Search results for: closed loop identification

Authors: M. Ramadan, B. Salah

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This paper introduces a framework to digitalize lean manufacturing tools to enhance smart lean-based manufacturing environments or Lean 4.0 manufacturing systems. The paper discusses the integration between lean tools and the powerful features of recent real-time data capturing systems with the help of Information and Communication Technologies (ICT) to develop an intelligent real-time monitoring and controlling system of production operations concerning lean targets. This integration is represented in the Lean 4.0 system called Dynamic Value Stream Mapping (DVSM). Moreover, the paper introduces the practice of Radio Frequency Identification (RFID) and ICT to smartly support lean tools and practices during daily production runs to keep the lean system alive and effective. This work introduces a practical description of how the lean method tools 5S, standardized work, and poka-yoke can be digitalized and smartly monitored and controlled through DVSM. A framework of the three tools has been discussed and put into practice in a German switchgear manufacturer.

Keywords: lean manufacturing, Industry 4.0, radio frequency identification, value stream mapping

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Authors: Bo-Huei Huang, Chih-Hsun Yang, Meng-Tsan Tsai

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Optical coherence tomography (OCT) enables to provide advantages of noninvasive imaging, high resolution, and high imaging speed. In this study, we integrated OCT and a CW laser for tumor diagnosis and treatment. The axial and transverse resolutions of the developed OCT system are 3 μm and 1 μm, respectively. The frame rate of OCT system is 30 frames/s. In this study, the tumor cells were implanted into the mice skin and scanned by OCT to observe the morphological and angiographic changes. With OCT imaging, 3D microstructures and skin angiography of mice skin can be simultaneously acquired, which can be utilized for identification of the tumor distribution. Then, the CW laser beam can be accurately controlled to expose on the center of the tumor, according to the OCT results. Moreover, OCT was used to monitor the induced photothermolysis and to evaluate the treatment outcome. The results showed that OCT-guided laser therapy could efficiently improve the treatment outcome and the extra damage induced by CW can be greatly reduced. Such OCT-guided laser therapy system could be a potential tool for dermatological applications.

Keywords: optical coherence tomography, laser therapy, skin tumor, position guide

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Authors: Stephanie Chen, Mitja Echim, Christof Büskens

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To describe and propagate the behavior of a system mathematical models are formulated. Parameter identification is used to adapt the coefficients of the underlying laws of science. For complex systems this approach can be incomplete and hence imprecise and moreover too slow to be computed efficiently. Therefore, these models might be not applicable for the numerical optimization of real systems, since these techniques require numerous evaluations of the models. Moreover not all quantities necessary for the identification might be available and hence the system must be adapted manually. Therefore, an approach is described that generates models that overcome the before mentioned limitations by not focusing on physical laws, but on measured (sensor) data of real systems. The approach is more general since it generates models for every system detached from the scientific background. Additionally, this approach can be used in a more general sense, since it is able to automatically identify correlations in the data. The method can be classified as a multivariate data regression analysis. In contrast to many other data regression methods this variant is also able to identify correlations of products of variables and not only of single variables. This enables a far more precise and better representation of causal correlations. The basis and the explanation of this method come from an analytical background: the series expansion. Another advantage of this technique is the possibility of real-time adaptation of the generated models during operation. Herewith system changes due to aging, wear or perturbations from the environment can be taken into account, which is indispensable for realistic scenarios. Since these data driven models can be evaluated very efficiently and with high precision, they can be used in mathematical optimization algorithms that minimize a cost function, e.g. time, energy consumption, operational costs or a mixture of them, subject to additional constraints. The proposed method has successfully been tested in several complex applications and with strong industrial requirements. The generated models were able to simulate the given systems with an error in precision less than one percent. Moreover the automatic identification of the correlations was able to discover so far unknown relationships. To summarize the above mentioned approach is able to efficiently compute high precise and real-time-adaptive data-based models in different fields of industry. Combined with an effective mathematical optimization algorithm like WORHP (We Optimize Really Huge Problems) several complex systems can now be represented by a high precision model to be optimized within the user wishes. The proposed methods will be illustrated with different examples.

Keywords: adaptive modeling, automatic identification of correlations, data based modeling, optimization

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Authors: Waheeda Jabbar, Ali Gul, Rida Noor, Sania Kurd, Saba Gulzar

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Hundreds of miners die from mining accidents each year due to poisonous gases found underground mining areas. This paper proposed an idea to protect the precious lives of mining workers. A supervising system is designed which is based on ZigBee wireless technique along with the smart protective helmets to detect real-time surveillance and it gives early warnings on presence of different poisonous gases in order to save mineworkers from any danger caused by these poisonous gases. A wireless sensor network is established using ZigBee wireless technique by integrating sensors on the helmet, apart from this helmet have embedded heartbeat sensor to detect the pulse rate and be aware of the physical or mental strength of a mineworker to increase the potential safety. Radio frequency identification (RFID) technology is used to find the location of workers. A ZigBee based base station is set-upped to control the communication. The idea is implemented and results are verified through experiment.

