Search results for: automated theorem proving

Authors: Kumar Kanudkuri, A. Sandhya

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Typically, in a MRI clinical setting, there are several protocols run, each indicated for a specific anatomy and disease condition. However, these protocols or parameters within them can change over time due to changes to the recommendations by the physician groups or updates in the software or by the availability of new technologies. Most of the time, the changes are performed by the MRI technologist to account for either time, coverage, physiological, or Specific Absorbtion Rate (SAR ) reasons. However, giving properly guidelines to MRI technologist is important so that they do not change the parameters that negatively impact the image quality. Typically a standard American College of Radiology (ACR) MRI phantom is used for Quality Control (QC) in order to guarantee that the primary objectives of MRI are met. The visual evaluation of quality depends on the operator/reviewer and might change amongst operators as well as for the same operator at various times. Therefore, overcoming these constraints is essential for a more impartial evaluation of quality. This makes quantitative estimation of image quality (IQ) metrics for MRI quality control is very important. So in order to solve this problem, we proposed that there is a need for a robust, open-source, and automated MRI image control tool. The Designed and developed an automatic analysis tool for measuring MRI image quality (IQ) metrics like Signal to Noise Ratio (SNR), Signal to Noise Ratio Uniformity (SNRU), Visual Information Fidelity (VIF), Feature Similarity (FSIM), Gray level co-occurrence matrix (GLCM), slice thickness accuracy, slice position accuracy, High contrast spatial resolution) provided good accuracy assessment. A standardized quality report has generated that incorporates metrics that impact diagnostic quality.

Keywords: ACR MRI phantom, MRI image quality metrics, SNRU, VIF, FSIM, GLCM, slice thickness accuracy, slice position accuracy

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Authors: Tukur Daiyabu Abdulkadir, Wan Ishak Wan Ismail, Muhammad Saufi Mohd Kassim

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Seed singulation and accuracy in seed spacing are the major challenges associated with the adoption of mechanical seeder for system of rice intensification. In this research the metering system of a pneumatic planter was modified and automated for increase precision to meet the demand of system of rice intensification SRI. The chain and sprocket mechanism of a conventional vacuum planter were now replaced with an electro mechanical system made up of a set of servo motors, limit switch, micro controller and a wheel divided into 10 equal angles. The circumference of the planter wheel was determined based on which seed spacing was computed and mapped to the angles of the metering wheel. A program was then written and uploaded to arduino micro controller and it automatically turns the seed plates for seeding upon covering the required distance. The servo motor was calibrated with the aid of labVIEW. The machine was then calibrated using a grease belt and varying the servo rpm through voltage variation between 37 rpm to 47 rpm until an optimum value of 40 rpm was obtained with a forward speed of 5 kilometers per hour. A pressure of 1.5 kpa was found to be optimum under which no skip or double was recorded. Precision in spacing (coefficient of variation), miss index, multiple index, doubles and skips were investigated. No skip or double was recorded both at laboratory and field levels. The operational parameters under consideration were both evaluated at laboratory and field. Even though there was little variation between the laboratory and field values of precision in spacing, multiple index and miss index, the different is not significant as both laboratory and field values fall within the acceptable range.

Keywords: automation, calibration, pneumatic seed planter, system of rice intensification

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Authors: Venuste Kayinamura, V. Iyamuremye, A. Ngirabakunzi

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Anti-retroviral therapy (ART) for HIV patient can cause a deficiency in glucose metabolism by promoting insulin resistance, glucose intolerance, and diabetes, diabetes mellitus keep increasing among HIV-infected patients worldwide but there is limited data on levels of blood glucose and its relationship with antiretroviral drugs (ARVs) and HIV-infection worldwide, particularly in Rwanda. A convenient sampling strategy was used in this study and it involved 323 HIV patients (n=323). Patients who are HIV positive under ARVs were involved in this study. The patient’s blood glucose was analyzed using an automated machine or glucometer (COBAS C 311). Data were analyzed using Microsoft Excel and SPSS V. 20.0 and presented in percentages. The highest diabetes mellitus prevalence was 93.33 % in people aged >40 years while the lowest diabetes mellitus prevalence was 6.67% in people aged between 21-and 40 years. The P-value was (0.021). Thus, there is a significant association between age and diabetes occurrence. The highest diabetes mellitus prevalence was 28.2% in patients under ART treatment for more than 10 years, 16.7% were <5years while 20% of patients were on ART treatment between 5-10 years. The P-value here is (0.03), thus the incidence of diabetes is associated with long-term ART use in HIV-infected patients. This study assessed the prevalence of diabetes among HIV-infected patients under ARVs attending the University Teaching Hospital of Butare (CHUB), it shows that the prevalence of diabetes is high in HIV-infected patients under ARTs. This study found no significant relationship between gender and diabetes mellitus growth. Therefore, regular assessment of diabetes mellitus especially among HIV-infected patients under ARVs is highly recommended to control other health issues caused by diabetes mellitus.

