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Authors: A. Chiker, A. Benamor, A. Haddad, Y. Hadji, M. Hadji

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Metal matrix composites (MMCs) have been of big interest for a few decades. Their major drawback lies in their enhanced mechanical performance over unreinforced alloys. They found ground in many engineering fields, such as aeronautics, aerospace, automotive, and other structural applications. One of the most used alloys as a matrix is nickel alloys, which meet the need for high-temperature mechanical properties; some attempts have been made to develop nickel base composites reinforced by high melt point and high modulus particulates. Among the carbides used as reinforcing particulates, chromium carbide is interesting for wear applications; it is widely used as a tribological coating material in high-temperature applications requiring high wear resistance and hardness. Moreover, a set of properties make it suitable for use in MMCs, such as toughness, the good corrosion and oxidation resistance of its three polymorphs -the cubic (Cr23C6), the hexagonal (Cr7C3), and the orthorhombic (Cr3C2)-, and it’s coefficient of thermal expansion that is almost equal to that of metals. The in-situ synthesis of CrC-reinforced Ni matrix composites could be achieved by the powder metallurgy route. To ensure the in-situ reactions during the sintering process, the use of phase precursors is necessary. Recently, new precursor materials have been proposed; these materials are called MAX phases. The MAX phases are thermodynamically stable nano-laminated materials displaying unusual and sometimes unique properties. These novel phases possess Mn+1AXn chemistry, where n is 1, 2, or 3, M is an early transition metal element, A is an A-group element, and X is C or N. Herein, the pressureless sintering method is used to elaborate Ni/Cr2AlC composites. Four composites were elaborated from 5, 10, 15 and 20 wt% of Cr2AlC MAX phase precursor which fully reacted with Ni-matrix at 1100 °C sintering temperature for 4 h in argon atmosphere. XRD results showed that Cr2AlC MAX phase was totally decomposed forming chromium carbide Cr7C3, and the released Al and Cr atoms diffused in Ni matrix giving rise to γ-Ni(Al,Cr) solid solution and γ’-Ni3(Al,Cr) intermetallic. Scanning Electron Microscopy (SEM) of the elaborated samples showed the presence of nanosized Cr7C3 reinforcing particles embedded in the Ni metal matrix, which have a direct impact on the tribological properties of the composites and their hardness. All the composites exhibited higher hardness than pure Ni; whereas adding 15 wt% of Cr2AlC gives the highest hardness (1.85 GPa). Using a ball-on-disc tribometer, dry sliding tests for the elaborated composites against 100Cr6 steel ball were studied under different applied loads. The microstructures and worn surface characteristics were then analyzed using SEM and Raman spectroscopy. The results show that all the composites exhibited better wear resistance compared to pure Ni, which could be explained by the formation of a lubricious tribo-layer during sliding and the good bonding between the Ni matrix and the reinforcing phases.

Keywords: composites, microscopy, sintering, wear

Procedia PDF Downloads 68

Authors: P. Wongthongtham, K. Coutinho, A. MacCarthy

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Layer-One.X (L1X) blockchain provides the infrastructure layer, and decentralised applications can be created on the L1X infrastructure. L1X tokenomics are important and require a proportional balance between token distribution, nurturing user activity and engagement, and financial incentives. In this paper, we present research in progress on L1X tokenomics describing key concepts and implementations, including token velocity and value, incentive scheme, and broad distribution. Particularly the economic design of the native token of the L1X blockchain, called HeartBit (HB), is presented.