Keywords: Arduino, gas sensor (MQ7), RFID, wireless ZigBee

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Authors: Rusudan Kinkladze, Guguli Kurashvili, Ketevan Chitaladze

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Foreign direct investment is a driving force in the development of the interdependent national economies, and the study and analysis of investments is an urgent problem. It is particularly important for transitional economies, such as Georgia, and the study and analysis of investments is an urgent problem. Consequently, the goal of the research is the study and analysis of direct foreign investments in Georgia, and identification and forecasting of modern trends, and covers the period of 2006-2015. The study uses the methods of statistical observation, grouping and analysis, the methods of analytical indicators of time series, trend identification and the predicted values are calculated, as well as various literary and Internet sources relevant to the research. The findings showed that modern investment policy In Georgia is favorable for domestic as well as foreign investors. Georgia is still a net importer of investments. In 2015, the top 10 investing countries was led by Azerbaijan, United Kingdom and Netherlands, and the largest share of FDIs were allocated in the transport and communication sector; the financial sector was the second, followed by the health and social work sector, and the same trend will continue in the future. 

Keywords: foreign direct investments, methods, statistics, analysis

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Authors: Jiuyao Jiang, Jiahao Liu, Jichao Li, Kewei Yang, Minghao Li, Bingfeng Ge

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Modern warfare has transitioned from the paradigm of isolated combat forces to system-to-system confrontations due to advancements in combat technologies and application concepts. A combat system-of-systems (CSoS) is a combat network composed of independently operating entities that interact with one another to provide overall operational capabilities. Enhancing the resilience of CSoS is garnering increasing attention due to its significant practical value in optimizing network architectures, improving network security and refining operational planning. Accordingly, a unified framework called CSoS space-time resilience enhancement (CSoS-STRE) has been proposed, which enhances the resilience of CSoS by incorporating spatial features. Firstly, a multilayer spatial combat network model has been constructed, which incorporates an information layer depicting the interrelations among combat entities based on the OODA loop, along with a spatial layer that considers the spatial characteristics of equipment entities, thereby accurately reflecting the actual combat process. Secondly, building upon the combat network model, a spatiotemporal resilience optimization model is proposed, which reformulates the resilience optimization problem as a classical linear optimization model with spatial features. Furthermore, the model is extended from scenarios without obstacles to those with obstacles, thereby further emphasizing the importance of spatial characteristics. Thirdly, a resilience-oriented recovery optimization method based on improved non dominated sorting genetic algorithm II (R-INSGA) is proposed to determine the optimal recovery sequence for the damaged entities. This method not only considers spatial features but also provides the optimal travel path for multiple recovery teams. Finally, the feasibility, effectiveness, and superiority of the CSoS-STRE are demonstrated through a case study. Simultaneously, under deliberate attack conditions based on degree centrality and maximum operational loop performance, the proposed CSoS-STRE method is compared with six baseline recovery strategies, which are based on performance, time, degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The comparison demonstrates that CSoS-STRE achieves faster convergence and superior performance.

Keywords: space-time resilience enhancement, resilience optimization model, combat system-of-systems, recovery optimization method, no-obstacles and obstacles

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Authors: Zahra Safaeian, Shila Goldasteh, Rouhollah Radjabi

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Flowerflies of Syrphidae family is one of the largest families among the Diptera order that due to predatory habit of some species in larva stage has an important role for controlling aphids of the fields. In the present study, flowerflies fauna in the alfalfa fields of the north of Khouzestan were studied during 2012-2013. The species of the family were collected using appropriate methods including insect collecting sweeping net and Malaise traps. According to the fact that the shape of male genitalia in the male insect is important in identification of these species the male genitalia was separated from the body and microscopical slide was prepared then species identification was done considering the male genitalia, the patterns and figures on the abdomen and using available keys. Based on the finding four species of Sphaerophoria scripta, Sphaerophoria turkmenica, Melanostoma mellinu, Sphaerophoria ruppelli were collected and according to the abundance frequency of the collected species the most abundance was related to Sphaerophoria scripta, then Sphaerophoria turkmenica had the most abundance and the least abundance was related to Sphaerophoria ruppelli.

Keywords: syrphidae, fauna, alfalfa, Iran

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Authors: Michael Josef Schwerer

