Search results for: safety helmet-wearing detection

Authors: Rathna Malhotra Gaur

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Owing to multiplicity and competition in the Indian food industry, it was always important for the government of India to bring in reforms that would protect the interest of the consumer and also the food operator. To further this objective, Food Safety, and Standards Act, 2006 (hereinafter referred to as FSSAI) was enacted for laying down science-based standards for articles and food and to regulate their storage, distribution, manufacture, same and import and to ensure safe food availability to the citizens of India. One of the safeguards towards consumer interest is the enactment of Food Safety and Standards (Licensing and Registration of Food Businesses, Regulation, 2011 within the mandate of FSSAI. It is mandatory for every food operator in India to get the registration certificate and procurement of food Licenses before starting operations in the country. All the nuances pertaining to the procurement of licenses are dealt with under these regulations. These regulations also lay down detailed provisions with regard to the conditions that the operator has to adhere to once the License is procured, going to the integrities of the safety and hygiene standards to be maintained by the food operators. This paper is an exhaustive effort to examine the provisions of obtaining the registration and License in India and the conditions that need to be fulfilled subsequently and further on the validity and renewal of these Food Licenses.

Keywords: food laws, food licenses, food registration, penalty

Procedia PDF Downloads 179

Authors: Mohammed Muzafar, Darshan Ruikar

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BIM, an acronym for Building Information Modelling relates to the practice of creating a computer generated model which is capable of displaying the planning, design, construction and operation of a structure. The resulting simulation is a data-rich, object-oriented, intelligent and parametric digital representation of the facility, from which views and data, appropriate to various users needs can be extracted and analysed to generate information that can be used to make decisions and to improve the process of delivering the facility. BIM also refers to a shift in culture that will influence the way the built environment and infrastructure operates and how it is delivered. One of the main issues of concern in the construction industry at present in the UK is its record on Health & Safety (H&S). It is, therefore, important that new technologies such as BIM are developed to help improve the quality of health and safety. Historically the H&S record of the construction industry in the UK is relatively poor as compared to the manufacturing industries. BIM and the digital environment it operates within now allow us to use design and construction data in a more intelligent way. It allows data generated by the design process to be re-purposed and contribute to improving efficiencies in other areas of a project. This evolutionary step in design is not only creating exciting opportunities for the designers themselves but it is also creating opportunity for every stakeholder in any given project. From designers, engineers, contractors through to H&S managers, BIM is accelerating a cultural change. The paper introduces the concept behind a research project that mitigates the H&S risks associated with the construction, operation and maintenance of assets through the adoption of BIM.

Keywords: building information modeling, BIM levels, health, safety, integration

Procedia PDF Downloads 255

Authors: Meltem Agar, Maisem Laabei, Hannah Leese, Pedro Estrela

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Pathogenic bacteria and the diseases they cause have become a global problem. Their early detection is vital and can only be possible by detecting the bacteria causing the disease accurately and rapidly. Great progress has been made in this field with the use of biosensors. Molecularly imprinted polymers have gain broad interest because of their excellent properties over natural receptors, such as being stable in a variety of conditions, inexpensive, biocompatible and having long shelf life. These properties make molecularly imprinted polymers an attractive candidate to be used in biosensors. In this study it is aimed to produce an aptamer-molecularly imprinted polymer based electrochemical sensor by utilizing the properties of molecularly imprinted polymers coupled with the enhanced specificity offered by DNA aptamers. These ‘apta-MIP’ sensors were used for the detection of Staphylococcus aureus and Escherichia coli. The experimental parameters for the fabrication of sensor were optimized, and detection of the bacteria was evaluated via Electrochemical Impedance Spectroscopy. Sensitivity and selectivity experiments were conducted. Furthermore, molecularly imprinted polymer only and aptamer only electrochemical sensors were produced separately, and their performance were compared with the electrochemical sensor produced in this study. Aptamer-molecularly imprinted polymer based electrochemical sensor showed good sensitivity and selectivity in terms of detection of Staphylococcus aureus and Escherichia coli. The performance of the sensor was assessed in buffer solution and tap water.