Keywords: tokenisation, layer one blockchain, interoperability, token distribution, L1X blockchain

Procedia PDF Downloads 111

Authors: Franziska Mais, Till Gramberg

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The paper presents the development of a sustainable business model for a platform-based application tailored for sawing companies in small and medium-sized enterprises (SMEs). The focus is on the integration of sustainability principles into the design of the business model to ensure a technologically advanced, legally sound, and economically efficient solution. Easy2IoT is a research project that aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements, and potential solutions for smart services are derived. The structuring of the business ecosystem within the application plays a central role, whereby the roles of the partners, the management of the IT infrastructure and services, as well as the design of a sustainable operator model are considered. The business model is developed using the value proposition canvas, whereby a detailed analysis of the requirements for the business model is carried out, taking sustainability into account. This includes coordination with the business model patterns, according to Gassmann, and integration into a business model canvas for the Easy2IoT product. Potential obstacles and problems are identified and evaluated in order to formulate a comprehensive and sustainable business model. In addition, sustainable payment models and distribution channels are developed. In summary, the article offers a well-founded insight into the systematic development of a sustainable business model for platform-based applications in SME sawmills, with a particular focus on the synergy of ecological responsibility and economic efficiency.

Keywords: business model, sustainable business model, IIoT, IIoT-platform, industrie 4.0, big data

Procedia PDF Downloads 81

Authors: C. Bolakis, I. S. Karanasiou, D. Grbovic, G. Karunasiri, N. Uzunoglu

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A new approach for efficient detection of THz radiation in biomedical imaging applications is proposed. A double-layered absorber consisting of a 32 nm thick aluminum (Al) metallic layer, located on a glass medium (SiO2) of 1 mm thickness, was fabricated and used to design a fine-tuned absorber through a theoretical and finite element modeling process. The results indicate that the proposed low-cost, double-layered absorber can be tuned based on the metal layer sheet resistance and the thickness of various glass media taking advantage of the diversity of the absorption of the metal films in the desired THz domain (6 to 10 THz). It was found that the composite absorber could absorb up to 86% (a percentage exceeding the 50%, previously shown to be the highest achievable when using single thin metal layer) and reflect less than 1% of the incident THz power. This approach will enable monitoring of the transmission coefficient (THz transmission ‘’fingerprint’’) of the biosample with high accuracy, while also making the proposed double-layered absorber a good candidate for a microbolometer pixel’s active element. Based on the aforementioned promising results, a more sophisticated and effective double-layered absorber is under development. The glass medium has been substituted by diluted poly-si and the results were twofold: An absorption factor of 96% was reached and high TCR properties acquired. In addition, a generalization of these results and properties over the active frequency spectrum was achieved. Specifically, through the development of a theoretical equation having as input any arbitrary frequency in the IR spectrum (0.3 to 405.4 THz) and as output the appropriate thickness of the poly-si medium, the double-layered absorber retains the ability to absorb the 96% and reflects less than 1% of the incident power. As a result, through that post-optimization process and the spread spectrum frequency adjustment, the microbolometer detector efficiency could be further improved.

Keywords: bio-imaging, fine-tuned absorber, fingerprint, microbolometer

Procedia PDF Downloads 347

Authors: Khaled Mawardi

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Bay laurel (Laurus nobilis L.) is an evergreen of the Laurus genus of the Lauraceae Family. It is a plant native to the southern Mediterranean and widespread in Syria. It is a plant with enormous industrial applications. For instance, they are used as platform chemicals in food, pharmaceutical and cosmetic applications. Herein, we report an efficient extraction of Bay laurel oil from Bay laurel fruits via a comparative investigation of boiled water conventional extraction technique and microwave-assisted extraction (MAE) by microwave heating at atmospheric pressure. In order to optimize the extraction efficiency, we investigated several extraction parameters, such as extraction time and microwave power. In addition, to demonstrate the feasibility of the method, oil obtained under optimal conditions by method (MAE) was compared quantitatively and qualitatively with that obtained by the conventional method. After 1h of microwave-assisted extraction (power of 600W), an oil yield of 9.8% with identified lauric acid content of 22.7%. In comparison, an extended extraction of up to 4h was required to obtain a 9.7% yield of oil extraction with 21.2% of lauric acid content. The change in microwave power impacts the fatty acids profile and also the quality parameters of Laurel Oil. It was found that the profile of fatty acids changed with the power, where the lauric acid content increased from 22.7% at 600W to 30.5% at 1200W owing to a decrease of oleic acid content from 32.8% at 600W to 28.3% at 1200W and linoleic acid content from 22.3% at 600W to 20.6% at 1200W. In addition, we observed a decrease in oil yield from 9.8% at 600W to 5.1% at 1200W. Summarily, the overall results indicated that the extraction of laurel fruit oils could be successfully performed using (MAE) at a short extraction time and lower energy compared with the fixed oil obtained by conventional processes of extraction. Microwave heating exerted more aggressive effects on the oil. Indeed, microwave heating inflicted changes in the fatty acids profile of oil; the most affected fraction was the unsaturated fatty acids, with higher susceptibility to oxidation.