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Background: The identification of unknown victims from mass disasters, violent crimes, or terrorist attacks is frequently facilitated through information from missing persons lists, portrait photos, old or recent pictures showing unique characteristics of a person such as scars or tattoos, or simply reference samples from blood relatives for DNA analysis. In contrast, the identification or at least the characterization of an unknown perpetrator from criminal or terrorist actions remains challenging, particularly in the absence of material or data for comparison, such as fingerprints, which had been previously stored in criminal records. In scenarios that result in high levels of destruction of the perpetrator’s corpse, for instance, blast or fire events, the chance for a positive identification using standard techniques is further impaired. Objectives: This study shows the forensic genetic procedures in the Legal Medicine Service of the German Air Force for the identification of unknown individuals, including such cases in which reference samples are not available. Scenarios requiring such efforts predominantly involve aircraft crash investigations, which are routinely carried out by the German Air Force Centre of Aerospace Medicine as one of the Institution’s essential missions. Further, casework by military police or military intelligence is supported based on administrative cooperation. In the talk, data from study projects, as well as examples from real casework, will be demonstrated and discussed with the audience. Methods: Forensic genetic identification in our laboratories involves the analysis of Short Tandem Repeats and Single Nucleotide Polymorphisms in nuclear DNA along with mitochondrial DNA haplotyping. Extended DNA analysis involves phenotypic markers for skin, hair, and eye color together with the investigation of a person’s biogeographic ancestry. Assessment of the biological age of an individual employs CpG-island methylation analysis using bisulfite-converted DNA. Forensic Investigative Genealogy assessment allows the detection of an unknown person’s blood relatives in reference databases. Technically, end-point-PCR, real-time PCR, capillary electrophoresis, pyrosequencing as well as next generation sequencing using flow-cell-based and chip-based systems are used. Results and Discussion: Optimization of DNA extraction from various sources, including difficult matrixes like formalin-fixed, paraffin-embedded tissues, degraded specimens from decomposed bodies or from decedents exposed to blast or fire events, provides soil for successful PCR amplification and subsequent genetic profiling. For cases with extremely low yields of extracted DNA, whole genome preamplification protocols are successfully used, particularly regarding genetic phenotyping. Improved primer design for CpG-methylation analysis, together with validated sampling strategies for the analyzed substrates from, e.g., lymphocyte-rich organs, allows successful biological age estimation even in bodies with highly degraded tissue material. Conclusions: Successful identification of unknown individuals or at least their phenotypic characterization using pigmentation markers together with age-informative methylation profiles, possibly supplemented by family tree search employing Forensic Investigative Genealogy, can be provided in specialized laboratories. However, standard laboratory procedures must be adapted to work with difficult and highly degraded sample materials.

Keywords: identification, forensic genetics, phenotypic markers, CPG methylation, biological age estimation, forensic investigative genealogy

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Authors: Raef Cherif, Houssine Bakkali, Wafaa El Khatiri, Yacine Yaddaden

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The analysis of how vibrations are transmitted between components is required in many engineering applications. Transfer path analysis (TPA) has been a valuable engineering tool for solving Noise, Vibration, and Harshness (NVH problems using sub-structuring applications. The most challenging part of a TPA analysis is estimating the equivalent forces at the contact points between the active and the passive side. Component TPA in situ Method calculates these forces by inverting the frequency response functions (FRFs) measured at the passive subsystem, relating the motion at indicator points to forces at the interface. However, matrix inversion could pose problems due to the ill-conditioning of the matrices leading to inaccurate results. This paper establishes a TPA model for an academic system consisting of two plates linked by four springs. A numerical study has been performed to improve the interface forces identification. Several parameters are studied and discussed, such as the singular value rejection and the number and position of indicator points chosen and used in the inversion matrix.

Keywords: transfer path analysis, matrix inverse method, indicator points, SVD decomposition

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Authors: Bin-Wen Lan, Ying-Xin Chen, Hsueh-Cheng Yang

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Liquefied petroleum gas is a fuel that has high octane number and low carbon number. This paper uses MSC-51 controller to investigate the effect of liquefied petroleum gas (LPG) on exhaust emissions for different engine speeds in a single cylinder, four-stroke and spark ignition engine. The results indicate that CO, CO2 and NOX exhaust emissions are lower with the use of LPG compared to the use of unleaded gasoline by using the developed controller. The open-loop in the LPG injection system was controlled by MCS-51 single chip. The results show that if a SI engine is operated with LPG fuel rather than gasoline fuel under the same conditions, significant reduction in exhaust emissions can be achieved. In summary, LPG has positive effects on main exhaust emissions such as CO, CO2 and NOX.

Keywords: LPG, control circuit, emission, MCS-51

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Dipak Sonawane, Sudarshan Daga, Somesh Gupta

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Quantitative risk assessment (QRA) is a very precise and consistent approach to defining the likelihood, consequence and severity of a major incident/accident. A variety of hazardous cargoes in bulk, such as hydrocarbons and flammable/toxic chemicals, are handled at various ports. It is well known that most of the operations are hazardous, having the potential of damaging property, causing injury/loss of life and, in some cases, the threat of environmental damage. In order to ensure adequate safety towards life, environment and property, the application of scientific methods such as QRA is inevitable. By means of these methods, comprehensive hazard identification, risk assessment and appropriate implementation of Risk Control measures can be carried out. In this paper, the authors, based on their extensive experience in Risk Analysis for ports and harbors, have exhibited how QRA can be used in practice to minimize and contain risk to tolerable levels. A specific case involving the operation for unloading of hydrocarbon at a port is presented. The exercise provides confidence that the method of QRA, as proposed by the authors, can be used appropriately for the identification of hazards and risk assessment of Ports and Terminals.