Keywords: aptamer, electrochemical sensor, staphylococcus aureus, molecularly imprinted polymer

Procedia PDF Downloads 118

Authors: Ndivhudzannyi Rembuluwani, Francis Dacosta, Emmanuel Mhlongo

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The small-scale rock aggregate sector of the mining industry is a major source of employment for a significant number of people, particularly in remote rural areas, where alternative livelihoods are rare. It contributes to local economy by generating income and producing major and essential materials for the building, construction, and other industries. However, the sector is confronted with many challenges that hamper productivity and growth. The problems that confront this sector includes: health and safety, environmental impacts, low production and low adherence to mining legislations. This study investigated the challenges confronting selected small-scale rock aggregate mines in the Vhembe District of Limpopo province of South Africa, assesses the health, safety, low production and environmental impacts associated with aggregate production and to develop an integrated approach of addressing the multi-faceted challenges.

Keywords: health and safety, legislative framework, productivity, rock aggregate, small-scale mining

Procedia PDF Downloads 505

Authors: Ivana Loncarevic, Biljana Pajin, Ivana Vasiljevic, Milana Lazovic, Danica Mrkajic, Aleksandar Fises, Strahinja Kovacevic

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Biscuits belong to a group of flour-confectionery products that are considerably consumed worldwide. The basic raw material for their production is wheat flour or integral flour as a nutritionally highly valuable component. However, this raw material is also a potential source of contamination since it may contain the residues of biochemical compounds originating from plant and soil protection agents. Therefore, it is necessary to examine the health safety of both raw materials and final products. The aim of this research was to examine the content of undesirable residues of pesticides (mostly organochlorine pesticides, organophosphorus pesticides, carbamate pesticides, triazine pesticides, and pyrethroid pesticides) in 30 different biscuit samples of domestic origin present on the Vojvodina market using Gas Chromatograph Thermo ISQ/Trace 1300. The results showed that all tested samples had the limit of detection of pesticide content below 0.01 mg/kg, indicating that this type of confectionary products is not contaminated with pesticides.

Keywords: biscuits, pesticides, contamination, quality

Procedia PDF Downloads 187

Authors: José Eugenio Ortiz, Pierre Plaza, Josefa Herrero, Iván Cabria, José Luis Blanco, Javier Gavilanes, José Ignacio Escavy, Ignacio López-Cilla, Virginia Yagüe, César Pérez, Silvia Rodríguez, Jorge Rico, Cecilia Serrano, Jesús Bernat

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The development of Artificial Intelligence services in mining processes, specifically in aggregate quarries, is facilitating automation and improving numerous aspects of operations. Ultimately, AI is transforming the mining industry by improving efficiency, safety and sustainability. With the ability to analyze large amounts of data and make autonomous decisions, AI offers great opportunities to optimize mining operations and maximize the economic and social benefits of this vital industry. Within the framework of the European DIGIECOQUARRY project, various services were developed for the identification of material quality, production estimation, detection of anomalies and prediction of consumption and production automatically with good results.

Keywords: aggregates, artificial intelligence, automatization, mining operations

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Authors: Ho Young Son, Bu Seog Ju, Woo Young Jung

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This study presents the seismic safety evaluation of weir structure subjected to strong earthquake ground motions, as a flood defense structure in civil engineering structures. The seismic safety analysis procedure was illustrated through development of Finite Element (FE) and InFinite Element (IFE) method in ABAQUS platform. The IFE model was generated by CINPS4, 4-node linear one-way infinite model as a sold continuum infinite element in foundation areas of the weir structure and then nonlinear FE model using friction model for soil-structure interactions was applied in this study. In order to understand the complex behavior of weir structures, nonlinear time history analysis was carried out. Consequently, it was interesting to note that the compressive stress gave more vulnerability to the weir structure, in comparison to the tensile stress, during an earthquake. The stress concentration of the weir structure was shown at the connection area between the weir body and stilling basin area. The stress both tension and compression was reduced in IFE model rather than FE model of weir structures.

