Search results for: explosive detection devices

Authors: Aicha Bouhlala, Sabah Chettibi

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Doping CeO₂ with transition metals is an effective way of tuning its properties. In the present work, we have performed self-consistent ab-initio calculation using the full-potential linearized augmented plane-wave method (FP-LAPW), based on the density functional theory (DFT) as implemented in the Wien2k simulation code to study the structural, electronic, and magnetic properties of the compound Ce₁₋ₓCuₓO₂ (x=12.5%) fluorite type oxide and to explore the effects of dopant Cu in ceria. The exchange correlation potential has been treated using the Perdew-Burke-Eenzerhof revised of solid (PBEsol). In structural properties, the equilibrium lattice constant is observed for the compound, which exists within the value of 5.382 A°. In electronic properties, the spin-polarized electronic bandstructure elucidates the semiconductor nature of the material in both spin channels, with the compound was observed to have a narrow bandgap on the spin-down configuration (0.162 EV) and bandgap on the spin-up (2.067 EV). Hence, the doped atom Cu plays a vital role in increasing the magnetic moments of the supercell, and the value of the total magnetic moment is found to be 2.99438 μB. Therefore, the compound Cu-doped CeO₂ shows a strong ferromagnetic behavior. The predicted results propose the compound could be a good candidate for spintronics applications.

Keywords: Cu-doped CeO₂, DFT, Wien2k, properties

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Authors: Morello Sara, Pederiva Sabina, Bianchi Manila, Martucci Francesca, Marchis Daniela, Decastelli Lucia

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Vegetables represent an integral part of a healthy diet due to their valuable nutritional properties and the growth in consumer demand in recent years is particularly remarkable for a diet rich in vitamins and micronutrients. However, plant-based products are involved in several food outbreaks connected to various sources of contamination and quite often, bacteria responsible for side effects showed high resistance to antibiotics. The abuse of antibiotics can be one of the main mechanisms responsible for increasing antibiotic resistance (AR). Plants grown for food use can be contaminated directly by spraying antibiotics on crops or indirectly by treatments with antibiotics due to the use of manure, which may contain both antibiotics and genes of antibiotic resistance (ARG). Antibiotic residues could represent a potential way of human health risk due to exposure through the consumption of plant-based foods. The presence of antibiotic-resistant bacteria might pose a particular risk to consumers. The present work aims to investigate through a multidisciplinary approach the occurrence of ARG by means of a biomolecular approach (PCR) and the prevalence of antibiotic residues using a multi residues LC-MS/MS method, both in different plant-based products. During the period from July 2020 to October 2021, a total of 74 plant samples (33 lettuces and 41 tomatoes) were collected from 57 farms located throughout the Piedmont area, and18 out of 74 samples (11 lettuces and 7 tomatoes) were selected to LC-MS/MS analyses. DNA extracted (ExtractME, Blirt, Poland) from plants used on crops and isolated bacteria were analyzed with 6 sets of end-point multiplex PCR (Qiagen, Germany) to detect the presence of resistance genes of the main antibiotic families, such as tet genes (tetracyclines), bla (β-lactams) and mcr (colistin). Simultaneous detection of 43 molecules of antibiotics belonging to 10 different classes (tetracyclines, sulphonamides, quinolones, penicillins, amphenicols, macrolides, pleuromotilines, lincosamides, diaminopyrimidines) was performed using Exion LC system AB SCIEX coupled to a triple quadrupole mass spectrometer QTRAP 5500 from AB SCIEX. The PCR assays showed the presence of ARG in 57% (n=42): tetB (4.8%; n=2), tetA (9.5%; n=4), tetE (2.4%; n=1), tetL (12%; n=5), tetM (26%; n=11), blaSHV (21.5%; n=9), blaTEM (4.8%; n =2) and blaCTX-M (19%; n=8). In none of the analyzed samples was the mcr gene responsible for colistin resistance detected. Results obtained from LC-MS/MS analyses showed that none of the tested antibiotics appear to exceed the LOQ (100 ppb). Data obtained confirmed the presence of bacterial populations containing antibiotic resistance determinants such as tet gene (tetracycline) and bla genes (beta-lactams), widely used in human medicine, which can join the food chain and represent a risk for consumers, especially with raw products. The presence of traces of antibiotic residues in vegetables, in concentration below the LOQ of the LC-MS/MS method applied, cannot be excluded. In conclusion, traces of antibiotic residues could be a health risk to the consumer due to potential involvement in the spread of AR. PCR represents a useful and effective approach to characterize and monitor AR carried by bacteria from the entire food chain.