Keywords: quantitative risk assessment, hazard assessment, consequence analysis, individual risk, societal risk

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Authors: David Ugalde, Arturo Castillo, Leopoldo Breschi

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The Tuned Mass Dampers (TMDs) are an effective system for mitigating vibrations in building structures. These dampers have traditionally focused on the protection of high-rise buildings against earthquakes and wind loads. The Camara Chilena de la Construction (CChC) building, built in 2018 in Santiago, Chile, is a 21-story RC wall building equipped with a 150-ton TMD and instrumented with six permanent accelerometers, offering an opportunity to monitor the dynamic response of this damped structure. This paper presents the system identification of the CChC building using power spectral density plots of ambient vibration and two seismic events (5.5 Mw and 6.7 Mw). Linear models of the building with and without the TMD are used to compute the theoretical natural periods through modal analysis and simulate the response of the building through response history analysis. Results show that natural periods obtained from both ambient vibrations and earthquake records are quite similar to the theoretical periods given by the modal analysis of the building model. Some of the experimental periods are noticeable by simple inspection of the earthquake records. The accelerometers in the first story better captured the modes related to the building podium while the upper accelerometers clearly captured the modes related to the tower. The earthquake simulation showed smaller accelerations in the model with TMD that are similar to that measured by the accelerometers. It is concluded that the system identification through power spectral density shows consistency with the expected dynamic properties. The structural health monitoring of the CChC building confirms the advantages of seismic protection technologies such as TMDs in seismic prone areas.

Keywords: system identification, tuned mass damper, wall buildings, seismic protection

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Authors: Karthick Gopalan, Selvamohan Thankiah

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Biosurfactants are lipid compounds produced by microbes, which are amphipathic molecules consisting of hydrophophic and hydrophilic domains. In the present investigation, ten bacterial strains were isolated from petroleum oil contaminated sites near petrol bunk. Oil collapsing test, haemolytic activity were used as a criteria for primary isolation of biosurfactant producing bacteria. In this study, all the bacterial strains gave positive results. Among the ten strains, two were observed as good biosurfactant producers, they utilize the diesel as a sole carbon source. Optimization of biosurfactant producing bacteria isolated from petroleum oil contaminated sites was carried out using different parameters such as, temperature (20ºC, 25ºC, 30ºC, 37ºC and 45ºC), pH (5,6,7,8 & 9) and nitrogen sources (ammonium chloride, ammonium carbonate and sodium nitrate). Biosurfactants produced by bacteria were extracted, dried and quantified. As a result of optimization of parameters the suitable values for the production of more amount of biosurfactant by the isolated bacterial species was observed as 30ºC (0.543 gm/lt) in the pH 7 (0.537 gm/lt) with ammonium nitrate (0.431 gm/lt) as sole carbon source.

Keywords: isolation and identification, biosurfactant, microorganism, bioaugmentation

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Authors: John Onolame Unuofin, Uchechukuw U. Nwodo, Anthony I. Okoh

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Laccase is constantly gaining status as important biocatalyst in biotechnology. The illimitable potential of its industrial applications and the corresponding aggressive need for phenomenal volumes of extracellularly secreted laccases have called for its interminable production from sources which are able to meet this demand within a relatively short period of time, preferably bacteria. In response to this call, this study was designed to source for laccase-producing bacteria from different environmental matrices. Three sampling environments were chosen such as wastewater treatment plants, University of Fort Hare vicinity and the Hogback woodland, all within the Eastern Cape, South Africa. Samples such as effluents, sediments, leaf litters, degrading wood and rock scrapings were selectively enriched with some model aromatic compounds and were further screened qualitatively and quantitatively on five phenolic substrates ABTS (2,2’-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid), Guaiacol, 1-Naphthol, Potassium Ferric Cyanide and Syringaldazine). Basis for selection was their ability to elicit a colour change on at least three of the above mentioned agar based assay substrates. The choice isolates were further identified based on 16S rRNA molecular identification techniques. 33 isolates were screened out of the 40 representative distinct colonies during the qualitative plate screens, while quantitative screens selected out 11 bacterial isolates. They were, based on molecular identification, desginated as members of the genera Pseudomonas, Stenotrophomonas and Citrobacter of the gammaproteobacteria and Bordetalla and Achromobacter of the betaproteobacteria respectively. We therefore conclude based on our outcomes that we may have isolated efficient laccase-producing bacteria, which might be of beneficial significance in catalysis and biotechnology.

Keywords: beta proteobacteria, catalysis, gammaproteobacteria, laccase

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Authors: Chao Yu, Qing-Guo Wang, Dan Zhang

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The outlier detection problem for dynamic systems is formulated as a matrix decomposition problem with low-rank, sparse matrices and further recast as a semidefinite programming (SDP) problem. A fast algorithm is presented to solve the resulting problem while keeping the solution matrix structure and it can greatly reduce the computational cost over the standard interior-point method. The computational burden is further reduced by proper construction of subsets of the raw data without violating low rank property of the involved matrix. The proposed method can make exact detection of outliers in case of no or little noise in output observations. In case of significant noise, a novel approach based on under-sampling with averaging is developed to denoise while retaining the saliency of outliers and so-filtered data enables successful outlier detection with the proposed method while the existing filtering methods fail. Use of recovered “clean” data from the proposed method can give much better parameter estimation compared with that based on the raw data.

