Search results for: George André Pereira Thé

Authors: John Ayisi, Gideon M. Kivengea, George A. Ombakho

Abstract:

In this emerging era of knowledge economy, universities, as major centres of learning and research, are becoming increasingly important as sources of ideas, knowledge, skills, innovation and technological advances. These ideas can be turned into new products, processes and systems needed to drive their respective national economies, and thus placing universities at the centre of the national innovation systems. Thus, commercialization of research outputs from universities to industry has become an area of strong policy interest in African countries. To assess the level of commercialization of research outputs in Kenyan universities, a standardized questionnaire covering seven sub-sections, namely: University Commercialization Environment, Management of Commercialization Activities, Commercialization Office, Intellectual Property Rights (IPRs), Early Stage Financing and Venture Capital; Industrial Linkages; and Technology Parks and Incubators was administered among a few selected public and private universities. Results show that all the universities have a strategic plan; though not all have innovation and commercialization as part of it. Half the nineteen surveyed universities indicated they have created designated offices for fostering commercialization. Majority have guidelines on IPRs which advocate IP to be co-owned by researcher/university. University-industry linkages are weak. Most universities are taking precursory steps to incentivise and encourage entrepreneurial activities among their academic staff and students, even though the level of resources devoted to them is low. It is recommended that building capacity in entrepreneurship among staff and students and committing more resources to R&D activities hold potential to increased commercialization of university research outputs.

Keywords: commercialization, knowledge, R&D, university

Procedia PDF Downloads 405

Authors: Mojgan Rabiey, Shyamali Roy, Billy Quilty, Ryan Creeth, George Sundin, Robert W. Jackson

Abstract:

Bacterial canker is a major disease of Prunus species such as cherry (Prunus avium). It is caused by Pseudomonas syringae species including P. syringae pv. syringae (Pss) and P. syringae pv. morsprunorum race 1 (Psm1) and race 2 (Psm2). Concerns over the environmental impact of, and developing resistance to, copper controls call for alternative approaches to disease management. One method of control could be achieved using naturally occurring bacteriophage (phage) infective to the bacterial pathogens. Phages were isolated from soil, leaf, and bark of cherry trees in five locations in the South East of England. The phages were assessed for their host range against strains of Pss, Psm1, and Psm2. The phages exhibited a differential ability to infect and lyse different Pss and Psm isolates as well as some other P. syringae pathovars. However, the phages were unable to infect beneficial bacteria such as Pseudomonas fluorescens. A subset of 18 of these phages were further characterised genetically (Random Amplification of Polymorphic DNA-PCR fingerprinting and sequencing) and using electron microscopy. The phages are tentatively identified as belonging to the order Caudovirales and the families Myoviridae, Podoviridae, and Siphoviridae, with genetic material being dsDNA. Future research will fully sequence the phage genomes. The efficacy of the phage, both individually and in cocktails, to reduce disease progression in vivo will be investigated to understand the potential for practical use of these phages as biocontrol agents.

Keywords: bacteriophage, pseudomonas, bacterial cancker, biological control

Procedia PDF Downloads 126

Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

Abstract:

Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

Procedia PDF Downloads 19

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

Procedia PDF Downloads 383

Authors: Vivek Ku, A. K. Janmeja, D. Aggarwal, R. Gupta

Abstract:

Purpose: Pulmonary rehabilitation plays a key role in management of chronic lung diseases. However, pulmonary rehabilitation is an underused modality in the management of interstitial lung disease (ILD). This is because limited information is available in literature and no data is available from India on this issue so far. The study was carried out to evaluate the role of pulmonary rehabilitation on respiratory parameters in ILD patients. Methods: The present study was a prospective randomized non-blind case control study. Total of 40 ILD patients were randomized into 2 groups of 20 patients each viz ‘pulmonary rehabilitation group’ and ‘control group’. Pulmonary rehabilitation group underwent 8 weeks pulmonary rehabilitation (PR) along with medical management as per guidelines and the control group was advised only medical management. Results: Mean age in case group was 59.15 ± 10.39 years and in control group was 62.10 ± 14.54 years. The case and the control groups were matched for age and sex. Mean MRC grading at the end of 8 weeks showed significant improvement in the case group as compared to control group (p= 0.011 vs p = 0.655). Similarly, mean St. George Respiratory Questionnaire (SGRQ) score also showed significant improvement in pulmonary rehabilitation group at the end of the study (p= 0.001 vs p= 0.492). However, FEV1 and FVC had no significant change in the case and control group. Similarly, blood gases also did not show any significant difference in the group. Conclusion: Pulmonary rehabilitation improves breathlessness and thereby improves quality of life in the patients suffering from ILD. However, the pulmonary function values and blood gases are unaffected by pulmonary rehabilitation. Clinical Implications: Further large scale multicentre study is needed to ascertain the association.