Keywords: plant-based products, ARG, PCR, antibiotic residues

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Authors: Jinkoo Kim, Minsung Kim

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This study develops a hybrid seismic energy dissipation device composed of a viscoelastic damper and a steel slit damper connected in parallel. A cyclic loading test is conducted on a test specimen to validate the seismic performance of the hybrid damper. Then a moment-framed model structure is designed without seismic load so that it is retrofitted with the hybrid dampers. The model structure is transformed into an equivalent simplified system to find out optimum story-wise damper distribution pattern using genetic algorithm. The effectiveness of the hybrid damper is investigated by fragility analysis and the life cycle cost evaluation of the structure with and without the dampers. The analysis results show that the model structure has reduced probability of reaching damage states, especially the complete damage state, after seismic retrofit. The expected damage cost and consequently the life cycle cost of the retrofitted structure turn out to be significantly small compared with those of the original structure. Acknowledgement: This research was supported by the Ministry of Trade, Industry and Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R & D program (N043100016).

Keywords: seismic retrofit, slit dampers, friction dampers, hybrid dampers

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Authors: Rakesh Choudhary, Ajay Kumar, Deepak Sharma

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The main goal of this paper is to investigate the challenges and benefits of IoT integration in biogas production. This overview explains how the inclusion of IoT can enhance biogas production efficiency. Therefore, such collected data can be explored by advanced analytics, including Artificial intelligence (AI) and Machine Learning (ML) algorithms, consequently improving bio-energy processes. To boost biogas generation efficiency, this report examines the use of IoT devices for real-time data collection on key parameters, e.g., pH, temperature, gas composition, and microbial growth. Real-time monitoring through big data has made it possible to detect diverse, complex trends in the process of producing biogas. The Informed by advanced analytics can also help in improving bio-energy production as well as optimizing operational conditions. Moreover, IoT allows remote observation, control and management, which decreases manual intervention needed whilst increasing process effectiveness. Such a paradigm shift in the incorporation of IoT technologies into biogas production systems helps to achieve higher productivity levels as well as more practical biomass quality biomethane through real-time monitoring-based proactive decision-making, thus driving continuous performance improvement.

Keywords: internet of things, biogas, renewable energy, sustainability, anaerobic digestion, real-time monitoring, optimization

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Authors: Raghuveer Kaur, Kartikay Sharma, Ashu Khanna

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Earnings management has been present from times immemorial. The recent downfall of giant enterprises like Enron, Satyam and WorldCom has brought a lot of focus on the study and detection of earnings management. The present study is an attempt to study earnings management in one of the fastest emerging economy - India. The study makes an attempt to understand earnings management in different sectors of the economy. The paper first tests a hypothesis to check whether different sectors of India are engaged in earnings management or not. In the later section the paper aims to study the level of earnings management in 6 popular sectors of India: IT&BPO, Retail, Telecom, Biotech, Hotels and coffee. To measure earnings management two popular techniques of detecting earnings management has been employed: Modified Jones Model and Beniesh M Score. A total of 332 companies were studied. Publicly available data from Capitaline database has been used. The paper also classifies the top and bottom five performers on the basis of sales turnover in each sector and identifies whether they manage their earnings or not.

Keywords: earnings management, India, modified Jones model, Beneish M score

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Authors: Norah Mohammed Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD and they are Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then Feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by the Support Vector Machine (SVM), achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: autism spectrum disorder, machine learning, feature selection, support vector machine

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Authors: Jyun-Guo You, Wei-Lung Tseng

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Herein, this study represents a synthetic route for producing highly luminescent AuNCs based on the integration of two concepts, including thiol-induced luminescence enhancement of ligand-insufficient GSH-AuNCs and Ce3+-induced aggregation of GSH-AuNCs. The synthesis of GSH-AuNCs was conducted by modifying the previously reported procedure. To produce more Au(I)-GSH complexes on the surface of ligand-insufficient GSH-AuNCs, the extra GSH is added to attach onto the AuNC surface. The formed ligand-sufficient GSH-AuNCs (LS-GSH-AuNCs) emit relatively strong luminescence. The luminescence of LS-GSH-AuNCs is further enhanced by the coordination of two carboxylic groups (pKa1 = 2 and pKa2 = 3.5) of GSH and lanthanide ions, which induce the self-assembly of LS-GSH-AuNCs. As a result, the quantum yield of the self-assembled LS-GSH-AuNCs (SA-AuNCs) was improved to be 13%. Interestingly, the SA-AuNCs were dissembled into LS-GSH-AuNCs in the presence of adenosine triphosphate (ATP) because of the formation of the ATP- lanthanide ion complexes. Our assay was employed to detect alkaline phosphatase (ALP) activity over the range of 0.1−10 U/mL with a limit of detection (LOD) of 0.03 U/mL.