Keywords: outlier detection, system identification, matrix decomposition, low-rank matrix, sparsity, semidefinite programming, interior-point methods, denoising

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Authors: Reem Alajmi, Hind Al Harbi, Tahany Ayaad, Zainab Al Musawi

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Hard ticks Hyalomma spp. (family: Ixodidae) are obligate ectoparasite in their all life stages on some domestic animals mainly camels and cattle. Ticks may lead to many economic and public health problems because of their blood feeding behavior. Also, they act as vectors for many bacterial, viral and protozoan agents which may cause serious diseases such as tick-born encephalitis, Rocky-mountain spotted fever, Q-fever and Lyme disease which can affect human and/or animals. In the present study, molecular identification of ticks that attack camels in Riyadh region, Saudi Arabia based on the partial sequence of mitochondrial 16s rRNA gene was applied. Also, the present study aims to detect natural infections of collected camel ticks with Rickessia spp. and Borelia spp. using PCR/hybridization of Citrate synthase encoding gene present in bacterial cells. Hard ticks infesting camels were collected from different camels located in a farm in Riyadh region, Saudi Arabia. Results of the present study showed that the collected specimens belong to two species: Hyalomma dromedari represent 99% of the identified specimens and Hyalomma marginatum which account for 1 % of identified ticks. The molecular identification was made through blasting the obtained sequence of this study with sequences already present and identified in GeneBank. All obtained sequences of H. dromedarii specimens showed 97-100% identity with the same gene sequence of the same species (Accession # L34306.1) which was used as a reference. Meanwhile, no intraspecific variations of H. marginatum mesured because only one specimen was collected. Results also had shown that the intraspecific variability between individuals of H. dromedarii obtained in 92 % of samples ranging from 0.2- 6.6%, while the remaining 7 % of the total samples of H. dromedarii showed about 10.3 % individual differences. However, the interspecific variability between H. dromedarii and H. marginatum was approximately 18.3 %. On the other hand, by using the technique of PCR/hybridization, we could detect natural infection of camel ticks with Rickettsia spp. and Borrelia spp. Results revealed the natural presence of both bacteria in collected ticks. Rickettsial spp. infection present in 29% of collected ticks, while 35% of collected specimen were infected with Borrelia spp. The valuable results obtained from the present study are a new record for the molecular identification of camel ticks in Riyadh, Saudi Arabia and their natural infection with both Rickettsia spp. and Borrelia spp. These results may help scientists to provide a good and direct control strategy of ticks in order to protect one of the most important economic animals which are camels. Also results of this project spotlight on the disease that might be transmitted by ticks to put out a direct protective plan to prevent spreading of these dangerous agents. Further molecular studies are needed to confirm the results of the present study by using other mitochondrial and nuclear genes for tick identification.

Keywords: Camel ticks, Rickessia spp. , Borelia spp. , mitochondrial 16s rRNA gene

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Authors: Arun Kharate, Sarata Kumar Sahu, Susen Kumar Panda, Niranjan Sahoo, H. K. Panda

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In the present study, an attempt has been made for isolation and identification of feline panleukopaenia virus (FPLV) from wild felids of Nandankanan zoo, Odisha, India, along with prevalence study of FPLV. Fecal samples collected from wild felids (26 tigers, 22 lions, 5 leopards, 3 hyenas, 1 jaguar, 2 foxes and 1 wild cat) were subjected to hemagglutinnation test and fluorescent antibody test. In hemagglutinnation test 13 (50%) samples from tiger, 14 (63.63%) samples from lions, 1 (20%) sample from leopards, 1 (50%) from fox, 3 (100%) samples from hyenas and 1 (100%) sample from wild cat were positive. On fluorescent antibody test (FAT), 15 (57.69%) samples from tiger, 18 (81.81%) from lions, 2 (40%) from leopards, 1 (50%) from fox, 3 (100%) from hyenas and 1 (100%) from wild cat were positive. FPLV was isolated using MDBK cell line and preliminary characterization was done on the basis of characteristic cytopathic effect. The virus samples were quantified through titration in MDBK cells. Serological confirmation of FPLV isolates was carried out by HI test, micro-SNT and indirect-ELISA. Physico-chemical characters like pH and temperature resistance along molecular identification using specific FPLV primers was carried out. Seroprevalence study of 36 serum samples employing HI test, micro SNT and indirect-ELISA revealed prevalence of 38.8, 44.4 and 72.2% respectively. During study period an adult tigress and a tiger cub died suspected of feline panleukopenia. The necropsy findings in both animals showed hemorrhagic gastroenteritis. The cytological examination revealed presence of intranuclear inclusion bodies in the intestinal epithelial cells. Spleen, mesenteric lymph node and intestine were positive for feline panleukopenia by FAT. The investigation revealed that feline panleukopenia was prevalent in wild felines of Nandankanan zoo.