Keywords: ILD, pulmonary rehabilitation, quality of life, pulmonary functions

Procedia PDF Downloads 243

Authors: Paula C. Ferreira, Ana Margarida Veiga Simão, Nádia Pereira, Aristides Ferreira, Alexandra Marques Pinto, Alexandra Barros, Vitor Martinho

Abstract:

Cyberbullying is a phenomenon of worldwide concern regarding children and adolescents’ mental health and risk behavior. Bystanders of this phenomenon can help diminish the incidence of this phenomenon if they engage in pro-social behavior. However, different social-cognitive and affective bystander reactions may surface because of the lack of contextual information and emotional cues in cyberbullying situations. Hence, this study investigated how cyberbullying bystanders’ unpleasant emotions could be related to their personal moral beliefs and their behavioral intentions to cyberbully or defend the victim. It also proposed to investigate how their normative beliefs of perceived severity about cyberbullying behavior could be related to their personal moral beliefs and their behavioral intentions. Three groups of adolescents participated in this study, namely a first of group 402 students (5th – 12th graders; Mage = 13.12; SD = 2.19; 55.7% girls) to compute explorative factorial analyses of the instruments used; a second group of 676 students (5th – 12th graders; Mage = 14.10; SD = 2.74; 55.5% were boys) to run confirmatory factor analyses; and a third group (N = 397; 5th – 12th graders; Mage = 13.88 years; SD = 1.45; 55.5% girls) to perform the main analyses to test the research hypotheses. Self-report measures were used, such as the Personal moral beliefs about cyberbullying behavior questionnaire, the Normative beliefs of perceived severity about cyberbullying behavior questionnaire, the Unpleasant emotions about cyberbullying incidents questionnaires, and the Bystanders’ behavioral intentions in cyberbullying situations questionnaires. Path analysis results revealed that unpleasant emotions were mediators of the relationship between adolescent cyberbullying bystanders’ personal moral beliefs and their intentions to help the victims in cyberbullying situations. Moreover, adolescent cyberbullying bystanders’ normative beliefs of gravity were mediators of the relationship between their personal moral beliefs and their intentions to cyberbully others. These findings provide insights for the development of prevention and intervention programs that promote social and emotional learning strategies as a means to prevent and intervene in cyberbullying.

Keywords: cyberbullying, normative beliefs of perceived severity, personal moral beliefs, unpleasant emotions

Procedia PDF Downloads 187

Authors: Norhaslina Hassan, Sherina Rezvanipour, Amirhosein Ghaffarian Hoseini, Ng Siew Cheok

Abstract:

The walkability features of urban streets are prominent factors that are often focused on achieving a pedestrian-friendly environment. The limited attention that walkability enhancements devote to pedestrians' experiences or perceptions, on the other hand, raises the question of whether walkability enhancement is sufficient for pedestrians to enjoy using the streets. Thus, this paper evaluates the relationship between the socio-physical components of urban streets and pedestrians’ perceptions. A total of 1152 pedestrians from five urban streets in two major Malaysian cities, Kuala Lumpur, and George Town, Penang, participated in this study. In particular, this study used pedestrian preference scores towards socio-physical attributes that exist in urban streets to assess their impact on pedestrians’ appraisals of street likeability, comfort, and safety. Through analysis, the principal component analysis extracted eight socio-physical components, which were then tested via an ordinal regression model to identify their impact on pedestrian street likeability, comfort (visual, auditory, haptic and olfactory), and safety (physical safety, environmental safety, and security). Furthermore, a non-parametric Kruskal Wallis test was used to identify whether the results were subjected to any socio-demographic differences. The results found that all eight components had some degree of effect on the appraisals. It was also revealed that pedestrians’ preferences towards the attributes as well as their appraisals significantly varied based on their age, gender, ethnicity and education. These results and their implications for urban planning are further discussed in this paper.

Keywords: pedestrian appraisal, pedestrian perception, street sociophysical attributes, walking experience

Procedia PDF Downloads 96

Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

Abstract:

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

Procedia PDF Downloads 451

Authors: Laribi-Habchi Hasiba, Bouanane-Darenfed Amel, Drouiche Nadjib, Pausse André, Mameri Nabil

Abstract:

Chitin, a linear 1, 4-linked N-acetyl-d-glucosamine (GlcNAc) polysaccharide is the major structural component of fungal cell walls, insect exoskeletons and shells of crustaceans. It is one of the most abundant naturally occurring polysaccharides and has attracted tremendous attention in the fields of agriculture, pharmacology and biotechnology. Each year, a vast amount of chitin waste is released from the aquatic food industry, where crustaceans (prawn, crab, Shrimp and lobster) constitute one of the main agricultural products. This creates a serious environmental problem. This linear polymer can be hydrolyzed by bases, acids or enzymes such as chitinase. In this context an extracellular chitinase (ChiA-65) was produced and purified from a newly isolated LHH100. Pure protein was obtained after heat treatment and ammonium sulphate precipitation followed by Sephacryl S-200 chromatography. Based on matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF/MS) analysis, the purified enzyme is a monomer with a molecular mass of 65,195.13 Da. The sequence of the 27 N-terminal residues of the mature ChiA-65 showed high homology with family-18 chitinases. Optimal activity was achieved at pH 4 and 75◦C. Among the inhibitors and metals tested p-chloromercuribenzoic acid, N-ethylmaleimide, Hg2+ and Hg + completelyinhibited enzyme activity. Chitinase activity was high on colloidal chitin, glycol chitin, glycol chitosane, chitotriose and chitooligosaccharide. Chitinase activity towards synthetic substrates in the order of p-NP-(GlcNAc) n (n = 2–4) was p-NP-(GlcNAc)2> p-NP-(GlcNAc)4> p-NP-(GlcNAc)3. Our results suggest that ChiA-65 preferentially hydrolyzed the second glycosidic link from the non-reducing end of (GlcNAc) n. ChiA-65 obeyed Michaelis Menten kinetics the Km and kcat values being 0.385 mg, colloidal chitin/ml and5000 s−1, respectively. ChiA-65 exhibited remarkable biochemical properties suggesting that this enzyme is suitable for bioconversion of chitin waste.