Keywords: anti-retroviral, diabetes mellitus, antiretroviral therapy, human immune deficiency virus

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Authors: Thelma Palaoag, Melanie Basa, Jezreel Mark Panilo

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Checking short-answer questions and essays, whether it may be paper or electronic in form, is a tiring and tedious task for teachers. Evaluating a student’s output require wide array of domains. Scoring the work is often a critical task. Several attempts in the past few years to create an automated writing assessment software but only have received negative results from teachers and students alike due to unreliability in scoring, does not provide feedback and others. The study aims to create an application that will be able to check short-answer questions which incorporate information extraction. Information extraction is a subfield of Natural Language Processing (NLP) where a chunk of text (technically known as unstructured text) is being broken down to gather necessary bits of data and/or keywords (structured text) to be further analyzed or rather be utilized by query tools. The proposed system shall be able to extract keywords or phrases from the individual’s answers to match it into a corpora of words (as defined by the instructor), which shall be the basis of evaluation of the individual’s answer. The proposed system shall also enable the teacher to provide feedback and re-evaluate the output of the student for some writing elements in which the computer cannot fully evaluate such as creativity and logic. Teachers can formulate, design, and check short answer questions efficiently by defining keywords or phrases as parameters by assigning weights for checking answers. With the proposed system, teacher’s time in checking and evaluating students output shall be lessened, thus, making the teacher more productive and easier.

Keywords: information extraction, short-answer question, natural language processing, application

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Authors: Bryce Benson, Sooin Lee, Ashwin Belle

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Unrecognized or delayed identification of patient deterioration is a key cause of in-hospitals adverse events. Clinicians rely on vital signs monitoring to recognize patient deterioration. However, due to ever increasing nursing workloads and the manual effort required, vital signs tend to be measured and recorded intermittently, and inconsistently causing large gaps during patient monitoring. Additionally, during deterioration, the body’s autonomic nervous system activates compensatory mechanisms causing the vital signs to be lagging indicators of underlying hemodynamic decline. This study analyzes the predictive efficacy of the Analytic for Hemodynamic Instability (AHI) system, an automated tool that was designed to help clinicians in early identification of deteriorating patients. The lead time analysis in this retrospective observational study assesses how far in advance AHI predicted deterioration prior to the start of an episode of hemodynamic instability (HI) becoming evident through vital signs? Results indicate that of the 362 episodes of HI in this study, 308 episodes (85%) were correctly predicted by the AHI system with a median lead time of 57 minutes and an average of 4 hours (240.5 minutes). Of the 54 episodes not predicted, AHI detected 45 of them while the episode of HI was ongoing. Of the 9 undetected, 5 were not detected by AHI due to either missing or noisy input ECG data during the episode of HI. In total, AHI was able to either predict or detect 98.9% of all episodes of HI in this study. These results suggest that AHI could provide an additional ‘pair of eyes’ on patients, continuously filling the monitoring gaps and consequently giving the patient care team the ability to be far more proactive in patient monitoring and adverse event management.

Keywords: clinical deterioration prediction, decision support system, early warning system, hemodynamic status, physiologic monitoring

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Authors: Lucas Bublitz, Michael Herdrich

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By achieving the first commercialization approval in San Francisco the Autonomous Driving (AD) industry proves the technology maturity of the SAE L4 AD systems and the corresponding software and hardware stack. This milestone reflects the upcoming phase in the industry, where the focus is now about scaling and supervising larger autonomous vehicle (AV) fleets in different operation areas. This requires an operation framework, which organizes and assigns responsibilities to the relevant AV technology and operation stakeholders from the AV system provider, the Remote Intervention Operator, the MaaS provider and regulatory & approval authority. This holistic operation framework consists of technological, processual, and organizational activities to ensure safe operation for fully automated vehicles. Regarding the supervision of large autonomous vehicle fleets, a major focus is on the continuous field monitoring. The field monitoring approach must reflect the safety and security criticality of incidents in the field during driving operation. This includes an automatic containment approach, with the overall goal to avoid safety critical incidents and reduce downtime by a malfunction of the AD software stack. An End-to-end (E2E) field monitoring approach detects critical faults in the field, uses a knowledge-based approach for evaluating the safety criticality and supports the automatic containment of these E/E faults. Applying such an approach will ensure the scalability of AV fleets, which is determined by the handling of incidents in the field and the continuous regulatory compliance of the technology after enhancing the Operational Design Domain (ODD) or the function scope by Functions on Demand (FoD) over the entire digital product lifecycle.

Keywords: field monitoring, incident management, multicompliance management for AI in AD, root cause analysis, database approach

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Authors: Rishi Agarwal

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Digital twins are emerging as a strong technology used to imitate and monitor physical objects digitally in real time across sectors. It is not only dealing with the digital space, but it is also actuating responses in the physical space in response to the digital space processing like storage, modeling, learning, simulation, and prediction. This paper explores the application of digital twins for enhancing physical security in retail stores. The retail sector still relies on outdated physical security practices like manual monitoring and metal detectors, which are insufficient for modern needs. There is a lack of real-time data and system integration, leading to ineffective emergency response and preventative measures. As retail automation increases, new digital frameworks must control safety without human intervention. To address this, the paper proposes implementing an intelligent digital twin framework. This collects diverse data streams from in-store sensors, surveillance, external sources, and customer devices and then Advanced analytics and simulations enable real-time monitoring, incident prediction, automated emergency procedures, and stakeholder coordination. Overall, the digital twin improves physical security through automation, adaptability, and comprehensive data sharing. The paper also analyzes the pros and cons of implementation of this technology through an Emerging Technology Analysis Canvas that analyzes different aspects of this technology through both narrow and wide lenses to help decision makers in their decision of implementing this technology. On a broader scale, this showcases the value of digital twins in transforming legacy systems across sectors and how data sharing can create a safer world for both retail store customers and owners.