Keywords: self-assembly, lanthanide ion, adenosine triphosphate, alkaline phosphatase

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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Visible light communication is an emerging technology for almost a decade now; there is a growing need for VLC systems to overcome the challenges faced by radio frequency RF communication systems. With the advancement in the development of solid-state sources, in the future would replace incandescent and fluorescent light sources. These solid-state devices are not only to be used for illumination but can also be employed for communication and navigational purposes. The replacement of conventional illumination sources with highly efficient light-emitting diodes (LED's) (generally white light) will reduce energy consumption as well as environmental pollution. White LEDs dissipate very less power as compared to conventional light sources. The use of LED's is not only beneficial in terms of power consumption, but it also has an intrinsic capability for indoor wireless communication as compared to indoor RF communication. It is considerably low in cost to operate than the RF systems such as Wi-Fi routers, allows convenient means of reusing the bandwidth, and there is a huge potential for high data rate transmissions with enhanced data security. This paper provides an overview of some of the current challenges with VLC and proposes a possible solution to deal with these challenges; it also examines some joint protocols to optimize the joint illumination and communication functionality.

Keywords: visible light communication, line of sight, root mean square delay spread, light emitting diodes

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Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower, and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software we used in our study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: preprocessing of the data used, feature detection, and classification. We tried to determine the success of our study with different accuracy metrics and the results. We presented it comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: decision tree, water quality, water pollution, machine learning

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Authors: J. Yang, M. Monnot, T. Eljaddi, L. Simonian, L. Ercolei, P. Moulin

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The application of membrane technology to wastewater treatment has expanded rapidly under increasing stringent legislation and environmental protection requirements. At the same time, the water resource is becoming precious, and water reuse has gained popularity. Particularly, ultrafiltration (UF) is a very promising technology for water reuse as it can retain organic matters, suspended solids, colloids, and microorganisms. Nevertheless, few studies dealing with operating optimization of UF as a tertiary treatment for water reuse on a semi-industrial scale appear in the literature. Therefore, this study aims to explore the permeate water quality and to optimize operating parameters (maximizing productivity and minimizing irreversible fouling) through the operation of a UF pilot plant under real conditions. The fully automatic semi-industrial UF pilot plant with periodic classic backwashes (CB) and air backwashes (AB) was set up to filtrate the secondary effluent of an urban wastewater treatment plant (WWTP) in France. In this plant, the secondary treatment consists of a conventional activated sludge process followed by a sedimentation tank. The UF process was thus defined as a tertiary treatment and was operated under constant flux. It is important to note that a combination of CB and chlorinated AB was used for better fouling management. The 200 kDa hollow fiber membrane was used in the UF module, with an initial permeability (for WWTP outlet water) of 600 L·m-2·h⁻¹·bar⁻¹ and a total filtration surface of 9 m². Fifteen filtration conditions with different fluxes, filtration times, and air backwash frequencies were operated for more than 40 hours of each to observe their hydraulic filtration performances. Through comparison, the best sustainable condition was flux at 60 L·h⁻¹·m⁻², filtration time at 60 min, and backwash frequency of 1 AB every 3 CBs. The optimized condition stands out from the others with > 92% water recovery rates, better irreversible fouling control, stable permeability variation, efficient backwash reversibility (80% for CB and 150% for AB), and no chemical washing occurrence in 40h’s filtration. For all tested conditions, the permeate water quality met the water reuse guidelines of the World Health Organization (WHO), French standards, and the regulation of the European Parliament adopted in May 2020, setting minimum requirements for water reuse in agriculture. In permeate: the total suspended solids, biochemical oxygen demand, and turbidity were decreased to < 2 mg·L-1, ≤ 10 mg·L⁻¹, < 0.5 NTU respectively; the Escherichia coli and Enterococci were > 5 log removal reduction, the other required microorganisms’ analysis were below the detection limits. Additionally, because of the COVID-19 pandemic, coronavirus SARS-CoV-2 was measured in raw wastewater of WWTP, UF feed, and UF permeate in November 2020. As a result, the raw wastewater was tested positive above the detection limit but below the quantification limit. Interestingly, the UF feed and UF permeate were tested negative to SARS-CoV-2 by these PCR assays. In summary, this work confirms the great interest in UF as intensified tertiary treatment for water reuse and gives operational indications for future industrial-scale production of reclaimed water.