Keywords: Bacillus licheniformis LHH100, characterization, extracellular chitinase, purification

Procedia PDF Downloads 418

Authors: Yanjie Zhu, André Jesus, Irwanda Laory

Abstract:

Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

Procedia PDF Downloads 282

Authors: Vinícius L. L. Morrone, David Douek, Helder M. F. Pereira, Bernadete L. M. Grandolpho

Abstract:

This work aimed to identify the relationships between the level of consumer environmental awareness and their search for sustainable properties, as well as to understand the main sustainability structures considered by these consumers during the decision process. Additionally, the paper looked up to the influence environmental awareness and financial status have over the disposition of buyers to pay more for sustainable properties. To achieve these objectives, 318 questionnaires were answered electronically, after being sent to the Green Building Brazil email basis, as to other Real Estate developers client basis. From all the questionnaires answered, 71 were discarded, leaving a total amount of 247 admitted questionnaires to be analyzed. The responses were evaluated based on the theory of consumer decision making, especially on the influence factors of this process. The data were processed using a PLS model, using the R software. The results have shown that the level of consumer environmental awareness effectively affects the consumer’s will of acquiring a sustainable property or, at least, a property with some environmental friendly structures. The consumer’s environmental awareness also positively impacts the importance consumers give to individual environmental friendly structures. Also, as a consumer value to those individual structures raises, it is also observed a raise in his will to buy a sustainable property. Additionally, the impact of consumer’s environmental awareness and financial status over the willingness to pay more for a property with those attributes. The results indicate that there was no relationship between consumers' environmental awareness and their willingness to pay more for a sustainable property. On the other hand, the financial status and the family income of the consumers showed a positive relation with the willingness to pay more for a sustainable property. This indicates that consumers with better financial conditions, which according to the analysis do not necessarily have a greater environmental awareness, are those who are willing to pay more for a sustainable property. Thus, this study indicates that, even if the environmental awareness impact positively the demand for sustainable structures and properties, this impact is not price reflected, due to the price elasticity of the consumption, especially for a category of lower income consumers. This paper adds to the literature in the way it projects some guidelines to the consumer’s decision process in the Real Estate market in emerging economies, as well as it presents some drivers to pricing decisions.

Keywords: consumer behavior, environmental awareness, real estate pricing, sustainable buildings

Procedia PDF Downloads 163

Authors: Hannah K. M. Tang, Muhammad Ateeq, Mark J. Lake, Badr Abdullah, Frederic A. Bezombes

Abstract:

Based on a theoretical assessment of current literature, virtual reality (VR) could help to treat sporting injuries in a number of ways. However, it is important to obtain rehabilitation specialists’ perspectives in order to design, develop and validate suitable content for a VR application focused on treatment. Subsequently, a one-day observation and interview study focused on the use of VR for the treatment of lower limb musculoskeletal conditions in athletes was conducted at St George’s Park England National Football Centre with rehabilitation specialists. The current paper established the methods suitable for obtaining practitioner’s viewpoints through observation and interview in this context. Particular detail was provided regarding the method of qualitatively processing interview results using the qualitative data analysis software tool NVivo, in order to produce a narrative of overarching themes. The observations and overarching themes identified could be used as a framework and success criteria of a VR application developed in future research. In conclusion, this work explained the methods deemed suitable for obtaining practitioner’s viewpoints through observation and interview. This was required in order to highlight characteristics and features of a VR application designed to treat lower limb musculoskeletal injury of athletes and could be built upon to direct future work.