Keywords: digital twin, retail store safety, digital twin in retail, digital twin for physical safety

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Authors: C. A. Ologunde

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Escherichia coli have been a major microorganisms associated with, and isolated from feacal samples either in adult or children all over the world. Strains of these organisms are resistant to cephalosporins and fluoroquinolone (FQ) antimicrobial agents among hospitalized patients and FQs are the most frequently prescribed antimicrobial class in hospitals, and the level of resistant of E. coli to these antimicrobial agents is a risk factor that should be assessed. Hence, this study was conducted to determine the prevalence and risk factors for colonization with fluoroquinolone (FQ)-resistant E. coli in hospitalized patients in Ado-Ekiti. Rectal swabs were obtained from patients in hospitals in the study area and FQ-resistant E. coli were isolated and identified by means of Nalidixic acid multi-disk and a 1-step screening procedure. Species identification and FQ resistance were confirmed by automated testing (Vitek, bioMerieux, USA). Individual colonies were subjected to pulse-field gel electrophoresis (PAGE) to determine macro-restriction polymorphism after digestion of chromosomal DNA. FQ-resistant E. coli was detected in the stool sample of 37(62%) hospitalized patient. With multivariable analyses, the use of FQ before hospitalization was the only independent risk factor for FQ-resistant E. coli carriage and was consistent for FQ exposures for the 3-12 months of study. Pulsed-field gel electrophoresis of FQ-resistant E. coli identified conal spread of 1(one) strain among 18 patients. Loss (9 patients) or acquisition (10 residents) of FQ-resistant E. coli was documented and was associated with de novo colonization with genetically distinct strains. It was concluded that FQ-resistant E. coli carriage was associated with clonal spread. The differential effects of individual fluoroquinolone on antimicrobial drug resistance are an important area for future study, as hospitals manipulate their formularies with regard to use of individual fluoroquinolone, often for economic reasons.

Keywords: E. coli, fluoroquinolone, risk factors, feacal carriage, hospitalized patients, Ado-Ekiti

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Authors: Jacquelyn Burkell, Jane Bailey

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Artificial intelligence (AI) solutions deployed within the justice system face the critical task of providing acceptable explanations for decisions or actions. These explanations must satisfy the joint criteria of public and professional accountability, taking into account the perspectives and requirements of multiple stakeholders, including judges, lawyers, parties, witnesses, and the general public. This research project analyzes and integrates two existing literature on explanations in order to propose guidelines for explainable AI in the justice system. Specifically, we review three bodies of literature: (i) explanations of the purpose and function of 'explainable AI'; (ii) the relevant case law, judicial commentary and legal literature focused on the form and function of reasons for judicial decisions; and (iii) the literature focused on the psychological and sociological functions of these reasons for judicial decisions from the perspective of the public. Our research suggests that while judicial ‘reasons’ (arguably accurate descriptions of the decision-making process and factors) do serve similar explanatory functions as those identified in the literature on 'explainable AI', they also serve an important ‘justification’ function (post hoc constructions that justify the decision that was reached). Further, members of the public are also looking for both justification and explanation in reasons for judicial decisions, and that the absence of either feature is likely to contribute to diminished public confidence in the legal system. Therefore, artificially automated judicial decision-making systems that simply attempt to document the process of decision-making are unlikely in many cases to be useful to and accepted within the justice system. Instead, these systems should focus on the post-hoc articulation of principles and precedents that support the decision or action, especially in cases where legal subjects’ fundamental rights and liberties are at stake.

Keywords: explainable AI, judicial reasons, public accountability, explanation, justification

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Nurhayati Br Tarigan, Rizkita Rachmi Esyanti

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Stevia (Stevia rebaudiana Bertoni) has a great potential to be used as a natural sweetener because it contains steviol glycoside, which is approximately 100 - 300 times sweeter than sucrose, yet low calories. Vegetative and generative propagation of S. rebaudiana is inefficient to produce stevia biomass and stevioside. One of alternative for stevia propagation is in vitro shoot culture. This research was conducted to optimize the best medium for shoot growth and to compare the bioconversion efficiency and stevioside production of S. rebaudiana shoot culture cultivated in thin layer culture (TLC), recipient for automated temporary immersion system (TIS RITA®) bioreactor, and bubble column bioreactor. The result showed that 1 ppm of Kinetin produced a healthy shoot and the highest number of leaves compared to BAP. Shoots were then cultivated in TLC, TIS RITA® bioreactor, and bubble column bioreactor. Growth medium efficiency was determined by yield and productivity. TLC produced the highest growth medium efficiency of S. rebaudiana, the yield was 0.471 ± 0.117 gbiomass.gsubstrate-1, and the productivity was 0.599 ± 0.122 gbiomass.Lmedium-1.day-1. While TIS RITA® bioreactor produced the lowest yield and productivity, 0.182 ± 0.024 gbiomass.gsubstrate-1 and 0.041 ± 0.0002 gbiomass.Lmedium-1.day-1 respectively. The yield of bubble column bioreactor was 0.354 ± 0.204 gbiomass.gsubstrate-1 and the productivity was 0,099 ± 0,009 gbiomass.Lmedium-1.day-1. The stevioside content from the highest to the lowest was obtained from stevia shoot which was cultivated on TLC, TIS RITA® bioreactor, and bubble column bioreactor; the content was 93,44 μg/g, 42,57 μg/g, and 23,03 μg/g respectively. All three systems could be used to produce stevia shoot biomass, but optimization on the number of nutrition and oxygen intake was required in each system.