Keywords: semi-industrial UF pilot plant, water reuse, fouling management, coronavirus

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Authors: O. Ikpotokin

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In any production process, every product is aimed to attain a certain standard, but the presence of assignable cause of variability affects our process, thereby leading to low quality of product. The ability to identify and remove this type of variability reduces its overall effect, thereby improving the quality of the product. In case of a univariate control chart signal, it is easy to detect the problem and give a solution since it is related to a single quality characteristic. However, the problems involved in the use of multivariate control chart are the violation of multivariate normal assumption and the difficulty in identifying the quality characteristic(s) that resulted in the out of control signals. The purpose of this paper is to examine the use of non-parametric control chart (the bootstrap approach) for obtaining control limit to overcome the problem of multivariate distributional assumption and the p-value method for detecting out of control signals. Results from a performance study show that the proposed bootstrap method enables the setting of control limit that can enhance the detection of out of control signals when compared, while the p-value method also enhanced in identifying out of control variables.

Keywords: bootstrap control limit, p-value method, out-of-control signals, p-value, quality characteristics

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Authors: Zeid A. Alothman

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Silver nanoparticle (AgNP) has been synthesized using adrenaline. Adrenaline readily undergoes an autoxidation reaction in an alkaline medium with the dissolved oxygen to form adrenochrome, thus behaving as a mild reducing agent for the dissolved oxygen. This reducing behavior of adrenaline when employed to reduce Ag(+) ions yielded a large enhancement in the intensity of absorbance in the visible region. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) studies have been performed to confirm the surface morphology of AgNPs. Further, the metallic nanoparticles with size greater than 2 nm caused a strong and broad absorption band in the UV-visible spectrum called surface plasmon band or Mie resonance. The formation of AgNPs caused the large enhancement in the absorbance values with λmax at 436 nm through the excitation of the surface plasmon band. The formation of AgNPs was adapted to for the quantitative assessment of adrenaline using spectrophotometry with lower detection limit and higher precision values.

Keywords: silver nanoparticle, adrenaline, XRD, TEM, analysis

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Authors: Sonia Perez-Gamboa, Qingquan Sun, Yan Zhang

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Non-intrusive sensor-based human activity recognition (HAR) is utilized in a spectrum of applications, including fitness tracking devices, gaming, health care monitoring, and smartphone applications. Deep learning models such as convolutional neural networks (CNNs) and long short term memory (LSTM) recurrent neural networks (RNNs) provide a way to achieve HAR accurately and effectively. In this paper, we design a multi-layer hybrid architecture with CNN and LSTM and explore a variety of multi-layer combinations. Based on the exploration, we present a lightweight, hybrid, and multi-layer model, which can improve the recognition performance by integrating local features and scale-invariant with dependencies of activities. The experimental results demonstrate the efficacy of the proposed model, which can achieve a 94.7% activity recognition rate on a benchmark human activity dataset. This model outperforms traditional machine learning and other deep learning methods. Additionally, our implementation achieves a balance between recognition rate and training time consumption.

Keywords: deep learning, LSTM, CNN, human activity recognition, inertial sensor

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Authors: Bong Keun Kang, Sung Suk Oh, Jeong-Woo Sohn, Jong-Ryul Choi, Young Ho Kim

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In this paper, we describe two-step micro-pattering by using laser cutting and wax printing. Wax printing is performed only on the bridges for hydrophobic barriers. We prepared 405nm blue-violet laser module and wax pencil module. And, this two modules combine x-y plot. The hollow microstructure formed by laser patterning define the hydrophilic flowing paths. However, bridges are essential to avoid the cutting area being the island. Through the support bridges, microfluidic solution spread out to the unnecessary areas. Chromatography blotting paper was purchased from Whatman. We used 20x20 cm and 46x57 cm of chromatography blotting paper. Axis moving speed of x-y plot was the main parameter of optimization. For aligning between the two patterning, the paper sheet was taped at the bottom. After the two-step patterning, temperature curing step was done at 110-130 °C. The resolution of the fabrication and the potential of the multiplex detection were investigated.

Keywords: µPADs, microfluidic, biosensor, mass-fabrication

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Authors: Mert Kıranç, Ekrem Duman, Murat Özbilen

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In order to achieve new product development (NPD) activities on time and within budgetary constraints, the NPD managers need a well-designed methodology. This study intends to create an integrated project management methodology for the ones who focus on new product development projects. In the scope of the study, four different management systems are combined. These systems are called as 'Schedule-oriented Stage-Gate Method, Risk Management, Change Management and Earned Value Management'. New product development term is quite common in many different industries such as defense industry, construction, health care/dental, higher education, fast moving consumer goods, white goods, electronic devices, marketing and advertising and software development. All product manufacturers run against each other’s for introducing a new product to the market. In order to achieve to produce a more competitive product in the market, an optimum project management methodology is chosen, and this methodology is adapted to company culture. The right methodology helps the company to present perfect product to the customers at the right time. The benefits of proposed methodology are discussed as an application by a company. As a result, how the integrated methodology improves the efficiency and how it achieves the success of the project are unfolded.