Keywords: athletes, lower-limb musculoskeletal injury, rehabilitation, return-to-sport, virtual reality

Procedia PDF Downloads 227

Authors: Nelson F. F. Ebecken, Gilberto C. Pereira, Lucio P. de Andrade

Abstract:

Bays, estuaries and coastal ecosystems are some of the most used and threatened natural systems globally. Its deterioration is due to intense and increasing human activities. This paper aims to monitor the socio-ecological in Brazil, model and simulate it through a multilayer network representing a DPSIR structure (Drivers, Pressures, States-Impacts-Responses) considering the concept of Management based on Ecosystems to support decision-making under the National/State/Municipal Coastal Management policy. This approach considers several interferences and can represent a significant advance in several scientific aspects. The main objective of this paper is the coupling of three different types of complex networks, the first being an ecological network, the second a social network, and the third a network of economic activities, in order to model the marine ecosystem. Multilayer networks comprise two or more "layers", which may represent different types of interactions, different communities, different points in time, and so on. The dependency between layers results from processes that affect the various layers. For example, the dispersion of individuals between two patches affects the network structure of both samples. A multilayer network consists of (i) a set of physical nodes representing entities (e.g., species, people, companies); (ii) a set of layers, which may include multiple layering aspects (e.g., time dependency and multiple types of relationships); (iii) a set of state nodes, each of which corresponds to the manifestation of a given physical node in a layer-specific; and (iv) a set of edges (weighted or not) to connect the state nodes among themselves. The edge set includes the intralayer edges familiar and interlayer ones, which connect state nodes between layers. The applied methodology in an existent case uses the Flow cytometry process and the modeling of ecological relationships (trophic and non-trophic) following fuzzy theory concepts and graph visualization. The identification of subnetworks in the fuzzy graphs is carried out using a specific computational method. This methodology allows considering the influence of different factors and helps their contributions to the decision-making process.

Keywords: marine ecosystems, complex systems, multilayer network, ecosystems management

Procedia PDF Downloads 82

Authors: Lefteris Tsipis, Demosthenes Vouyioukas, George Loumos, Antonis Kargas, Dimitris Varoutas

Abstract:

The employment of technology and digitization is crucial for cultural organizations to establish and sustain digital repositories for their cultural heritage artefacts. This utilization is also essential in facilitating the presentation of cultural works and exhibits to a broader audience. Consequently, in this work, we propose a digital repository that functions as Software as a Service (SaaS), primarily promoting the safe storage, display, and sharing of cultural materials, enhancing accessibility, and fostering a deeper understanding and appreciation of cultural heritage. Moreover, the proposed digital repository service is designed as a multitenant architecture, which enables organizations to expand their reach, enhance accessibility, foster collaboration, and ensure the preservation of their content. Specifically, this project aims to assist each cultural institution in organizing its digital cultural assets into collections and feeding other digital platforms, including educational, museum, pedagogical, and games, through appropriate interfaces. Moreover, the creation of this digital repository offers a cutting-edge and effective open-access laboratory solution. It allows organizations to have a significant influence on their audiences by fostering cultural understanding and appreciation. Additionally, it facilitates the connection between different digital repositories and national/European aggregators, promoting collaboration and information sharing. By embracing this solution, cultural institutions can benefit from shared resources and features, such as system updates, backup and recovery services, and data analytics tools, that are provided by the platform.

Keywords: cultural technologies, gaming technologies, web sharing, digital repository

Procedia PDF Downloads 46

Authors: F. Murilo T. Luna, Vanessa F. Oliveira, Alysson Rocha, Expedito J. S. Parente, Andre V. Bueno, Matheus C. M. Farias, Celio L. Cavalcante Jr.

Abstract:

Aviation jetfuel used in aircraft gas-turbine engines is customarily obtained from the kerosene distillation fraction of petroleum (150-275°C). Mineral jetfuel consists of a hydrocarbon mixture containing paraffins, naphthenes and aromatics, with low olefins content. In order to ensure their safety, several stringent requirements must be met by jetfuels, such as: high energy density, low risk of explosion, physicochemical stability and low pour point. In this context, aviation fuels eventually obtained from biofeedstocks (which have been coined as ‘biojetfuel’), must be used as ‘drop in’, since adaptations in aircraft engines are not desirable, to avoid problems with their operation reliability. Thus, potential aviation biofuels must present the same composition and physicochemical properties of conventional jetfuel. Among the potential feedtstocks for aviation biofuel, the babaçu oil, extracted from a palm tree extensively found in some regions of Brazil, contains expressive quantities of short chain saturated fatty acids and may be an interesting choice for biojetfuel production. In this study, biojetfuel was synthesized through homogeneous transesterification of babaçu oil using methanol and its properties were compared with petroleum-based jetfuel through measurements of oxidative stability, physicochemical properties and low temperature properties. The transesterification reactions were carried out using methanol and after decantation/wash procedures, the methyl esters were purified by molecular distillation under high vacuum at different temperatures. The results indicate significant improvement in oxidative stability and pour point of the products when compared to the fresh oil. After optimization of operational conditions, potential biojetfuel samples were obtained, consisting mainly of C8 esters, showing low pour point and high oxidative stability. Jet engine tests are being conducted in an automated test bed equipped with pollutant emissions analysers to study the operational performance of the biojetfuel that was obtained and compare with a mineral commercial jetfuel.