Keywords: bubble column, growth medium efficiency, Stevia rebaudiana, stevioside, TIS RITA®, TLC

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Authors: Nadilla Saskia, Octoria Nur, Assegaf Zareeva

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Mass transport infrastructures are essential to enhance the connectivity between regions and regional equity in Indonesia. Indonesia’s capital city, Jakarta, ranked the 10th highest congestion rate in the world based on the 2019 traffic index, contributing to air pollution and energy consumption. Other than that, the World Air Quality Report in 2019 depicted Jakarta’s air pollutant concentration at 49.4 mg, the 5th highest in the world. Issues of severe traffic congestion, lack of sufficient urban infrastructure in Jakarta, and greenhouse gas emissions have to be addressed through mass transportation. Indonesia’s government is currently constructing The Greater Jakarta LRT (Light Rapid Transit) as convenient, efficient, and environmentally friendly transportation connecting Jakarta with Bekasi and Cibubur areas and plans to serve the passengers in August 2023. Greater Jakarta LRT is operated with Grade of Automation (GoA) No.3, Driverless Train Operation (DTO). Hence, the automated technology used in rail infrastructure is anticipated to address these issues with greater results. The paper will be validated and establish the extent to which the automation system would increase energy efficiency, help reduce carbon emissions, and benefit the environment. Based on the calculated CO2 emissions and fuel consumption for the existing condition (2015) during the feasibility study of the LRT Project and the predicted condition in 2030, it is obtained that Greater Jakarta LRT with GoA3 operation will reduce the CO2 emissions and fuel consumption by more than 50% in 2030. In the bigger picture, Greater Jakarta LRT supports the government's goal of achieving Jakarta Net-Zero Emissions (NZE) 2050.

Keywords: LRT, Grade of Automation (GoA), energy efficiency, carbon emissions, railway infrastructure, DKI Jakarta

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Authors: Margareta Simina Stanc, Aurel Mototolea, Tiberiu Potarniche

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Sacidava archeological site is located in Dobrudja region, Romania, on a hill on the right bank of the Danube - the Musait point, located at about 5 km north-east from Dunareni village. The place-name documents the fact that, prior to the Roman conquest, in the area, there was a Getic settlement. The location of the Sacidava was made possible by corroborating the data provided by the ancient sources with the epigraphic documents (the milial pillar during the time of Emperor Decius). The tegular findings attest that an infantry unit, cohors I Cilicum milliaria equitata, as well as detachments from Legio V Macedonica and Legio XI Claudia, were confined to Sacidava. During the period of the Dominion, the garrison of the fortification is the host of a cavalry unit: cuneus equitum scutariorum. In the immediate vicinity to the Roman fortress, to the east, were identified two other fortifications: a Getic settlement (4th-1st century B.C.) and an Early Medieval settlement (9th-10th century A.C.). The archaeological material recovered during the research is represented by ceramic forms such as amphoras, jugs, pots, cups, plates, to which are added oil lamps, some of them typologically new at the time of discovery. Local ceramic shapes were also founded, worked by hand or by wheel, considered un-Romanized or in the course of Romanization. During the time of the Principality, Sacidava it represented an important military outpost serving mainly the city of Tropaeum Traiani, controlling also the supply and transport on the Danube limes in the Moesic sector. This role will determine the development of the fortress and the appearance of extramuros civil structures, thus becoming an important landmark during the 5th-6th centuries A.C., becoming a representation of power of the Roman empire in an area of continuous conflict. During recent archaeological researches, faunal remains were recovered, and their analysis allowed to estimate the animal resources and subsistence practices (animal husbandry, hunting, fishing) in the settlement. The methodology was specific to archaeozoology, mainly consisting of anatomical, taxonomical, and taphonomical identifications, recording, and quantification of the data. The remains of domestic mammals have the highest proportion indicating the importance of animal husbandry; the predominant species are Bos taurus, Ovis aries/Capra hircus, and Sus domesticus. Fishing and hunting were of secondary importance in the subsistence economy of the community. Wild boar and the red deer were the most frequently hunted species. Just a few fish bones were recovered. Thus, the ancient city of Sacidava is proving to be an important element of cultural heritage of the south-eastern part of Romania, for whose conservation and enhancement efforts must be made, especially by landscape reconstruction.

Keywords: archaeozoology, landscape reconstruction, limes, military outpost

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Authors: O. Ubani, H. I. Atagana, M. S. Thantsha

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This study was conducted to measure the reduction in polycyclic aromatic hydrocarbons (PAHs) content in oil sludge by co-composting the sludge with pig, cow, horse and poultry manures under laboratory conditions. Four kilograms of soil spiked with 800 g of oil sludge was co-composted differently with each manure in a ratio of 2:1 (w/w) spiked soil:manure and wood-chips in a ratio of 2:1 (w/v) spiked soil:wood-chips. Control was set up similar as the one above but without manure. Mixtures were incubated for 10 months at room temperature. Compost piles were turned weekly and moisture level was maintained at between 50% and 70%. Moisture level, pH, temperature, CO2 evolution and oxygen consumption were measured monthly and the ash content at the end of experimentation. Bacteria capable of utilizing PAHs were isolated, purified and characterized by molecular techniques using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), amplification of the 16S rDNA gene using the specific primers (16S-P1 PCR and 16S-P2 PCR) and the amplicons were sequenced. Extent of reduction of PAHs was measured using automated soxhlet extractor with dichloromethane as the extraction solvent coupled with gas chromatography/mass spectrometry (GC/MS). Temperature did not exceed 27.5O°C in all compost heaps, pH ranged from 5.5 to 7.8 and CO2 evolution was highest in poultry manure at 18.78 µg/dwt/day. Microbial growth and activities were enhanced. Bacteria identified were Bacillus, Arthrobacter and Staphylococcus species. Results from PAH measurements showed reduction between 77 and 99%. The results from the control experiments may be because it was invaded by fungi. Co-composting of spiked soils with animal manures enhanced the reduction in PAHs. Interestingly, all bacteria isolated and identified in this study were present in all treatments, including the control.