Keywords: project, project management, management methodology, new product development, risk management, change management, earned value, stage-gate

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Authors: Hyun-Woo Cho

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Unexpected operational events or abnormalities of industrial processes have a serious impact on the quality of final product of interest. In terms of statistical process control, fault detection and diagnosis of processes is one of the essential tasks needed to run the process safely. In this work, nonlinear representation of process measurement data is presented and evaluated using a simulation process. The effect of using different representation methods on the diagnosis performance is tested in terms of computational efficiency and data handling. The results have shown that the nonlinear representation technique produced more reliable diagnosis results and outperforms linear methods. The use of data filtering step improved computational speed and diagnosis performance for test data sets. The presented scheme is different from existing ones in that it attempts to extract the fault pattern in the reduced space, not in the original process variable space. Thus this scheme helps to reduce the sensitivity of empirical models to noise.

Keywords: fault diagnosis, nonlinear technique, process data, reduced spaces

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Authors: David J. Thomson, Joshua C. J. Chew

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Background: Syndrome of Irreversible Lithium-Effectuated Neurotoxicity (SILENT) is a rare complication of lithium toxicity that typically causes irreversible cerebellar dysfunction. These patients may require hemodialysis and extensive supports in the intensive care. Methods: A review was performed on the available literature of SILENT with a focus on current pathophysiological hypotheses and advances in treatment. Articles were restricted to the English language. Results: Although the exact mechanism is unclear, CNS demyelination, especially in the cerebellum, was seen on the brain biopsies of a proportion of patients. There is no definitive management of SILENT but instead current management is focused on primary and tertiary prevention – detection of those at risk, and rehabilitation post onset of neurological deficits. Conclusions: This review draws conclusions from a limited amount of available literature, most of which are isolated case reports. Greater awareness of SILENT and further investigation into the risk factors and pathogenesis are required so this serious and irreversible syndrome may be avoided.

Keywords: lithium toxicity, pathogenesis, SILENT, syndrome of irreversible lithium-effectuated neurotoxicity

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Authors: A. Calabrese, V. F. Uricchio, D. di Noia, S. Favale, C. Caiati, G. P. Maggi, G. Donvito, D. Diacono, S. Tangaro, A. Italiano, E. Riezzo, M. Zippitelli, M. Toriello, E. Celiberti, D. Festa, A. Colaianni

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Climate change has a severe impact on human health. There is a vast literature demonstrating temperature increase is causally related to cardiovascular problem and represents a high risk for human health, but there are not study that improve a solution. In this work, it is studied how the clime influenced the human parameter through the analysis of climatic conditions in an area of the Apulia Region: Capurso Municipality. At the same time, medical personnel involved identified a set of variables useful to define an index describing health condition. These scientific studies are the base of an innovative alert system, IPPOCRATE, whose aim is to asses climate risk and share information to population at risk to support prevention and mitigation actions. IPPOCRATE is an e-health system, it is designed to provide technological support to analysis of health risk related to climate and provide tools for prevention and management of critical events. It is the first integrated system of prevention of human risk caused by climate change. IPPOCRATE calculates risk weighting meteorological data with the vulnerability of monitored subjects and uses mobile and cloud technologies to acquire and share information on different data channels. It is composed of four components: Multichannel Hub. Multichannel Hub is the ICT infrastructure used to feed IPPOCRATE cloud with a different type of data coming from remote monitoring devices, or imported from meteorological databases. Such data are ingested, transformed and elaborated in order to be dispatched towards mobile app and VoIP phone systems. IPPOCRATE Multichannel Hub uses open communication protocols to create a set of APIs useful to interface IPPOCRATE with 3rd party applications. Internally, it uses non-relational paradigm to create flexible and highly scalable database. WeHeart and Smart Application The wearable device WeHeart is equipped with sensors designed to measure following biometric variables: heart rate, systolic blood pressure and diastolic blood pressure, blood oxygen saturation, body temperature and blood glucose for diabetic subjects. WeHeart is designed to be easy of use and non-invasive. For data acquisition, users need only to wear it and connect it to Smart Application by Bluetooth protocol. Easy Box was designed to take advantage from new technologies related to e-health care. EasyBox allows user to fully exploit all IPPOCRATE features. Its name, Easy Box, reveals its purpose of container for various devices that may be included depending on user needs. Territorial Registry is the IPPOCRATE web module reserved to medical personnel for monitoring, research and analysis activities. Territorial Registry allows to access to all information gathered by IPPOCRATE using GIS system in order to execute spatial analysis combining geographical data (climatological information and monitored data) with information regarding the clinical history of users and their personal details. Territorial Registry was designed for different type of users: control rooms managed by wide area health facilities, single health care center or single doctor. Territorial registry manages such hierarchy diversifying the access to system functionalities. IPPOCRATE is the first e-Health system focused on climate risk prevention.