Keywords: biojetfuel, babaçu oil, oxidative stability, engine tests

Procedia PDF Downloads 235

Authors: Klytaimnistra Katsara, George Kenanakis, Eleftherios Alissandrakis, Vassilis M. Papadakis

Abstract:

In recent decades, microplastics (MPs) attracted the interest of the research community as the level of environmental plastic pollution has increased over the years. Through air inhalation and food consumption, MPs enter the human body, creating a series of possible health issues. The majority of MPs enter through the digestive tract; they migrate from the plastic packaging of the foodstuffs. Several plastics, such as Polyethylene (PE), are commonly used as food packaging material due to their preservation and storage capabilities. In this work, the surfaces of three different cured meat products with varied fat compositions were studied (bacon, mortadella, and salami) to determine the migration of MPs from plastic packaging. Micro-Raman spectroscopic measurements were performed in an experimental set lasting 28 days, where the meat samples were stored in vacuum-sealed low-density polyethylene (LDPE) pouches under refrigeration conditions at 4°C. Specific measurement days (0, 3, 9, 12, 15, and 28 days of storage) were chosen to obtain comparative results. Raman micro-spectroscopy was used to monitor the MPs migration, where the Raman spectral profile of LDPE first appeared on day 9 in Bacon, day 15 in Salami, and finally, on day 28 in Mortadella. All the meat samples on day 28 were tainted because a layer of bacterial outgrowth had developed on their surface. In conclusion, MP migration from food packaging to the surface of the cured meat samples was proven. To minimize the consumption of MPs in cured meat products that are stored in plastic packaging, a short period of storage time under refrigeration conditions is advised.

Keywords: cured meat, food packaging, low-density polyethylene, microplastic migration, micro-Raman spectroscopy

Procedia PDF Downloads 47

Authors: Ana Elisa Ribeiro Costa, Sónia Daniela Ferreira Miranda, João Pedro De Carvalho Pereira, João Carlos Simões Bernardo

Abstract:

Elastomeric thermoplastics (TPEs) have been widely used in the footwear industry over the years. Recently this industry has been requesting materials that can combine lightweight and high abrasion resistance. Although there are blowing agents on the market to improve the lightweight, when these are incorporated into molten polymers during the extrusion or injection molding, it is necessary to have some specific processing conditions (e.g. effect of temperature and hydrodynamic stresses) to obtain good properties and acceptable surface appearance on the final products. Therefore, it is a great advantage for the compounder industry to acquire compounds that already include the blowing agents. In this way, they can be handled and processed under the same conditions as a conventional raw material. In this work, the expandable TPEs compounds, namely a TPU and a SEBS, with the incorporation of blowing agents, have been developed through a co-rotating modular twin-screw parallel extruder. Different blowing agents such as thermo-expandable microspheres and an azodicarbonamide were selected and different screw configurations and temperature profiles were evaluated since these parameters have a particular influence on the expansion inhibition of the blowing agents. Furthermore, percentages of incorporation were varied in order to investigate their influence on the final product properties. After the extrusion of these compounds, expansion was tested by the injection process. The mechanical and physical properties were characterized by different analytical methods like tensile, flexural and abrasive tests, determination of hardness and density measurement. Also, scanning electron microscopy (SEM) was performed. It was observed that it is possible to incorporate the blowing agents on the TPEs without their expansion on the extrusion process. Only with reprocessing (injection molding) did the expansion of the agents occur. These results are corroborated by SEM micrographs, which show a good distribution of blowing agents in the polymeric matrices. The other experimental results showed a good mechanical performance and its density decrease (30% for SEBS and 35% for TPU). This study suggested that it is possible to develop optimized compounds for footwear applications (e.g., sole shoes), which only will be able to expand during the injection process.

Keywords: blowing agents, expandable thermoplastic elastomeric compounds, low density, footwear applications

Procedia PDF Downloads 173

Authors: George Mahalu

Abstract:

The Chua diode has been configured over time in various ways, using electronic structures like operational amplifiers (AOs) or devices with gas or semiconductors. When discussing the use of semiconductor devices, tunnel diodes (Esaki diodes) are most often considered, and more recently, transistorized configurations such as lambda diodes. The paperwork proposed here uses in the modeling a lambda diode type configuration consisting of two junction field effect transistors (JFET). The original scheme is created in the MULTISIM electronic simulation environment and is analyzed in order to identify the conditions for the appearance of evolutionary unpredictability specific to nonlinear dynamic systems with chaos-induced behavior. The chaotic deterministic oscillator is one autonomous type, a fact that places it in the class of Chua’s type oscillators, the only significant and most important difference being the presence of a nonlinear device like the one mentioned structure above. The chaotic behavior is identified both by means of strange attractor-type trajectories and visible during the simulation and by highlighting the hypersensitivity of the system to small variations of one of the input parameters. The results obtained through simulation and the conclusions drawn are useful in the further research of ways to implement such constructive electronic solutions in theoretical and practical applications related to modern small signal amplification structures, to systems for encoding and decoding messages through various modern ways of communication, as well as new structures that can be imagined both in modern neural networks and in those for the physical implementation of some requirements imposed by current research with the aim of obtaining practically usable solutions in quantum computing and quantum computers.