Keywords: bioremediation, co-composting, oil refinery sludge, PAHs, bacteria spp, animal manures, molecular techniques

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Authors: Zatovska T. V., Nesterova N. V., Baranova G. V., Zagorodnya S. D.

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In recent years, biosensor technologies based on the phenomenon of surface plasmon resonance (SPR) are becoming increasingly used in biology and medicine. Their application facilitates exploration in real time progress of binding of biomolecules and identification of agents that specifically interact with biologically active substances immobilized on the biosensor surface (biochips). Special attention is paid to the use of Biosensor analysis in determining the antibody-antigen interaction in the diagnostics of diseases caused by viruses and bacteria. According to WHO, the diseases that are caused by the herpes simplex virus (HSV), take second place (15.8%) after influenza as a cause of death from viral infections. Current diagnostics of HSV infection include PCR and ELISA assays. The latter allows determination the degree of immune response to viral infection and respective stages of its progress. In this regard, the searches for new and available diagnostic methods are very important. This work was aimed to develop Biosensor chip for detection of specific antibodies to HSV-1 in the human blood serum. The proteins of HSV1 (strain US) were used as antigens. The viral particles were accumulated in cell culture MDBK and purified by differential centrifugation in cesium chloride density gradient. Analysis of the HSV1 proteins was performed by polyacrylamide gel electrophoresis and ELISA. The protein concentration was measured using De Novix DS-11 spectrophotometer. The device for detection of antigen-antibody interactions was an optoelectronic two-channel spectrometer ‘Plasmon-6’, using the SPR phenomenon in the Krechman optical configuration. It was developed at the Lashkarev Institute of Semiconductor Physics of NASU. The used carrier was a glass plate covered with 45 nm gold film. Screening of human blood serums was performed using the test system ‘HSV-1 IgG ELISA’ (GenWay, USA). Development of Biosensor chip included optimization of conditions of viral antigen sorption and analysis steps. For immobilization of viral proteins 0.2% solution of Dextran 17, 200 (Sigma, USA) was used. Sorption of antigen took place at 4-8°C within 18-24 hours. After washing of chip, three times with citrate buffer (pH 5,0) 1% solution of BSA was applied to block the sites not occupied by viral antigen. It was found direct dependence between the amount of immobilized HSV1 antigen and SPR response. Using obtained biochips, panels of 25 positive and 10 negative for the content of antibodies to HSV-1 human sera were analyzed. The average value of SPR response was 185 a.s. for negative sera and from 312 to. 1264 a.s. for positive sera. It was shown that SPR data were agreed with ELISA results in 96% of samples proving the great potential of SPR in such researches. It was investigated the possibility of biochip regeneration and it was shown that application of 10 mM NaOH solution leads to rupture of intermolecular bonds. This allows reuse the chip several times. Thus, in this study biosensor chip for detection of specific antibodies to HSV1 was successfully developed expanding a range of diagnostic methods for this pathogen.

Keywords: biochip, herpes virus, SPR

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Authors: Juhi Sah

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Habitat is characterized by the inter-dependency of environmental elements. Anthropocentric development approach is increasing our vulnerability towards natural hazards. Hence, manmade interventions should have a higher level of sensitivity towards the natural settings. Sensitivity towards the environment can be assessed by the behavior of the stakeholders involved. This led to the establishment of a hypothesis: there exists a legitimate relationship between the behavioral sciences, land use evolution and environment conservation, in the planning process. An attempt has been made to establish this relationship by reviewing the existing set of knowledge and case examples pertaining to the three disciplines under inquiry. Understanding the scarce & deteriorating nature of fresh-water reserves of earth and experimenting the above concept, a case study of a growing urban center's river flood plain is selected, in a developing economy, India. Cases of urban flooding in Chennai, Delhi and other mega cities of India, imposes a high risk on the unauthorized settlement, on the floodplains of the rivers. The issue addressed here is the encroachment of floodplains, through psychological enlightenment and modification through knowledge building. The reaction of an individual or society can be compared to a cognitive process. This study documents all the stakeholders' behavior and perception for their immediate natural environment (water body), and produce various land uses suitable along a river in an urban settlement as per different stakeholder's perceptions. To assess and induce morally responsible behavior in a community (small scale or large scale), tools of psychological inquiry is used for qualitative analysis. The analysis will deal with varied data sets from two sectors namely: River and its geology, Land use planning and regulation. Identification of a distinctive pattern in the built up growth, river ecology degradation, and human behavior, by handling large quantum of data from the diverse sector and comments on the availability of relevant data and its implications, has been done. Along the whole river stretch, condition and usage of its bank vary, hence stakeholder specific survey questionnaires have been prepared to accurately map the responses and habits of the rational inhabitants. A conceptual framework has been designed to move forward with the empirical analysis. The classical principle of virtues says "virtue of a human depends on its character" but another concept defines that the behavior or response is a derivative of situations and to bring about a behavioral change one needs to introduce a disruption in the situation/environment. Owing to the present trends, blindly following the results of data analytics and using it to construct policy, is not proving to be in favor of planned development and natural resource conservation. Thus behavioral assessment of the rational inhabitants of the planet is also required, as their activities and interests have a large impact on the earth's pre-set systems and its sustenance.