Keywords: Feline panleukopenia, fluorescent antibody test, hemagglutination test, indirect-ELISA, Nandankanan zoo

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Authors: Marianna Ciaccia, Gennaro Agrimi, Isabella Pisano, Maurizio Bettiga, Silvia Rapacioli, Giulia Mensa, Monica Marzagalli

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Objective: Untargeted metabolomic analysis of extracellular metabolites is a powerful approach that focuses on comprehensively profiling in the extracellular space. In this study, we applied extracellular metabolomic analysis to investigate the metabolism of two probiotic microorganisms with health benefits that extend far beyond the digestive tract and the immune system. Methods: Analytical techniques employed in extracellular metabolomic analysis encompass various technologies, including mass spectrometry (MS), which enables the identification of metabolites present in the fermentation media, as well as the comparison of metabolic profiles under different experimental conditions. Multivariate statistical analysis techniques like principal component analysis (PCA) or partial least squares-discriminant analysis (PLS-DA) play a crucial role in uncovering metabolic signatures and understanding the dynamics of metabolic networks. Results: Different types of supernatants from fermentation processes, such as dairy-free, not dairy-free media and media with no cells or pasteurized, were subjected to metabolite profiling, which contained a complex mixture of metabolites, including substrates, intermediates, and end-products. This profiling provided insights into the metabolic activity of the microorganisms. The integration of advanced software tools has facilitated the identification and characterization of metabolites in different fermentation conditions and microorganism strains. Conclusions: In conclusion, untargeted extracellular metabolomic analysis, combined with software tools, allowed the study of the metabolites consumed and produced during the fermentation processes of probiotic microorganisms. Ongoing advancements in data analysis methods will further enhance the application of extracellular metabolomic analysis in fermentation research, leading to improved bioproduction and the advancement of sustainable manufacturing processes.

Keywords: biotechnology, metabolomics, lactic bacteria, probiotics, postbiotics

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Authors: Peter Fedichev

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We studied fundamental aspects of aging to develop a mathematical model of gene regulatory network. We show that aging manifests itself as an inherent instability of gene network leading to exponential accumulation of regulatory errors with age. To validate our approach we studied age-dependent omic data such as transcriptomes, metabolomes etc. of different model organisms and humans. We build a computational platform based on our model to identify the targets and biomarkers of aging to design new drugs against aging and age-related diseases. As biomarkers of aging, we choose the rate of aging and the biological age since they completely determine the state of the organism. Since rate of aging rapidly changes in response to an external stress, this kind of biomarker can be useful as a tool for quantitative efficacy assessment of drugs, their combinations, dose optimization, chronic toxicity estimate, personalized therapies selection, clinical endpoints achievement (within clinical research), and death risk assessments. According to our model, we propose a method for targets identification for further interventions against aging and age-related diseases. Being a biotech company, we offer a complete pipeline to develop an anti-aging drug-candidate.

Keywords: aging, longevity, biomarkers, senescence

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Authors: Luis Enrique Bautista-Hinojosa, Luis A. Herrera, Cristian Arriaga-Canon

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Text should be written in the third person. Please avoid using "I" “my” or the pronoun "one". It is best to say "It is believed..." rather than "I believe..." or "One believes...".

Keywords: transcriptomics, co-expression, cancer, biomarkers

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Authors: Shammael Selorfia

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The Quality improvement project aimed to improve junior doctors and nurses’ knowledge and confidence in diagnosing and managing delirium on inpatient surgical wards in a tertiary hospital. The study aimed to develop a standardised assessment and management checklist for all staff working with patients who were presenting with signs of delirium. The aim of the study was to increase confidence of staff at dealing with delirium and improve the quality of referrals that were being sent to the Mental Health Liaison team over a 6-month period. A significant proportion of time was being spent by the Mental Health Liaison triage nurses on referrals for delirium. Data showed 28% of all delirium referrals from surgical teams were being closed at triage reflecting a poor standard of quality of those referrals. A qualitative survey of junior doctors in 6 surgical specialties in a UK tertiary hospital was conducted. These specialties include general surgery, vascular, plastic, urology, neurosurgery, and orthopaedics. The standardised checklist was distributed to all surgical wards. A comparison was made between the Mental health team caseload of delirium before intervention was compared and after. A Qualitative survey at end of 3-month cycle and compare overall caseload on Mental Health Liaison team to pre-QIP data with aim to improve quality of referrals and reduce workload on Mental Health Liaison team. At the end of the project cycle, we demonstrated an improvement in the quality of referrals with a decrease in the percentage of referrals being closed at triage by 8%. Our surveys also indicated an increase in the knowledge of official trust delirium guidelines and confidence at managing the patients. This project highlights that a new approach to delirium using multi-component interventions is needed, where the diagnosis of delirium is shared amongst medical and nursing staff, and everyone plays role in management. The key is improving awareness of delirium and encouraging the use of recognized diagnostic tools and official guidelines. Recommendations were made to the trust on how to implement a long-lasting change.