Keywords: seismic, numerical analysis, FEM, weir, boundary condition

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Authors: Taimaz Larimian, Negin Sadeghi

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Very little is known about the impacts of urban security on the level of social sustainability within the cities of developing countries. Urban security is still struggling to find its position in the social sustainability agenda, despite the significant role of safety and security on different aspects of peoples’ lives. This paper argues that urban safety and security should be better integrated within the social sustainability framework. With this aim, this study investigates the hypothesized relationship between social sustainability and Crime Prevention through Environmental Design (CPTED) approach at the neighborhood scale. This study proposes a model of key influential dimensions of CPTED analyzed into localized factors and sub-factors. These factors are then prioritized using pairwise comparison logic and fuzzy group Analytic Hierarchy Process (AHP) method in order to determine the relative importance of each factor on achieving social sustainability. The proposed model then investigates social sustainability in six case study neighborhoods of Isfahan city based on residents’ perceptions of safety within their neighborhood. Mixed method of data collection is used by using a self-administered questionnaire to explore the residents’ perceptions of social sustainability in their area of residency followed by an on-site observation to measure the CPTED construct. In all, 150 respondents from selected neighborhoods were involved in this research. The model indicates that CPTED approach has a significant direct influence on increasing social sustainability in neighborhood scale. According to the findings, among different dimensions of CPTED, ‘activity support’ and ‘image/ management’ have the most influence on people’s feeling of safety within studied areas. This model represents a useful designing tool in achieving urban safety and security during the development of more socially sustainable and user-friendly urban areas.

Keywords: crime prevention through environmental design (CPTED), developing countries, fuzzy analytic hierarchy process (FAHP), social sustainability

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Arabi Abderrazak, Bourouba Nacerdine, Ayad Mouloud, Belaout Abdeslam

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In this paper, a new test technique of analog circuits using time mode simulation is proposed for the single catastrophic faults detection in analog circuits. This test process is performed to overcome the problem of catastrophic faults being escaped in a DC mode test applied to the inverter amplifier in previous research works. The circuit under test is a second-order low pass filter constructed around this type of amplifier but performing a function that differs from that of the previous test. The test approach performed in this work is based on two key- elements where the first one concerns the unique square pulse signal selected as an input vector test signal to stimulate the fault effect at the circuit output response. The second element is the filter response conversion to a square pulses sequence obtained from an analog comparator. This signal conversion is achieved through a fixed reference threshold voltage of this comparison circuit. The measurement of the three first response signal pulses durations is regarded as fault effect detection parameter on one hand, and as a fault signature helping to hence fully establish an analog circuit fault diagnosis on another hand. The results obtained so far are very promising since the approach has lifted up the fault coverage ratio in both modes to over 90% and has revealed the harmful side of faults that has been masked in a DC mode test.

Keywords: analog circuits, analog faults diagnosis, catastrophic faults, fault detection

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

Procedia PDF Downloads 276

Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Koichi Kurita

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In this study, an effective non-contact technique for the detection of human physical activity is proposed. The technique is based on detecting the electrostatic induction current generated by the walking motion under non-contact and non-attached conditions. A theoretical model for the electrostatic induction current generated because of a change in the electric potential of the human body is proposed. By comparing the obtained electrostatic induction current with the theoretical model, it becomes obvious that this model effectively explains the behavior of the waveform of the electrostatic induction current. The normal walking motions are recorded using a portable sensor measurement located in a passageway　of office building. The obtained results show that detailed information regarding physical activity such as a walking cycle can be estimated using our proposed technique. This suggests that the proposed technique which is based on the detection of the walking signal, can be successfully applied to the detection of human walking motion in a secured building.

Keywords: human walking motion, access control, electrostatic induction, alarm monitoring

Procedia PDF Downloads 357

Authors: Deonie Botha

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Digital transformation and the digitization of occupational health and safety training have grown exponentially due to a variety of contributing factors. The literature suggests that digitalization has numerous benefits but also has associated challenges. The aim of the paper is to develop an understanding of both the perceived benefits and challenges of digitalization in an occupational health and safety context in an effort to design and develop e-learning interventions that will optimize the benefits of digitalization and address the associated challenges. The paper proposes, deliberate and tests the design principles of an e-learning intervention to ensure alignment with the requirements of a digitally transformed environment. The results of the research are based on a literature review regarding the requirements and effect of the Fourth Industrial Revolution on learning and e-learning in particular. The findings of the literature review are enhanced with empirical research in the form of a case study conducted in an organization that designs and develops e-learning content in the occupational health and safety industry. The primary findings of the research indicated that: (i) The requirements of learners and organizations in respect of e-learning are different than previously (i.e., a pre-Fourth Industrial Revolution related work setting). (ii) The design principles of an e-learning intervention need to be aligned with the entire value chain of the organization. (iii) Digital twins support and enhance the design and development of e-learning. (iv)Learning should incorporate a multitude of sensory experiences and should not only be based on visual stimulation. (v) Data that are generated as a result of e-learning interventions should be incorporated into big data streams to be analyzed and to become actionable. It is therefore concluded that there is general consensus on the requirements that e-learning interventions need to adhere to in a digitally transformed occupational health and safety work environment. The challenge remains for organizations to incorporate data generated as a result of e-learning interventions into the digital ecosystem of the organization.