Keywords: behavioral assessment, flood plain encroachment, land use planning, river sustenance

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Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Piotr Kacejko, Mariusz Duk, Miroslaw Wendeker

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This paper presents an adaptive controller to track the maximum power point of a photovoltaic modules (PV) under fast irradiation change on the city-bus roof. Photovoltaic systems have been a prominent option as an additional energy source for vehicles. The Municipal Transport Company (MPK) in Lublin has installed photovoltaic panels on its buses roofs. The solar panels turn solar energy into electric energy and are used to load the buses electric equipment. This decreases the buses alternators load, leading to lower fuel consumption and bringing both economic and ecological profits. A DC–DC boost converter is selected as the power conditioning unit to coordinate the operating point of the system. In addition to the conversion efficiency of a photovoltaic panel, the maximum power point tracking (MPPT) method also plays a main role to harvest most energy out of the sun. The MPPT unit on a moving vehicle must keep tracking accuracy high in order to compensate rapid change of irradiation change due to dynamic motion of the vehicle. Maximum power point track controllers should be used to increase efficiency and power output of solar panels under changing environmental factors. There are several different control algorithms in the literature developed for maximum power point tracking. However, energy performances of MPPT algorithms are not clarified for vehicle applications that cause rapid changes of environmental factors. In this study, an adaptive MPPT algorithm is examined at real ambient conditions. PV modules are mounted on a moving city bus designed to test the solar systems on a moving vehicle. Some problems of a PV system associated with a moving vehicle are addressed. The proposed algorithm uses a scanning technique to determine the maximum power delivering capacity of the panel at a given operating condition and controls the PV panel. The aim of control algorithm was matching the impedance of the PV modules by controlling the duty cycle of the internal switch, regardless of changes of the parameters of the object of control and its outer environment. Presented algorithm was capable of reaching the aim of control. The structure of an adaptive controller was simplified on purpose. Since such a simple controller, armed only with an ability to learn, a more complex structure of an algorithm can only improve the result. The presented adaptive control system of the PV system is a general solution and can be used for other types of PV systems of both high and low power. Experimental results obtained from comparison of algorithms by a motion loop are presented and discussed. Experimental results are presented for fast change in irradiation and partial shading conditions. The results obtained clearly show that the proposed method is simple to implement with minimum tracking time and high tracking efficiency proving superior to the proposed method. This work has been financed by the Polish National Centre for Research and Development, PBS, under Grant Agreement No. PBS 2/A6/16/2013.

Keywords: adaptive control, photovoltaic energy, city bus electric load, DC-DC converter

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Authors: Dominic Huschke, Rudolf Keil

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New driving functionalities like highly automated driving have a major impact on the electrics/electronics architecture of future vehicles and inevitably lead to higher safety requirements. Partly due to these increased requirements, the vehicle industry is increasingly looking at semiconductor switches as an alternative to conventional melting fuses. The protective functionality of semiconductor switches can be implemented in hardware as well as in software. A current approach discussed in science and industry is the implementation of a model of the protected low voltage power cable on a microcontroller to calculate its temperature. Here, the information regarding the current is provided by the continuous current measurement of the semiconductor switch. The signal to open the semiconductor switch is provided by the microcontroller when a previously defined limit for the temperature of the low voltage power cable is exceeded. A setup for the testing of the described principle for electronic fuse protection of a low voltage power cable is built and successfullyvalidated with experiments afterwards. Here, the evaluation criterion is the deviation of the measured temperature of the low voltage power cable from the specified limit temperature when the semiconductor switch is opened. The analysis is carried out with an assumed ambient temperature as well as with a measured ambient temperature. Subsequently, the experimentally performed investigations are simulated in a virtual environment. The explicit focus is on the simulation of the behavior of the microcontroller with an implemented model of a low voltage power cable in a real-time environment. Subsequently, the generated results are compared with those of the experiments. Based on this, the completely virtual design of the described approach is assumed to be valid.

Keywords: automotive wire harness, electronic fuse protection, low voltage power cable, semiconductor-based fuses, software-based validation

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Authors: Nigina Toktasynova, Sholpan Sagyndykova, Zhanat Kenzhebayeva, Maksat Kalimoldayev, Mariya Ishimova, Irbulat Utepbergenov

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Any business needs a stable operation and continuous improvement, therefore it is necessary to constantly interact with the environment, to analyze the work of the enterprise in terms of employees, executives and consumers, as well as to correct any inconsistencies of certain types of processes and their aggregate. In the case of heat supply organizations, in addition to suppliers, local legislation must be considered which often is the main regulator of pricing of services. In this case, the process approach used to build a functional organizational structure in these types of businesses in Kazakhstan is a challenge not only in the implementation, but also in ways of analyzing the employee's salary. To solve these problems, we investigated the management system of heating enterprise, including strategic planning based on the balanced scorecard (BSC), quality management in accordance with the standards of the Quality Management System (QMS) ISO 9001 and analysis of the system based on expert judgment using fuzzy inference. To carry out our work we used the theory of fuzzy sets, the QMS in accordance with ISO 9001, BSC according to the method of Kaplan and Norton, method of construction of business processes according to the notation IDEF0, theory of modeling using Matlab software simulation tools and graphical programming LabVIEW. The results of the work are as follows: We determined possibilities of improving the management of heat-supply plant-based on QMS; after the justification and adaptation of software tool it has been used to automate a series of functions for the management and reduction of resources and for the maintenance of the system up to date; an application for the analysis of the QMS based on fuzzy inference has been created with novel organization of communication software with the application enabling the analysis of relevant data of enterprise management system.