Keywords: climate change, health risk, new technological system

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Authors: Diane Ghanem, Arjun Gupta, Rohan Vijayan, Ali Uneri, Babar Shafiq

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Introduction: Ankle sprains and fractures often result in syndesmosis injuries. Unstable syndesmotic injuries result from relative motion between the distal ends of the tibia and fibula, anatomic juncture which should otherwise be rigid, and warrant operative management. Clinical and radiological evaluations of intraoperative syndesmosis stability remain a challenging task as traditional 2D fluoroscopy is limited to a uniplanar translational displacement. The purpose of this pilot cadaveric study is to compare the 2D fluoroscopy and 3D cone beam computed tomography (CBCT) stress-induced syndesmosis displacements. Methods: Three fresh-frozen lower legs underwent 2D fluoroscopy and 3D CIOS CBCT to measure syndesmosis position before dissection. Syndesmotic injury was simulated by resecting the (1) anterior inferior tibiofibular ligament (AITFL), the (2) posterior inferior tibiofibular ligament (PITFL) and the inferior transverse ligament (ITL) simultaneously, followed by the (3) interosseous membrane (IOM). Manual external rotation and Cotton stress test were performed after each of the three resections and 2D and 3D images were acquired. Relevant 2D and 3D parameters included the tibiofibular overlap (TFO), tibiofibular clear space (TCS), relative rotation of the fibula, and anterior-posterior (AP) and medial-lateral (ML) translations of the fibula relative to the tibia. Parameters were measured by two independent observers. Inter-rater reliability was assessed by intraclass correlation coefficient (ICC) to determine measurement precision. Results: Significant mismatches were found in the trends between the 2D and 3D measurements when assessing for TFO, TCS and AP translation across the different resection states. Using 3D CBCT, TFO was inversely proportional to the number of resected ligaments while TCS was directly proportional to the latter across all cadavers and ‘resection + stress’ states. Using 2D fluoroscopy, this trend was not respected under the Cotton stress test. 3D AP translation did not show a reliable trend whereas 2D AP translation of the fibula was positive under the Cotton stress test and negative under the external rotation. 3D relative rotation of the fibula, assessed using the Tang et al. ratio method and Beisemann et al. angular method, suggested slight overall internal rotation with complete resection of the ligaments, with a change < 2mm - threshold which corresponds to the commonly used buffer to account for physiologic laxity as per clinical judgment of the surgeon. Excellent agreement (>0.90) was found between the two independent observers for each of the parameters in both 2D and 3D (overall ICC 0.9968, 95% CI 0.995 - 0.999). Conclusions: The 3D CIOS CBCT appears to reliably depict the trend in TFO and TCS. This might be due to the additional detection of relevant rotational malpositions of the fibula in comparison to the standard 2D fluoroscopy which is limited to a single plane translation. A better understanding of 3D imaging may help surgeons identify the precise measurements planes needed to achieve better syndesmosis repair.

Keywords: 2D fluoroscopy, 3D computed tomography, image processing, syndesmosis injury

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Authors: Kamyar Fakhr, Roozbeh Salmani

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Alphonse Bertillon developed the first biometric security system in the 1800s. Today, many governments and giant companies are considering or have procured biometrically enabled security schemes. Iris is a kaleidoscope of patterns and colors. Each individual holds a set of irises more unique than their thumbprint. Every single day, giant companies like Google and Apple are experimenting with reliable biometric systems. Now, after almost 200 years of improvements, face ID does not work with masks, it gives access to fake 3D images, and there is no global usage of biometric recognition systems as national identity (ID) card. The goal of this paper is to demonstrate the advantages of iris recognition overall biometric recognition systems. It make two extensions: first, we illustrate how a very large amount of internet fraud and cyber abuse is happening due to bugs in face recognition systems and in a very large dataset of 3.4M people; second, we discuss how establishing a secure global network of iris recognition devices connected to authoritative convolutional neural networks could be the safest solution to this dilemma. Another aim of this study is to provide a system that will prevent system infiltration caused by cyber-attacks and will block all wireframes to the data until the main user ceases the procedure.