Keywords: digitalization, training, fourth industrial revolution, big data

Procedia PDF Downloads 156

Authors: Delphine Vendryes, Dushyanth Sekhar, Baojia Tong, Matthew Theisen, Chester Curme

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Much of the data that inform the decisions of governments, corporations and individuals are harvested from unstructured documents. Data extraction is defined here as a process that turns non-machine-readable information into a machine-readable format that can be stored, for instance, in a database. In financial services, introducing more automation in data extraction pipelines is a major challenge. Information sought by financial data consumers is often buried within vast bodies of unstructured documents, which have historically required thorough manual extraction. Automated solutions provide faster access to non-machine-readable datasets, in a context where untimely information quickly becomes irrelevant. Data quality standards cannot be compromised, so automation requires high data integrity. This multifaceted task is broken down into smaller steps: ingestion, table parsing (detection and structure recognition), text analysis (entity detection and disambiguation), schema-based record extraction, user feedback incorporation. Selected intermediary steps are phrased as machine learning problems. Solutions leveraging cutting-edge approaches from the fields of computer vision (e.g. table detection) and natural language processing (e.g. entity detection and disambiguation) are proposed.

Keywords: computer vision, entity recognition, finance, information retrieval, machine learning, natural language processing

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Authors: Bilal Saoud

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In this paper, we propose a new method to find the community structure in networks. Our method is based on bee colony and the maximization of modularity to find the community structure. We use a bee colony algorithm to find the first community structure that has a good value of modularity. To improve the community structure, that was found, we merge communities until we get a community structure that has a high value of modularity. We provide a general framework for implementing our approach. We tested our method on computer-generated and real-world networks with a comparison to very known community detection methods. The obtained results show the effectiveness of our proposition.

Keywords: bee colony, networks, modularity, normalized mutual information

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Authors: Arth J. Shah, Madhu R. Kamble

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Voice biometric liveness detection is customized to certify an authentication process of the voice data presented is genuine and not a recording or synthetic voice. With the rise of deepfakes and other equivalently sophisticated spoofing generation techniques, it’s becoming challenging to ensure that the person on the other end is a live speaker or not. Voice Liveness Detection (VLD) system is a group of security measures which detect and prevent voice spoofing attacks. Motivated by the recent development of the Kolmogorov-Arnold Network (KAN) based on the Kolmogorov-Arnold theorem, we proposed KAN for the VLD task. To date, multilayer perceptron (MLP) based classifiers have been used for the classification tasks. We aim to capture not only the compositional structure of the model but also to optimize the values of univariate functions. This study explains the mathematical as well as experimental analysis of KAN for VLD tasks, thereby opening a new perspective for scientists to work on speech and signal processing-based tasks. This study emerges as a combination of traditional signal processing tasks and new deep learning models, which further proved to be a better combination for VLD tasks. The experiments are performed on the POCO and ASVSpoof 2017 V2 database. We used Constant Q-transform, Mel, and short-time Fourier transform (STFT) based front-end features and used CNN, BiLSTM, and KAN as back-end classifiers. The best accuracy is 91.26 % on the POCO database using STFT features with the KAN classifier. In the ASVSpoof 2017 V2 database, the lowest EER we obtained was 26.42 %, using CQT features and KAN as a classifier.

Keywords: Kolmogorov Arnold networks, multilayer perceptron, pop noise, voice liveness detection

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Authors: Moise A. E., State G., Tudorache M., Custură I., Enea D. N., Osman (Defta) A., Drăgotoiu D.

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This scientific paper explores risk management strategies in the food supply chain, with a focus on chicken raw materials, in the context of a company sourcing from the EU and non-EU. The aim of the paper is to adapt the requirements of international standards (IFS, BRC, QS, ITW, FSSC, ISO), proposing efficient methods to identify and remediate non-conformities and corrective and preventive actions. Defining the supply flow and acceptance steps promotes collaboration with suppliers to ensure the quality and safety of raw materials. To assess the risks of suppliers and raw materials, objective criteria are developed and vulnerabilities in the supply chain are analyzed, including the risk of fraud. Active monitoring of international alerts through RASFF helps to identify emerging risks quickly, and regular analysis of international trends and company performance enables continuous adaptation of risk management strategies. Implementing these measures strengthens food safety and consumer confidence in the final products supplied.