Keywords: delirium, surgery, quality, improvement

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Authors: Ali Alshumrani, Nathan Clarke, Bogdan Ghite, Stavros Shiaeles

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Digital forensics has become an essential tool in the investigation of cyber and computer-assisted crime. Arguably, given the prevalence of technology and the subsequent digital footprints that exist, it could have a significant role across almost all crimes. However, the variety of technology platforms (such as computers, mobiles, Closed-Circuit Television (CCTV), Internet of Things (IoT), databases, drones, cloud computing services), heterogeneity and volume of data, forensic tool capability, and the investigative cost make investigations both technically challenging and prohibitively expensive. Forensic tools also tend to be siloed into specific technologies, e.g., File System Forensic Analysis Tools (FS-FAT) and Network Forensic Analysis Tools (N-FAT), and a good deal of data sources has little to no specialist forensic tools. Increasingly it also becomes essential to compare and correlate evidence across data sources and to do so in an efficient and effective manner enabling an investigator to answer high-level questions of the data in a timely manner without having to trawl through data and perform the correlation manually. This paper proposes a Unified Forensic Analysis Tool (U-FAT), which aims to establish a common language for electronic information and permit multi-source forensic analysis. Core to this approach is the identification and development of forensic analyses that automate complex data correlations, enabling investigators to investigate cases more efficiently. The paper presents a systematic analysis of major crime categories and identifies what forensic analyses could be used. For example, in a child abduction, an investigation team might have evidence from a range of sources including computing devices (mobile phone, PC), CCTV (potentially a large number), ISP records, and mobile network cell tower data, in addition to third party databases such as the National Sex Offender registry and tax records, with the desire to auto-correlate and across sources and visualize in a cognitively effective manner. U-FAT provides a holistic, flexible, and extensible approach to providing digital forensics in technology, application, and data-agnostic manner, providing powerful and automated forensic analysis.

Keywords: digital forensics, evidence correlation, heterogeneous data, forensics tool

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Authors: Fuyuan Ma, Yuhan Wang, Junhe Zhang, Ying Wang

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Identifying the sources of information dissemination is a pivotal task in the study of collective behaviors in networks, enabling us to discern and intercept the critical pathways through which information propagates from its origins. This allows for the control of the information’s dissemination impact in its early stages. Numerous methods for source detection rely on pre-existing, underlying propagation models as prior knowledge. Current models that eschew prior knowledge attempt to harness label propagation algorithms to model the statistical characteristics of propagation states or employ Graph Neural Networks (GNNs) for deep reverse modeling of the diffusion process. These approaches are either deficient in modeling the propagation patterns of information or are constrained by the over-smoothing problem inherent in GNNs, which limits the stacking of sufficient model depth to excavate global propagation patterns. Consequently, we introduce the ODESI model. Initially, the model employs a label propagation algorithm to delineate the distribution density of infected states within a graph structure and extends the representation of infected states from integers to state vectors, which serve as the initial states of nodes. Subsequently, the model constructs a deep architecture based on GNNs-coupled Ordinary Differential Equations (ODEs) to model the global propagation patterns of continuous propagation processes. Addressing the challenges associated with solving ODEs on graphs, we approximate the analytical solutions to reduce computational costs. Finally, we conduct simulation experiments on two real-world social network datasets, and the results affirm the efficacy of our proposed ODESI model in source identification tasks.

Keywords: source identification, ordinary differential equations, label propagation, complex networks

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Authors: Hamda M. Al-Ali

Abstract:

The ultimate aim of space exploration has been centralized around the possibility of life on other planets in the solar system. This aim is driven by the detrimental effects that climate change could potentially have on human survival on Earth in the future. This drives humans to search for feasible solutions to increase environmental and economical sustainability on Earth and to evaluate and explore the ability of human survival on other planets such as Mars. To do that, frequent space missions are required to meet the ambitious human goals. This means that reliable and affordable access to space is required, which could be largely achieved through the use of reusable spacecrafts. Therefore, materials and resources must be used wisely to meet the increasing demand. Space missions are currently extremely expensive to operate. However, reusing materials hence spacecrafts, can potentially reduce overall mission costs as well as the negative impact on both space and Earth environments. This is because reusing materials leads to less waste generated per mission, and therefore fewer landfill sites are required. Reusing materials reduces resource consumption, material production, and the need for processing new and replacement spacecraft and launch vehicle parts. Consequently, this will ease and facilitate human access to outer space as it will reduce the demand for scarce resources, which will boost material efficiency in the space industry. Material efficiency expresses the extent to which resources are consumed in the production cycle and how the waste produced by the industrial process is minimized. The strategies proposed in this paper to boost material efficiency in the space sector are the introduction of key performance indicators that are able to measure material efficiency as well as the introduction of clearly defined policies and legislation that can be easily implemented within the general practices in the space industry. Another strategy to improve material efficiency is by amplifying energy and resource efficiency through reusing materials. The circularity of various spacecraft materials such as Kevlar, steel, and aluminum alloys could be maximized through reusing them directly or after galvanizing them with another layer of material to act as a protective coat. This research paper has an aim to investigate and discuss how to improve material efficiency in space missions considering circular economy concepts so that space and Earth become more economically and environmentally sustainable. The circular economy is a transition from a make-use-waste linear model to a closed-loop socio-economic model, which is regenerative and restorative in nature. The implementation of a circular economy will reduce waste and pollution through maximizing material efficiency, ensuring that businesses can thrive and sustain. Further research into the extent to which reusable launch vehicles reduce space mission costs have been discussed, along with the environmental and economic implications it could have on the space sector and the environment. This has been examined through research and in-depth literature review of published reports, books, scientific articles, and journals. Keywords such as material efficiency, circular economy, reusable launch vehicles and spacecraft materials were used to search for relevant literature.