Keywords: biometric system, convolutional neural network, cyber-attack, secure

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Authors: Xiaochun Huang, Xuejun Zhao, Yun Zou, Feiyu Yang, Wenbin Liu, Nan Deng, Ming Zhang, Nengbin Cai

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A simple device termed infrared radiation (IR) was developed for rapid visualization of sweat fingerprints deposit on paper with blue light (450 nm, 11 W). In this approach, IR serves as the pretreatment device before the sweat fingerprints was illuminated by blue light. An annular blue light source was adopted for visualizing latent sweat fingerprints. Sample fingerprints were examined under various conditions after deposition, and experimental results indicate that the recovery rate of the latent sweat fingerprints is in the range of 50%-100% without chemical treatments. A mechanism for the observed visibility is proposed based on transportation and re-impregnation of fluorescer in paper at the region of water. And further exploratory experimental results gave the full support to the visible mechanism. Therefore, such a method as IR-pretreated in detecting latent fingerprints may be better for examination in the case where biological information of samples is needed for consequent testing.

Keywords: forensic science, visualization, infrared radiation, blue light, latent sweat fingerprints, detection

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Authors: Memnun Seven, Aygül Akyüz

Abstract:

Having a baby, giving birth and being a mother are generally considered happy events, especially for women who have had a history of infertility and may have suffered emotionally, physically and financially. Although the transition from the prenatal period to the postnatal period is usually desired and planned, it is a developmental and cognitive transition period full of complex emotional reactions. During this period, common mood disorders for women include maternity blues, postpartum depression and postpartum psychosis. Postpartum depression is a common and serious mood disorder which can jeopardize the health of the mother, baby and family within the first year of delivery. Knowing the risks factors is an important issue for the early detection and early intervention of postpartum depression. However, knowing that a history of infertility may contribute to the development of postpartum depression, there are few studies assessing the effects of infertility during the diagnosis and treatment of depression. In this review, the effects of infertility on the development of postpartum depression and nurse/midwives’ roles in this issue are discussed in light with the literature.

Keywords: infertility, postpartum depression, risk factors, mood disorder

Procedia PDF Downloads 471

Authors: O. A. Al-Mashaqbeh, A. M. Ghrair, D. Alsafadi, S. S. Dalahmeh, S. L. Bartelt-Hunt, D. D. Snow

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Grab samples were collected in the summer to characterize selected pharmaceuticals and personal care products (PPCPs) in the influent of two wastewater treatment plants (WWTPs) in Jordan. Liquid chromatography tandem mass spectrometry (LC–MS/MS) was utilized to determine the concentrations of 18 compounds of PPCPs. Among all of the PPCPs analyzed, eight compounds were detected in the influent samples (1,7-dimethylxanthine, acetaminophen, caffeine, carbamazepine, cotinine, morphine, sulfamethoxazole and trimethoprim). However, five compounds (amphetamine, cimetidine, diphenhydramine, methylenedioxyamphetamine (MDA) and sulfachloropyridazine) were not detected in collected samples (below the detection limits <0.005 µg/l). Moreover, the results indicated that the highest concentration levels detected in collected samples were caffeine, acetaminophen, 1,7-dimethylxanthine, cotinine and carbamazepine at concentration of 182.5 µg/L, 28.7 µg/l, 7.47 µg/l, 4.67 µg/l and 1.54 µg/L, respectively. In general, most of compounds concentrations measured in wastewater in Jordan are within the range for wastewater previously reported in India wastewater except caffeine.

Keywords: pharmaceuticals, personal care products, wastewater, Jordan

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Authors: Allamula Ashok, Peela Lasya, Daljin Jacob, P. Muhammed Razi, Satyesh Kumar Yadav

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We report zinc (Zn) as a seed layer material and a need for a specific disposition sequence to grow ultrathin silver (Ag) films on quartz (SiO₂). Ag films of thickness 4, 6, 8 and 10 nm were deposited by DC magnetron sputtering without and with Zn seed layer thickness of 1, 2 and 4 nm. The effect of Zn seed layer thickness and its annealing on the surface morphology, sheet resistance, and stability of ultrathin Ag films is investigated. We show that by increasing Zn seed layer thickness from 1 to 2 nm, there is a 5-order reduction in sheet resistance of 6 nm Ag films. We find that annealing of the seed layer is crucial to achieving stability of ultrathin Ag films. 6 nm Ag film with 2 nm Zn is unstable to 100 oC annealing, while the 6 nm Ag film with annealed 2 nm Zn seed layer is stable. 2 nm Zn seeded 8 nm Ag film maintained a constant sheet resistance of 7 Ω/□ for all 6 months of exposure to ambient conditions. Among the ultrathin film grown, 8nm Ag film with 2nm Zn seed layer had the best figure of merit with sheet resistance of 7 Ω/□, mean absolute surface roughness (Ra) ~1 nm, and optical transparency of 61 %. Such stable exposed ultrathin Ag films can find applications as catalysts, sensors, and transparent and conductive electrodes for solar cells, LEDs and plasmonic devices.