Keywords: balanced scorecard, heat supply, quality management system, the theory of fuzzy sets

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Sarka Langer, Despoina Teli, Blanka Cabovska, Jan-Olof Dalenbäck, Lars Ekberg, Gabriel Bekö, Pawel Wargocki, Natalia Giraldo Vasquez

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The aim of the study was to investigate the impact of various ventilation systems on indoor climate, air pollution, chemistry, and perception. Measurements of thermal environment and indoor air quality were performed in 45 primary school classrooms in Gothenburg, Sweden. The classrooms were grouped into three categories according to their ventilation system: category A) natural or exhaust ventilation or automated window opening; category B) balanced mechanical ventilation systems with constant air volume (CAV); and category C) balanced mechanical ventilation systems with variable air volume (VAV). A questionnaire survey about indoor air quality, perception of temperature, odour, noise and light, and sensation of well-being, alertness focus, etc., was distributed among the 10-12 years old children attending the classrooms. The results (medians) showed statistically significant differences between ventilation category A and categories B and C, but not between categories B and C in air change rates, median concentrations of carbon dioxide, individual volatile organic compounds formaldehyde and isoprene, in-door-to-outdoor ozone ratios and products of ozonolysis of squalene, a constituent of human skin oils, 6-methyl-5-hepten-2-one and decanal. Median ozone concentration, ozone loss -a difference between outdoor and indoor ozone concentrations- were different only between categories A and C. Median concentration of total VOCs and a perception index based on survey responses on perceptions and sensations indoors were not significantly different. In conclusion, ventilation systems have an impact on air change rates, indoor air quality, and chemistry, but the Swedish primary school children’s perception did not differ with the ventilation systems of the classrooms.

Keywords: indoor air pollutants, indoor climate, indoor chemistry, air change rate, perception

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Authors: D. Malouch, M. Berchel, C. Dreanno, S. Stachowski-Haberkorn, P-A. Jaffres

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Biofouling is a complex natural phenomenon that involves biological, physical and chemical properties related to the environment, the submerged surface and the living organisms involved. Bio-colonization of artificial structures can cause various economic and environmental impacts. The increase in costs associated with the over-consumption of fuel from biocolonized vessels has been widely studied. Measurement drifts from submerged sensors, as well as obstructions in heat exchangers, and deterioration of offshore structures are major difficulties that industries are dealing with. Therefore, surfaces that inhibit biocolonization are required in different areas (water treatment, marine paints, etc.) and many efforts have been devoted to produce efficient and eco-compatible antifouling agents. The different steps of surface fouling are widely described in literature. Studying the biofilm and its stages provides a better understanding of how to elaborate more efficient antifouling strategies. Several approaches are currently applied, such as the use of biocide anti-fouling paint (mainly with copper derivatives) and super-hydrophobic coatings. While these two processes are proving to be the most effective, they are not entirely satisfactory, especially in a context of a changing legislation. Nowadays, the challenge is to prevent biofouling with non-biocide compounds, offering a cost effective solution, but with no toxic effects on marine organisms. Since the micro-fouling phase plays an important role in the regulation of the following steps of biofilm formation, it is desired to reduce or delate biofouling of a given surface by inhibiting the micro-fouling at its early stages. In our recent works, we reported that some amphiphilic compounds exhibited bacteriostatic or bactericidal properties at a concentration that did not affect mammalian eukaryotic cells. These remarkable properties invited us to assess this type of bio-inspired phospholipids to prevent the colonization of surfaces by marine bacteria. Of note, other studies reported that amphiphilic compounds interacted with bacteria leading to a reduction of their development. An amphiphilic compound is a molecule consisting of a hydrophobic domain and a polar head (ionic or non-ionic). These compounds appear to have interesting antifouling properties: some ionic compounds have shown antimicrobial activity, and zwitterions can reduce nonspecific adsorption of proteins. Herein, we investigate the potential of amphiphilic compounds as inhibitors of bacterial growth and marine biofilm formation. The aim of this study is to compare the efficacy of four synthetic phospholipids that features a cationic charge or a zwitterionic polar-head group to prevent microfouling with marine bacteria. Toxicity of these compounds was also studied in order to identify the most promising compounds that inhibit biofilm development and show low cytotoxicity on two links representative of coastal marine food webs: phytoplankton and oyster larvae.

Keywords: amphiphilic phospholipids, biofilm, marine fouling, non-toxique assays

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Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

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Authors: Hamidullah Binol, Abdullah Bal

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Nowadays, food safety is a great public concern; therefore, robust and effective techniques are required for detecting the safety situation of goods. Hyperspectral Imaging (HSI) is an attractive material for researchers to inspect food quality and safety estimation such as meat quality assessment, automated poultry carcass inspection, quality evaluation of fish, bruise detection of apples, quality analysis and grading of citrus fruits, bruise detection of strawberry, visualization of sugar distribution of melons, measuring ripening of tomatoes, defect detection of pickling cucumber, and classification of wheat kernels. HSI can be used to concurrently collect large amounts of spatial and spectral data on the objects being observed. This technique yields with exceptional detection skills, which otherwise cannot be achieved with either imaging or spectroscopy alone. This paper presents a nonlinear technique based on kernel Fukunaga-Koontz transform (KFKT) for detection of fat content in ground meat using HSI. The KFKT which is the nonlinear version of FKT is one of the most effective techniques for solving problems involving two-pattern nature. The conventional FKT method has been improved with kernel machines for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this paper aims to segment the fat content of the ground meat by regarding the fat as target class which is tried to be separated from the remaining classes (as clutter). We have applied the KFKT on visible and nearinfrared (VNIR) hyperspectral images of ground meat to determine fat percentage. The experimental studies indicate that the proposed technique produces high detection performance for fat ratio in ground meat.