Keywords: food supply chain, international standards, quality and safety of raw materials, RASFF

Procedia PDF Downloads 51

Authors: Soheila Ghanbari

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After the Civil Rights Act of 1964 was enacted, federal policy transformed employment rights. Equal employment opportunity law, legislation for occupational safety and health, and regulations for fringe benefits were established to ensure that employees have rights to equal protection, health and safety, and the benefits guaranteed by employers. In research analyzing data from 279 organizations over time, it was discovered that legal changes prompted organizations to establish personnel, antidiscrimination, safety, and benefits departments to ensure compliance. However, as the process of institutionalization advanced, middle managers began to separate these fresh offices from policy and rationalize them solely in economic terms as a component of the new human resources management model. This common occurrence is seen in the United States, where the Constitution represents government control of business as unlawful. It could potentially clarify the extended lack of a state theory in organizational analysis and shed light on a puzzle pointed out by state theorists: the federal state is weak in terms of administration but strong in terms of norms.

Keywords: management, state, human, resources, employment

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Authors: Sajjad Ahmed, Knut Hinkelmann, Flavio Corradini

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Fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.

Keywords: fake news detection, natural language processing, machine learning, classification techniques.

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Authors: Lae-Jeong Park

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The paper presents a method that utilizes figure-ground color segmentation to extract effective global feature in terms of false positive reduction in the head-shoulder detection. Conventional detectors that rely on local features such as HOG due to real-time operation suffer from false positives. Color cue in an input image provides salient information on a global characteristic which is necessary to alleviate the false positives of the local feature based detectors. An effective approach that uses figure-ground color segmentation has been presented in an effort to reduce the false positives in object detection. In this paper, an extended version of the approach is presented that adopts separate multipart foregrounds instead of a single prior foreground and performs the figure-ground color segmentation with each of the foregrounds. The multipart foregrounds include the parts of the head-shoulder shape and additional auxiliary foregrounds being optimized by a search algorithm. A classifier is constructed with the feature that consists of a set of the multiple resulting segmentations. Experimental results show that the presented method can discriminate more false positive than the single prior shape-based classifier as well as detectors with the local features. The improvement is possible because the presented approach can reduce the false positives that have the same colors in the head and shoulder foregrounds.

Keywords: pedestrian detection, color segmentation, false positive, feature extraction

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Authors: Yagahira E. Castro-Sesquen, Chloe Kim, Robert H. Gilman, David J. Sullivan, Peter C. Searson

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Diagnosis of severe malaria is particularly important in highly endemic regions since most patients are positive for parasitemia and treatment differs from non-severe malaria. Diagnosis can be challenging due to the prevalence of diseases with similar symptoms. Accurate diagnosis is increasingly important to avoid overprescribing antimalarial drugs, minimize drug resistance, and minimize costs. A nanoparticle-based assay for detection and quantification of Plasmodium falciparum histidine-rich protein 2 (HRP2) in urine and serum is reported. The assay uses magnetic beads conjugated with anti-HRP2 antibody for protein capture and concentration, and antibody-conjugated quantum dots for optical detection. Western Blot analysis demonstrated that magnetic beads allows the concentration of HRP2 protein in urine by 20-fold. The concentration effect was achieved because large volume of urine can be incubated with beads, and magnetic separation can be easily performed in minutes to isolate beads containing HRP2 protein. Magnetic beads and Quantum Dots 525 conjugated to anti-HRP2 antibodies allows the detection of low concentration of HRP2 protein (0.5 ng mL-1), and quantification in the range of 33 to 2,000 ng mL-1 corresponding to the range associated with non-severe to severe malaria. This assay can be easily adapted to a non-invasive point-of-care test for classification of severe malaria.

