patients = 47, total embryos=200, euploid=112, aneuploid=88), we obtained the same results (P = 0.0316). The statistical power for this analysis exceeded 87%. No other variable was different between the two groups. Conclusion— Our results demonstrate the importance of morphokinetic variables as potential biomarkers that could aid in non-invasively characterizing euploid and aneuploid embryos. We seek to study a larger population of embryos and incorporate the embryo quality in future studies.

Keywords: IVF, embryo, euploidy, aneuploidy, morphokinteic

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Authors: Sanele E. Nene L., Lia M. Hewitt

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Background: Artificial intelligence (AI) has a high value in healthcare, various applications have been developed for the efficiency of clinical operations, such as appointment/surgery scheduling, diagnostic image analysis, prognosis, prediction and management of specific ailments. Purpose: The purpose of this study was to explore, describe, contrast, evaluate, and develop the various leadership strategies as a conceptual framework, applied by public health Operational Managers (OMs) to embrace AI benefits, with the aim to improve the healthcare system in a public hospital. Design and Method: A qualitative, exploratory, descriptive and contextual research design was followed and a descriptive phenomenological approach. Five phases were followed to conduct this study. Phenomenological individual interviews and focus groups were used to collect data and a phenomenological thematic data analysis method was used. Findings and conclusion: Three themes surfaced as the experiences of AI by the OMs; Positive experiences related to AI, Management and leadership processes in AI facilitation, and Challenges related to AI.

Keywords: ambivalence, embracing, Artificial intelligence, public hospital

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Authors: Aby Nesan Raj, Sumil K. Raj, Sumesh Jayan

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The objective of the study is to formulate a self-regulation model that shall enhance the usage of Intelligent Transportation Systems by understanding the theory of self-efficacy. The core logic of the self-regulation model shall monitor driver's behavior based on the situations related to the various sources of Self Efficacy like enactive mastery, vicarious experience, verbal persuasion and physiological arousal in addition to the vehicle data. For this study, four different vehicle data, speed, drowsiness, diagnostic data and surround camera views are considered. This data shall be given to the self-regulation model for evaluation. The oddness, which is the output of self-regulation model, shall feed to Intelligent Transportation Systems where appropriate actions are being taken. These actions include warning to the user as well as the input to the related transportation systems. It is also observed that the usage of vehicle as a sensor reduces the wastage of resource utilization or duplication. Altogether, this approach enhances the intelligence of the transportation systems especially in safety, productivity and environmental performance.

Keywords: emergency management, intelligent transportation system, self-efficacy, traffic management

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Authors: Bahareh Golchin, Nooshin Riahi

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One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Tahmidul Islam

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Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Philipp Wenger, Michael Beltle, Stefan Tenbohlen, Uwe Riechert

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The integration of renewable energy introduces new challenges to the transmission grid, as the power generation is located far from load centers. The associated necessary long-range power transmission increases the demand for high voltage direct current (HVDC) transmission lines and DC distribution grids. HVDC gas-insulated switchgears (GIS) are considered being a key technology, due to the combination of the DC technology and the long operation experiences of AC-GIS. To ensure long-term reliability of such systems, insulation defects must be detected in an early stage. Operational experience with AC systems has proven evidence, that most failures, which can be attributed to breakdowns of the insulation system, can be detected and identified via partial discharge (PD) measurements beforehand. In AC systems the identification of defects relies on the phase resolved partial discharge pattern (PRPD). Since there is no phase information within DC systems this method cannot be transferred to DC PD diagnostic. Furthermore, the behaviour of e.g. free-moving particles differs significantly at DC: Under the influence of a constant direct electric field, charge carriers can accumulate on particles’ surfaces. As a result, a particle can lift-off, oscillate between the inner conductor and the enclosure or rapidly bounces at just one electrode, which is known as firefly motion. Depending on the motion and the relative position of the particle to the electrodes, broadband electromagnetic PD pulses are emitted, which can be recorded by ultra-high frequency (UHF) measuring methods. PDs are often accompanied by light emissions at the particle’s tip which enables optical detection. This contribution investigates PD characteristics of free moving metallic particles in a commercially available 300 kV SF6-insulated HVDC-GIS. The influences of various defect parameters on the particle motion and the PD characteristic are evaluated experimentally. Several particle geometries, such as cylinder, lamella, spiral and sphere with different length, diameter and weight are determined. The applied DC voltage is increased stepwise from inception voltage up to UDC = ± 400 kV. Different physical detection methods are used simultaneously in a time-synchronized setup. Firstly, the electromagnetic waves emitted by the particle are recorded by an UHF measuring system. Secondly, a photomultiplier tube (PMT) detects light emission with a wavelength in the range of λ = 185…870 nm. Thirdly, a high-speed camera (HSC) tracks the particle’s motion trajectory with high accuracy. Furthermore, an electrically insulated electrode is attached to the grounded enclosure and connected to a current shunt in order to detect low frequency ion currents: The shunt measuring system’s sensitivity is in the range of 10 nA at a measuring bandwidth of bw = DC…1 MHz. Currents of charge carriers, which are generated at the particle’s tip migrate through the gas gap to the electrode and can be recorded by the current shunt. All recorded PD signals are analyzed in order to identify characteristic properties of different particles. This includes e.g. repetition rates and amplitudes of successive pulses, characteristic frequency ranges and detected signal energy of single PD pulses. Concluding, an advanced understanding of underlying physical phenomena particle motion in direct electric field can be derived.

Keywords: current shunt, free moving particles, high-speed imaging, HVDC-GIS, UHF

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

Procedia PDF Downloads 93