Keywords: chua, diode, memristor, chaos

Procedia PDF Downloads 62

Authors: Kyra Kannen, Sonja D. Roelen, Sebastian Schnieder, Jarek Krajewski, Steffen Holsteg, André Karger, Johanna Askeridis, Celina Slawik, Philip Mildner, Jens Piesk, Ruslan David, Holger Kürten, Benjamin Oster, Robert Malzan, Mike Ludemann

Abstract:

Virtual Reality (VR) is increasingly recognized for its potential in transforming mental disorder treatment, offering advantages such as cost-effectiveness, time efficiency, accessibility, reduced stigma, and scalability. While the application of VR in the context of anxiety disorders has been extensively evaluated and demonstrated to be effective, the utilization of VR as a therapeutic treatment for depression remains under-investigated. Our goal is to pioneer immersive VR therapy modules for treating major depression, alongside a web-based system for home use. We develop a modular digital therapy platform grounded in psychodynamic therapy interventions which addresses stress reduction, exploration of social situations and relationship support, social skill training, avoidance behavior analysis, and psychoeducation. In addition, an automated depression monitoring system, based on acoustic voice analysis, is implemented in the form of a speech-based diary to track the affective state of the user and depression severity. The use of immersive VR facilitates patient immersion into complex and realistic interpersonal interactions with high emotional engagement, which may contribute to positive treatment acceptance and satisfaction. In a proof-of-concept study, 45 depressed patients were assigned to VR or web-platform modules, evaluating user experience, usability and additional metrics including depression severity, mindfulness, interpersonal problems, and treatment satisfaction. The findings provide valuable insights into the effectiveness and user-friendliness of VR and web modules for depression therapy and contribute to the refinement of more tailored digital interventions to improve mental health.

Keywords: virtual reality therapy, digital health, depression, psychotherapy

Procedia PDF Downloads 33

Authors: Dhouibi Mohamed, Stirbu Bogdan, Chabotier André, Pirlot Marc

Abstract:

Effects of erosion and wear on the performance of small caliber guns have been analyzed throughout numerical and experimental studies. Mainly, qualitative observations were performed. Correlations between the volume change of the chamber and the maximum pressure are limited. This paper focuses on the development of a numerical model to predict the maximum pressure evolution when the interior shape of the chamber changes in the different weapon’s life phases. To fulfill this goal, an experimental campaign, followed by a numerical simulation study, is carried out. Two test barrels, « 5.56x45mm NATO » and « 7.62x51mm NATO,» are considered. First, a Coordinate Measuring Machine (CMM) with a contact scanning probe is used to measure the interior profile of the barrels after each 300-shots cycle until their worn out. Simultaneously, the EPVAT (Electronic Pressure Velocity and Action Time) method with a special WEIBEL radar are used to measure: (i) the chamber pressure, (ii) the action time, (iii) and the bullet velocity in each barrel. Second, a numerical simulation study is carried out. Thus, a coupled interior ballistic model is developed using the dynamic finite element program LS-DYNA. In this work, two different models are elaborated: (i) coupled Eularien Lagrangian method using fluid-structure interaction (FSI) techniques and a coupled thermo-mechanical finite element using a lumped parameter model (LPM) as a subroutine. Those numerical models are validated and checked through three experimental results, such as (i) the muzzle velocity, (ii) the chamber pressure, and (iii) the surface morphology of fired projectiles. Results show a good agreement between experiments and numerical simulations. Next, a comparison between the two models is conducted. The projectile motions, the dynamic engraving resistances and the maximum pressures are compared and analyzed. Finally, using this obtained database, a statistical correlation between the muzzle velocity, the maximum pressure and the chamber volume is established.

Keywords: engraving process, finite element analysis, gun barrel erosion, interior ballistics, statistical correlation

Procedia PDF Downloads 187

Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

Abstract:

Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

Procedia PDF Downloads 77

Authors: Ola Jon Mork, Lars Andre Giske, Emil Bjørlykhaug

Abstract:

Stabilization of critical processes in order to have the right quality of the products, more efficient production and smoother flow is a key issue in lean production. This paper presents how simulation of key welding processes can stabilize complicated welding processes in small scale production, and how simulation can impact the entire production concept seen from the perspective of lean production. First, a field study was made to learn the production processes in the factory, and subsequently the field study was transformed into a value stream map to get insight into each operation, the quality issues, operation times, lead times and flow of materials. Valuable practical knowledge of how the welding operations were done by operators, appropriate tools and jigs, and type of robots that could be used, was collected. All available information was then implemented into a simulation environment for further elaboration and development. Three researchers, the management of the company and skilled operators at the work floor where working on the project over a period of eight months, and a detailed description of the process was made by the researchers. The simulation showed that simulation could solve a number of technical challenges, the robot program can be tuned in off line mode, and the design and testing of the robot cell could be made in the simulator. Further on the design of the product could be optimized for robot welding and the jigs could be designed and tested in simulation environment. This means that a key issue of lean production can be solved; the welding operation will work with almost 100% performance when it is put into real production. Stabilizing of one key process is critical to gain control of the entire value chain, then a Takt Time can be established and the focus can be directed towards the next process in the production which should be stabilized. Results show that industrial parameters like welding time, welding cost and welding quality can be defined on the simulation stage. Further on, this gives valuable information for calculation of the factories business performance, like manufacturing volume and manufacturing efficiency. Industrial impact from simulation is more efficient implementation of lean manufacturing, since the welding process can be stabilized. More research should be done to gain more knowledge about simulation as a tool for implementation of lean, especially where there complex processes.