Keywords: HRP2 protein, malaria, magnetic beads, Quantum dots

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Authors: Anastasios Manos, Despina Elisabeth Filippidou

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Introduction: Historical buildings are an inte-gral part of the cultural heritage of every place, and beyond the obvious need for protection against risks, they have specific requirements regarding the handling of hazards and disasters such as fire, floods, earthquakes, etc. Ensuring high levels of protection and safety for these buildings is impera-tive for two distinct but interconnected reasons: a) they themselves constitute cultural heritage, and b) they are often used as museums/cultural spaces, necessitating the protection of both human life (vis-itors and workers) and the cultural treasures they house. However, these buildings present serious constraints in implementing the necessary measures to protect them from destruction due to their unique architecture, construction methods, and/or the structural materials used in the past, which have created an existing condition that is sometimes challenging to reshape and operate within the framework of modern regulations and protection measures. One of the most devastating risks that threaten historical buildings is fire. Catastrophic fires demonstrate the need for timely evaluation of fire safety measures in historical buildings. Recog-nizing the criticality of protecting historical build-ings from the risk of fire, the Confederation of Fire Protection Associations in Europe (CFPA E) issued specific guidelines in 2013 (CFPA-E Guideline No 30:2013 F) for the fire protection of historical buildings at the European level. However, until now, few actions have been implemented towards leveraging modern technologies in the field of con-struction and maintenance of buildings, such as Building Information Modeling (BIM) and the Inter-net of Things (IoT), for the protection of historical buildings from risks like fires, floods, etc. The pro-ject BIM4Cult has bee developed in order to fill this gap. It is a tool for timely assessing and monitoring of the fire safety level of historical buildings using BIM and IoT technologies in an integrated manner. The tool serves as a decision support expert system for improving the fire safety of historical buildings by continuously monitoring, controlling and as-sessing critical risk factors for fire.

Keywords: Iot, fire, BIM, expert system

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Authors: M. V. Chidananda Murthy, M. Z. Kurian, H. S. Guruprasad

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Image restoration in the process of communication is one of the emerging fields in the image processing. The motion analysis processing is the simplest case to detect motion in an image. Applications of motion analysis widely spread in many areas such as surveillance, remote sensing, film industry, navigation of autonomous vehicles, etc. The scene may contain multiple moving objects, by using motion analysis techniques the blur caused by the movement of the objects can be enhanced by filling-in occluded regions and reconstruction of transparent objects, and it also removes the motion blurring. This paper presents the design and comparison of various motion detection and enhancement filters. Median filter, Linear image deconvolution, Inverse filter, Pseudoinverse filter, Wiener filter, Lucy Richardson filter and Blind deconvolution filters are used to remove the blur. In this work, we have considered different types and different amount of blur for the analysis. Mean Square Error (MSE) and Peak Signal to Noise Ration (PSNR) are used to evaluate the performance of the filters. The designed system has been implemented in Matlab software and tested for synthetic and real-time images.

Keywords: image enhancement, motion analysis, motion detection, motion estimation

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Authors: A. J. Babajanyan, T. A. Abrahamyan, H. A. Minasyan, K. V. Nerkararyan

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Optical radiation emitted from a metal-coated fiber tip apex at liquid-air interface was measured. The intensity of the output radiation was strongly depending on the relative position of the tip to a liquid-air interface and varied with surface fluctuations. This phenomenon permits in-situ real-time investigation of nano-metric vibrations of the liquid surface and provides a basis for development of various origin ultrasensitive vibration detecting sensors. The described method can be used for detection of week seismic vibrations.

Keywords: fiber-tip, liquid-air interface, nano vibration, opto-mechanical sensor

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Authors: Lingzhi Li, Jiankun Zhang, Tiantian Gu

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Hazard management that can prevent fatal accidents and property losses is a fundamental process during the buildings’ construction stage. However, due to lack of safety supervision resources and operational pressures, the conduction of hazard management is poor and ineffective in China. In order to improve the quality of construction safety management, it is critical to explore the use of information technologies to ensure that the process of hazard management is efficient and effective. After exploring the existing problems of construction hazard management in China, this paper develops the griddization management model for construction hazard management. First, following the knowledge grid infrastructure, the griddization computing infrastructure for construction hazards management is designed which includes five layers: resource entity layer, information management layer, task management layer, knowledge transformation layer and application layer. This infrastructure will be as the technical support for realizing grid management. Second, this study divides the construction hazards into grids through city level, district level and construction site level according to grid principles. Last, a griddization management process including hazard identification, assessment and control is developed. Meanwhile, all stakeholders of construction safety management, such as owners, contractors, supervision organizations and government departments, should take the corresponding responsibilities in this process. Finally, a case study based on actual construction hazard identification, assessment and control is used to validate the effectiveness and efficiency of the proposed griddization management model. The advantage of this designed model is to realize information sharing and cooperative management between various safety management departments.