Keywords: circular economy, key performance indicator, material efficiency, reusable launch vehicles, spacecraft materials

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Authors: Satender Saraswat, Dharmendra Prasad Singh, Rajesh Kumar Verma, Swati Sarswat

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Background and Purpose: The predominant cause of candidiasis was Candida albicans which has shifted towards non-Candida albicans Candida (NCAC) (Candida species other than the C. albicans). NCAC, earlier considered non-pathogenic or minimally virulent, are now considered a primary cause of morbidity and mortality in immunocompromised. With the NCAC spp. gaining weightage in the clinical cases, this study was conducted to determine the prevalence of NCAC spp. in different clinical specimens and to assess a few of their virulence factors. Material and Methods: Routine samples for bacterial culture and sensitivity, showing colony characteristics like Candida on Blood Agar and microscopic features resembling Candida spp. were processed further. Candida isolates were tested for chlamydospore formation, biochemical tests including sugar fermentation and sugar assimilation tests, and growth at 42oC, colony colour on HiCrome™ Candida Differential Agar, HiCandida Identification Kit and VITEK-2 Compact. Virulence factors like adherence to buccal epithelial cells (ABEC), biofilm formation, hemolytic activity, and production of coagulase enzyme were also tested. Results: Mean age of the patients was 38.46 with a male-female ratio of 1.36:1. 137 Candida isolates were recovered. 45.3% isolates were isolated from urine, 19.7% from vaginal swabs and 13.9% from oropharyngeal swabs. 55 (40.1%) isolates of C. albicans and 82 (59.9%) of NCAC spp. were identified, with C. tropicalis (23.4%) in NCAC. C. albicans (3; 50%) was the commonest species in cases of candidemia. Haemolysin production (85.5%) and ABEC (78.2%) were the major virulence factors in C. albicans. C. tropicalis (59.4%) and C. dubliniensis (50%) showed maximum ABEC. Biofilm forming capacity was higher in C. tropicalis (78.1%) than C. albicans (67%). Conclusion: This study suggests varied prevalence and virulence based on geographical locations, even within a subcontinent. It clearly demarcates the emergence of NCAC and their predominance in different body fluids. Identification of Candida to species level should become a routine in all the laboratories.

Keywords: ABEC, NCAC, non-Candida albicans Candida, Vitek-2TM compact

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Authors: Mourad Bakouka, Abdelaziz Rabehi

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Companies need to have logistics and transportation in place that can adapt to the changing nature of construction sites. This ensures they can react quickly when needed. A study was conducted to develop a way to locate and track materials on construction sites. The system is an RFID/GPS integration that's required to pull off this feat. The study also reports how the platform has been used in construction. They found many advantages to using it, including reductions in both time and costs as well as improved management of materials orders. . For example, the time in which a project could start up was shortened from two weeks to three days with just a single digital order. As of now, the technology is still limited in its widespread adoption due largely to overall lack of awareness and difficulty connecting to it. However, as more and more companies embrace it in construction, the technology is expected to become ubiquitous. The developed platform provides contractors and construction managers with real-time information about the status of materials and work, allowing them to better manage the workflow in a project. The study sheds new light on this subject, which is essential to know. This work is becoming increasingly aware of the use of smart tools in constructing buildings.

Keywords: materials management, internet of things (IoT), radio frequency identification (RFID), construction site, supply chain management

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Authors: Irtaza M. Syed, Kaamran Raahemifar

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This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a three leg voltage source inverter (VSI). Operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a 35.7 kW PV system in Matlab/Simulink. Implementation and results show simplicity and accuracy, as well as reliability of the model.

Keywords: model predictive control, three phase voltage source inverter, PV system, Matlab/simulink

Procedia PDF Downloads 596

Authors: Shaimaa Alobaidi

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The presentation examines how urbanization opens possibilities for American Indian characters like Zits in Alexie’s Flightto explore new definitionsoftheirtribal self-identification. Zits travels in time and views the world from different bodies, ages, and races; his journeys end with different perspectives on the idea of nationhood as an American Indian. He is an example of Vine Deloria’s statementthat “urban Indians have become the cutting edge of the new Indian nationalism” (248). Flight is chosen because the momentZits leaves the real world for time-traveling adventures is very critical; it is a moment of rage that ends in the mass murder of many Anglo-Americans. The paper focus on the turning point when he returns into his body with new opportunities towards his existence among the majority of anglo-Americans who cannot help but see him American Indian minority in need of help and assistance. Characters, such as Zits, attempt to outlive alienation, and Alexie gives new definitionsof their ethnic nationhood. Futuristicdoes not mean the very far unpredictable future; it is rather a nearpotential future for teenagers of American Indians, like Zits, Arnold, andCoyoteSprings- the band in ReservationBlues; all revolutionary personalitiesin Alexie’s works. They will be analyzed as Gerald Vizenor’s “postindianwarriors” who have the ability to identify Indigenous nationalism in a post-colonial context.

Keywords: alienation, self-identification, nationhood, urbanization, postindian

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

Procedia PDF Downloads 278