Keywords: ultrathin Ag films, magnetron sputtering, thermal stability, seed layer, exposed silver, zinc.

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Authors: Hebat‑Allah S. Tohamy, Mohamed El‑Sakhawy, Samir Kamel

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Fluorescent carbon quantum dots (CQDs) were prepared by an economical, green, and single-step procedure based on microwave heating of urea with sugarcane bagasse (SCB), cellulose (C), or carboxymethyl cellulose (CMC). The prepared CQDs were characterized using a series of spectroscopic techniques, and they had small size, strong absorption in the UV, and excitation wavelength-dependent fluorescence. The prepared CQDs were used for Pb(II) adsorption from an aqueous solution. The removal efficiency percentages (R %) were 99.16, 96.36, and 98.48 for QCMC, QC, and QSCB. The findings validated the efficiency of CQDs synthesized from CMC, cellulose, and SCB as excellent materials for further utilization in the environmental fields of wastewater pollution detection, adsorption, and chemical sensing applications. The kinetics and isotherms studied found that all CQD isotherms fit well with the Langmuir model than Freundlich and Temkin models. According to R², the pseudo-second-order fits the adsorption of QCMC, while the first-order one fits with QC and QSCB.

Keywords: carbon quantum dots, graphene quantum dots, fluorescence, quantum yield, water treatment, agricultural wastes

Procedia PDF Downloads 121

Authors: Minh Nguyen

Abstract:

Today, QR or Quick Response Codes are prevalent, and mobile/smart devices can efficiently read and understand them. Therefore, we can see their appearance in many areas, such as storing web pages/websites, business phone numbers, redirecting to an app download, business location, social media. The popularity of the QR Code is mainly because of its many advantages, such as it can hold a good amount of information, is small, easy to scan and read by a general RGB camera, and it can still work with some damages on its surface. However, there are still some issues. For instance, some areas needed to be kept untouched for its successful decode (e.g., the “Finder Patterns,” the “Quiet Zone,” etc.), the capability of built-in auto-correction is not robust enough, and it is not flexible enough for many application such as Augment Reality (AR). We proposed a new Colour Quick Response Code that has several advantages over the original ones: (1) there is no untouchable area, (2) it allows up to 40% of the entire code area to be damaged, (3) it is more beneficial for Augmented Reality applications, and (4) it is back-compatible and readable by available QR Code scanners such as Pyzbar. From our experience, our Colour Quick Response Code is significantly more flexible on damage compared to the original QR Code. Our code is believed to be suitable in situations where standard 2D Barcodes fail to work, such as curved and shiny surfaces, for instance, medical blood test sample tubes and syringes.

Keywords: QR code, computer vision, image processing, 2D barcode

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Authors: Seyfullah Madakbas, Emrah Cakmakci

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Polyaniline (PANI), the most studied member of the conductive polymers, has a wide range of uses from several electronic devices to various conductive high-technology applications. Boron nitride (BN) is a boron and nitrogen containing compound with superior chemical and thermal resistance and thermal conductivity. Even though several composites of PANI was prepared in literature, the preparation of h-BN/PANI composites is rare. In this work PANI was polymerized in the presence of different amounts of h-BN (1, 3 and 5% with respect to PANI) by using 0.1 M solution of NH4S2O8 in HCl as the oxidizing agent and conductive composites were prepared. Composites were structurally characterized with FTIR spectroscopy and X-Ray Diffraction (XRD). Thermal properties of conductive composites were determined by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Dielectric measurements were performed in the frequency range of 106–108 Hz at room temperature. The corresponding bands for the benzenoid and quinoid rings at around 1593 and 1496 cm-1 in the FTIR spectra of the composites proved the formation of polyaniline. Together with the FTIR spectra, XRD analysis also revealed the existence of the interactions between PANI and h-BN. Glass transition temperatures (Tg) of the composites increased with the increasing amount of PANI (from 87 to 101). TGA revealed that the char yield of the composites increased as the amount of h-BN was increased in the composites. Finally the dielectric permittivity of 3 wt.%h-BN-containing composite was measured and found as approximately 17. This work was supported by Marmara University, Commission of Scientific Research Project.

Keywords: dielectric permittivity, h-BN, PANI, thermal analysis

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Authors: Mahasa Zahirnia

Abstract:

The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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

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

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

Procedia PDF Downloads 144