Keywords: construction hazard, griddization computing, grid management, process

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Authors: Brandtner-Hafner Martin

Abstract:

Adhesives are increasingly being used in the construction sector. On the one hand, this concerns dowel reinforcements using chemical anchors. On the other hand, the sealing and repair of cracks in structural concrete components are still on the rise. In the field of bonding, the interface between the joined materials is the most critical area. Therefore, it is of immense importance to characterize and investigate this section sufficiently by fracture analysis. Since standardized mechanical test methods are not sufficiently capable of doing this, recourse is made to an innovative concept based on fracture energy. Therefore, a series of experimental tests were performed using the so-called GF-principle to study the interface bonding safety of adhesively bonded concrete joints. Several different structural adhesive systems based on epoxy, CA/A hybrid, PUR, MS polymer, dispersion, and acrylate were selected for bonding concrete substrates. The results show that stable crack propagation and prevention of uncontrolled failure in bonded concrete joints depend very much on the adhesive system used, and only fracture analytical evaluation methods can provide empirical information on this.

Keywords: interface bonding safety, adhesively bonded concrete joints, GF-principle, fracture analysis

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Authors: Sao-Jeng Chao, Chia-Yun Wei, Si-Han Lai, Cheng-Yu Huang, Yu-Han Teng

Abstract:

In Taiwan, the landforms are mainly dominated by mountains and hills. Many road sections of the National Highway are impossible to avoid problems such as slope excavation or slope filling. In order to increase the safety of the slope, various slope protection methods are used to stabilize the slope, especially the soil anchor technique is the most common. This study is inspired by the soil liquefaction potential map. The concept of the potential map is widely used. The typhoon, earth-rock flow, tsunami, flooded area, and the recent discussion of soil liquefaction have safety potential concepts. This paper brings the concept of safety potential to the anchored slope. Because the soil anchor inspection is only the concept of points, this study extends the concept of the point to the surface, using the Quantum GIS program to present the slope damage area, and depicts the slope appearance and soil anchor point with the slope as-built drawing. The soil anchor scores are obtained by anchor inspection data, and the low, medium and high potential areas are remitted by interpolation. Thus, the area where the anchored slope may be harmful is judged and relevant maintenance is provided. The maintenance units can thus prevent judgment and deal with the anchored slope as soon as possible.

Keywords: anchor, slope, potential map, lift-off test, existing load

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

Abstract:

Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

Procedia PDF Downloads 342

Authors: M. Brockes, S. Beck, A. Sigaroudi, C. Brockes

Abstract:

Over the last few years, telemedicine and telemonitoring have become popular ways of treatment, especially in other chronic diseases. Therefore, this type of treatment methodology was also implemented in patients with interstitial lung disease (ILD). In January 2024, a new service for patients with interstitial lung disease (ILD) treated with Nintedanib was established, which contains daily telemonitoring (home spirometry, pulse oximetry, and daily level of activity), daily evaluation of parameters as well as a telemedical availability answered by doctors and telemedical specialists throughout 365 days per year. The main motivational points of this service are the early detection of first signs of exacerbations and/or other symptoms/complications, as well as easier access to healthcare professionals. The evaluation of the patient’s quality of life and the subjective feeling of safetyness was measured through patient-reported experience measurements (PREMs) and patient-reported outcome measurements (PROMs). Patients were introduced to the telemedical and telemonitoring service six months ago. Within this period, every sixty days, the questionnaires were conducted by the scientific employees. Due to the unlimited time frame of the long-term service, the evaluation has not been completed. The first analysis of patient-reported experience measurements (PREMs) and patient reported outcome measurements (PROMs) had shown an increased positive effect on the patients' quality of life as well as an increased positive effect on the subjective feeling of safety at home, plus a reduction and avoidance of secondary damages (e.g., exacerbations, deterioration of typical interstitial lung disease ILD symptoms and pharmaceutical side effects). The first results have shown a tendency that the telemedical treatment combined with telemonitoring at home and the encouragement of patients to actively participate in their healthcare has a positive effect on the patient’s overall well-being and could be implemented as a complementation of the traditional standard of care.

Keywords: avoidance of secondary damages, interstitial lung disease, telemedicine and telemonitoring, subjective feeling of safety

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