Keywords: food (ground meat) inspection, Fukunaga-Koontz transform, hyperspectral imaging, kernel methods

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

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Authors: Stephen L. Green, Alexander N. Gorban, Ivan Y. Tyukin

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Within the past decade, using Convolutional Neural Networks (CNN)’s to create Deep Learning systems capable of translating Sign Language into text has been a breakthrough in breaking the communication barrier for deaf-mute people. Conventional research on this subject has been concerned with training the network to recognize the fingerspelling gestures of a given language and produce their corresponding alphanumerics. One of the problems with the current developing technology is that images are scarce, with little variations in the gestures being presented to the recognition program, often skewed towards single skin tones and hand sizes that makes a percentage of the population’s fingerspelling harder to detect. Along with this, current gesture detection programs are only trained on one finger spelling language despite there being one hundred and forty-two known variants so far. All of this presents a limitation for traditional exploitation for the state of current technologies such as CNN’s, due to their large number of required parameters. This work aims to present a technology that aims to resolve this issue by combining a pretrained legacy AI system for a generic object recognition task with a corrector method to uptrain the legacy network. This is a computationally efficient procedure that does not require large volumes of data even when covering a broad range of sign languages such as American Sign Language, British Sign Language and Chinese Sign Language (Pinyin). Implementing recent results on method concentration, namely the stochastic separation theorem, an AI system is supposed as an operate mapping an input present in the set of images u ∈ U to an output that exists in a set of predicted class labels q ∈ Q of the alphanumeric that q represents and the language it comes from. These inputs and outputs, along with the interval variables z ∈ Z represent the system’s current state which implies a mapping that assigns an element x ∈ ℝⁿ to the triple (u, z, q). As all xi are i.i.d vectors drawn from a product mean distribution, over a period of time the AI generates a large set of measurements xi called S that are grouped into two categories: the correct predictions M and the incorrect predictions Y. Once the network has made its predictions, a corrector can then be applied through centering S and Y by subtracting their means. The data is then regularized by applying the Kaiser rule to the resulting eigenmatrix and then whitened before being split into pairwise, positively correlated clusters. Each of these clusters produces a unique hyperplane and if any element x falls outside the region bounded by these lines then it is reported as an error. As a result of this methodology, a self-correcting recognition process is created that can identify fingerspelling from a variety of sign language and successfully identify the corresponding alphanumeric and what language the gesture originates from which no other neural network has been able to replicate.

Keywords: convolutional neural networks, deep learning, shallow correctors, sign language

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Authors: A. Sohouli, A. Suleman

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This study focuses on reliability analysis of variable stiffness composite laminate structures to investigate the potential structural improvement compared to conventional (straight fibers) composite laminate structures. A computational framework was developed which it consists of a deterministic design step and reliability analysis. The optimization part is Discrete Material Optimization (DMO) and the reliability of the structure is computed by Monte Carlo Simulation (MCS) after using Stochastic Response Surface Method (SRSM). The design driver in deterministic optimization is the maximum stiffness, while optimization method concerns certain manufacturing constraints to attain industrial relevance. These manufacturing constraints are the change of orientation between adjacent patches cannot be too large and the maximum number of successive plies of a particular fiber orientation should not be too high. Variable stiffness composites may be manufactured by Automated Fiber Machines (AFP) which provides consistent quality with good production rates. However, laps and gaps are the most important challenges to steer fibers that effect on the performance of the structures. In this study, the optimal curved fiber paths at each layer of composites are designed in the first step by DMO, and then the reliability analysis is applied to investigate the sensitivity of the structure with different standard deviations compared to the straight fiber angle composites. The random variables are material properties and loads on the structures. The results show that the variable stiffness composite laminate structures are much more reliable, even for high standard deviation of material properties, than the conventional composite laminate structures. The reason is that the variable stiffness composite laminates allow tailoring stiffness and provide the possibility of adjusting stress and strain distribution favorably in the structures.

Keywords: material optimization, Monte Carlo simulation, reliability analysis, response surface method, variable stiffness composite structures

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Authors: Odaleia de O. Farias, Suzelene C. Marinho, Ecleidson B. Fragoso, Daniel S. Lima, Francisco R. S. Lira, Lara S. Araújo, Gabriel dos S. D. Soares

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In populous cities, prehospital care services that use vehicles alternative to ambulances are needed in order to reduce costs and improve response time to occurrences in areas with large concentration of people, such as leisure and tourism spaces. In this context, it was implanted a program called BIKE VIDA, that is innovative quick access and assistance program. The aim of this study is to describe the implantation and initial profile of occurrences performed by an urgency/emergency pre-hospital care service through paramedics on bicycles. It is a cross-sectional, descriptive study carried out in the city of Fortaleza, Ceara, Brazil. The data included service records from July to August 2017. Ethical aspects were respected. The service covers a perimeter of 4.5 km, divided into three areas with perimeter of 1.5 km for each paramedic, attending from 5 am to 9 pm. Materials transported by bicycles include External Automated Defibrillator - DEA, portable oxygen, oximeter, cervical collar, stethoscope, sphygmomanometer, dressing and immobilization materials and personal protective equipment. Occurrences are requested directly by calling the emergency number 192 or through direct approach to the professional. In the first month of the program, there were 93 emergencies/urgencies, mainly in the daytime period (71,0%), in males (59,7%), in the age range of 26 to 45 years (46,2%). The main nature was traumatic incidents (53.3%). Most of the cases (88,2%) did not require ambulance transport to the hospital, and there were two deaths. Pre-hospital service through bicycles is an innovative strategy in Brazil and has shown to be promising in terms of reducing costs and improving the quality of the services offered.

Keywords: emergency, response time, prehospital care, urgency

Procedia PDF Downloads 197