Keywords: simulation, lean, stabilization, welding process

Procedia PDF Downloads 298

Authors: Kiran Nair, Jincy George, Suhaib Anagreh

Abstract:

Artificial intelligence (AI) in a marketing context is a form of a deterministic tool designed to optimize and enhance marketing tasks, research tools, and techniques. It is on the verge of transforming marketing roles and revolutionize the entire industry. This paper aims to explore the current dissemination of the application of artificial intelligence (AI) in the marketing mix, reviewing the scope and application of AI in various aspects of sales and distribution management. The paper also aims at identifying the areas of the strong impact of AI in factors of sales and distribution management such as distribution channel, purchase automation, customer service, merchandising automation, and shopping experiences. This is a qualitative research paper that aims to examine the impact of AI on sales and distribution management of 30 multinational brands in six different industries, namely: airline; automobile; banking and insurance; education; information technology; retail and telecom. Primary data is collected by means of interviews and questionnaires from a sample of 100 marketing managers that have been selected using convenient sampling method. The data is then analyzed using descriptive statistics, correlation analysis and multiple regression analysis. The study reveals that AI applications are extensively used in sales and distribution management, with a strong impact on various factors such as identifying new distribution channels, automation in merchandising, customer service, and purchase automation as well as sales processes. International brands have already integrated AI extensively in their day-to-day operations for better efficiency and improved market share while others are investing heavily in new AI applications for gaining competitive advantage.

Keywords: artificial intelligence, sales and distribution, marketing mix, distribution channel, customer service

Procedia PDF Downloads 127

Authors: Mohamed Dhouibi, Bogdan Stirbu, Chabotier André, Marc Pirlot

Abstract:

Internal barrel wear is a major factor affecting the performance of small caliber guns in their different life phases. Wear analysis is, therefore, a very important process for understanding how wear occurs, where it takes place, and how it spreads with the aim on improving the accuracy and effectiveness of small caliber weapons. This paper discusses the measurement and analysis of combustion chamber wear for a small-caliber gun using a Coordinate Measuring Machine (CMM). Initially, two different NATO small caliber guns: 5.56x45mm and 7.62x51mm, are considered. A Micura Zeiss Coordinate Measuring Machine (CMM) equipped with the VAST XTR gold high-end sensor is used to measure the inner profile of the two guns every 300-shot cycle. The CMM parameters, such us (i) the measuring force, (ii) the measured points, (iii) the time of masking, and (iv) the scanning velocity, are investigated. In order to ensure minimum measurement error, a statistical analysis is adopted to select the reliable CMM parameters combination. Next, two measurement strategies are developed to capture the shape and the volume of each gun chamber. Thus, a task-specific measurement uncertainty (TSMU) analysis is carried out for each measurement plan. Different approaches of TSMU evaluation have been proposed in the literature. This paper discusses two different techniques. The first is the substitution method described in ISO 15530 part 3. This approach is based on the use of calibrated workpieces with similar shape and size as the measured part. The second is the Monte Carlo simulation method presented in ISO 15530 part 4. Uncertainty evaluation software (UES), also known as the Virtual Coordinate Measuring Machine (VCMM), is utilized in this technique to perform a point-by-point simulation of the measurements. To conclude, a comparison between both approaches is performed. Finally, the results of the measurements are verified through calibrated gauges of several dimensions specially designed for the two barrels. On this basis, an experimental database is developed for further analysis aiming to quantify the relationship between the volume of wear and the muzzle velocity of small caliber guns.

Keywords: coordinate measuring machine, measurement uncertainty, erosion and wear volume, small caliber guns

Procedia PDF Downloads 123

Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

Abstract:

Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

Procedia PDF Downloads 139

Authors: Joseph George, Anne Kotteswara Roa

Abstract:

Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

Procedia PDF Downloads 100

Authors: Matthew F. Gibson, Narasimha D. Rao, Raphael B. Slade, Joana Portugal Pereira, Joeri Rogelj

Abstract:

Globally, India’s population is among the most severely impacted by nutrient deficiency, yet millions of tonnes of food are lost before reaching consumers. Across food groups, grains represent the largest share of daily calories and overall losses by mass in India. If current losses remain unresolved and follow projected population rates, we estimate, by 2030, losses from grains for human consumption could increase by 1.3-1.8 million tonnes (Mt) per year against current levels of ~10 Mt per year. This study quantifies energy input to minimise storage losses across India, responsible for a quarter of grain supply chain losses. In doing so, we identify and explore a Sustainable Development Goal (SDG) triplet between SDG₂, SDG₇, and SDG₁₂ and provide insight for development of joined up agriculture and health policy in the country. Analyzing rice, wheat, maize, bajra, and sorghum, we quantify one route to reduce losses in supply chains, by modelling the energy input to maintain favorable climatic conditions in modern silo storage. We quantify key nutrients (calories, protein, zinc, iron, vitamin A) contained within these losses and calculate roughly how much deficiency in these dietary components could be reduced if grain losses were eliminated. Our modelling indicates, with appropriate uncertainty, maize has the highest energy input intensity for storage, at 110 kWh per tonne of grain (kWh/t), and wheat the lowest (72 kWh/t). This energy trade-off represents 8%-16% of the energy input required in grain production. We estimate if grain losses across the supply chain were saved and targeted to India’s nutritionally deficient population, average protein deficiency could reduce by 46%, calorie by 27%, zinc by 26%, and iron by 11%. This study offers insight for development of Indian agriculture, food, and health policy by first quantifying and then presenting benefits and trade-offs of tackling food grain losses.

Keywords: energy, food loss, grain storage, hunger, India, sustainable development goal, SDG

Procedia PDF Downloads 106

Authors: Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

Abstract:

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand the smallholder farming communities within Scotland by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted in conjunction with text mining of the data in search of common themes, words, and topics found within the text. Results from bi-grams and topic modelling uncover four main topics of interest within the data pertaining to aspects of livestock husbandry: feeding, breeding, slaughter, and disposal. These topics were found amongst both the poultry and pig sub-forums. Topic modeling appears to be a useful method of unsupervised classification regarding this form of data, as it has produced clusters that relate to biosecurity and animal welfare. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter and Facebook/Meta, in addition to time series analysis to highlight temporal patterns.

Keywords: veterinary epidemiology, disease surveillance, infodemiology, infoveillance, smallholding, social media, web scraping, sentiment analysis, geolocation, text mining, NLP

Procedia PDF Downloads 71

Authors: Christiana Ene Ogwuche, Joseph Amupitan, George Ndukwe, Rachael Ayo

Abstract:

Maesobotrya barteri (Baill), belonging to the family Euphorbiaceae, is a medicinal plant growing widely in tropical Africa. The Aerial plant parts of Maesobotrya barteri (Baill) were collected fresh from Orokam, Ogbadibo local Government of Benue State, Nigeria in July 2013. Taxonomical identification was done by Mallam Musa Abdullahi at the Herbarium unit of Biological Sciences Department, ABU, Zaria, Nigeria. Pulverized aerial parts of Maesobotrya barteri (960g) was exhaustively extracted successively using petroleum ether, chloroform, ethyl acetate and methanol and concentrated in the rotary evaporator at 40°C. The Petroleum ether extract had the second highest activity against test microbes from preliminary crude microbial screenings. The Petroleum ether extract was subjected to phytochemical studies, antimicrobial analysis and column chromatography (CC). The column chromatography yielded fraction PE, which was further purified using preparative thin layer chromatography to give PE1. The structure of the isolated compound was established using 1-D NMR and 2-D NMR spectroscopic analysis and by direct comparison with data reported in literature was confirmed to be a mixture, an isomer of Betulinic acid and Oleanolic acid, both with the molecular weight (C₃₀H₄₈O₃). The bioactivity of this compound was carried out using some clinical pathogens and the activity compared with standard drugs, and this was found to be comparable with the standard drug.

Keywords: Maesobotrya barteri, medicinal plant, bioactivity, petroleum spirit extract, butellinic acid, oleanilic acid

Procedia PDF Downloads 170

Authors: Kimiyo Shimomai, Mihoko Harada

Abstract:

This study is a literature review of the psychological reactions that occur in end-of-life cancer patients who are nearing death. It searched electronic databases and selected literature related to psychological studies of end-of-life patients. There was no limit on the search period, and the search was conducted until the second week of December 2021. The keywords were specified as “death and dying”, “terminal illness”, “end-of-life”, “palliative care”, “psycho-oncology” and “research”. These literatures referred to Holly (2017): Comprehensive Systematic Review for Advanced Practice Nursing, P268 Figure 10.3 to ensure quality. These literatures were selected with a dissertation score of 4 or 5. The review was conducted in two stages with reference to the procedure of George (2002). First, these references were searched for keywords in the database, and then relevant references were selected from the psychology and nursing studies of end-of-life patients. The number of literatures analyzed was 76 for overseas and 17 for domestic. As for the independent variables, "physical variable" was the most common in 36 literatures (66.7%), followed by "psychological variable" in 35 literatures (64.8%), "spiritual variable" in 21 literatures (38%), and "social variable" in 17 literatures. (31.5%), "Variables related to medical care / treatment" were 16 literatures (29.6%). To summarize the relationship between these independent variables and the dependent variable, when the dependent variable is "psychological variable", the independent variables are "psychological variable", "social variable", and "physical variable". Among the independent variables, the physical variables were the most common. The psychological responses that occur in end-stage cancer patients who are nearing death are mutually influenced by psychological, social, and physical variables. Therefore, it supported the "total pain" advocated by Cicely Saunders.

Keywords: cancer patient, end-of-life, literature review, psychological process

Procedia PDF Downloads 102