Keywords: microwaves, extraction, Laurel oil, solvent-free

Procedia PDF Downloads 65

Authors: Mahsa Shafaei Bajestani, Mahmoud Yazdani, Aliakbar Golshani

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Artificial lightweight aggregates have a wide range of applications in industry and engineering. Nowadays, the usage of this material in geotechnical activities, especially as backfill in retaining walls has been growing due to the specific characteristics which make it a competent alternative to the conventional geotechnical materials. In practice, a material with lower weight but higher shear strength parameters would be ideal as backfill behind retaining walls because of the important roles that these parameters play in decreasing the overall active lateral earth pressure. In this study, two types of Light Expanded Clay Aggregates (LECA) produced in the Leca factory are investigated. LECA is made in a rotary kiln by heating natural clay at different temperatures up to 1200 °C making quasi-spherical aggregates with different sizes ranged from 0 to 25 mm. The loose bulk density of these aggregates is between 300 and 700 kN/m³. The purpose of this research is to determine the stress-strain behavior, shear strength parameters, and the energy absorption of LECA materials. Direct shear tests were conducted at five normal stresses of 25, 50, 75, 100, and 200 kPa. In addition, conventional triaxial compression tests were operated at confining pressures of 50, 100, and 200 kPa to examine stress-strain behavior. The experimental results show a high internal angle of friction and even a considerable amount of nominal cohesion despite the granular structure of LECA. These desirable properties along with the intrinsic low density of these aggregates make LECA as a very proper material in geotechnical applications. Furthermore, the results demonstrate that lightweight aggregates may have high energy absorption that is excellent alternative material in seismic isolations.

Keywords: expanded clay, direct shear test, triaxial test, shear properties, energy absorption

Procedia PDF Downloads 164

Authors: A. N. Ghafar, O. A. Chaudhari, W. Oettel, P. Fontana

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This study is a part of the EU project GEOCOND-Advanced materials and processes to improve performance and cost-efficiency of shallow geothermal systems and underground thermal storage. In heat exchange boreholes, to improve the heat transfer between the pipes and the surrounding ground, the space between the pipes and the borehole wall is normally filled with geothermal grout. Traditionally, bentonite has been a crucial component in most commercially available geothermal grouts to assure the required stability and impermeability. The investigations conducted in the early stage of this project during the benchmarking tests on some commercial grouts showed considerable sensitivity of the rheological properties of the tested grouts to the mixing parameters, i.e., mixing time and velocity. Further studies on this matter showed that bentonite, which has been one of the important constituents in most grout mixes, was probably responsible for such behavior. Apparently, proper amount of shear should be applied during the mixing process to sufficiently activate the bentonite. The higher the amount of applied shear the more the activation of bentonite, resulting in change in the grout rheology. This explains why, occasionally in the field applications, the flow properties of the commercially available geothermal grouts using different mixing conditions (mixer type, mixing time, mixing velocity) are completely different than expected. A series of tests were conducted on the grout mixes, with and without bentonite, using different mixing protocols. The aim was to eliminate/reduce the sensitivity of the rheological properties of the geothermal grouts to the mixing parameters by replacing bentonite with polymeric (non-clay) stabilizers. The results showed that by replacing bentonite with a proper polymeric stabilizer, the sensitivity of the grout mix on mixing time and velocity was to a great extent diminished. This can be considered as an alternative for the developers/producers of geothermal grouts to provide enhanced materials with less uncertainty in obtained results in the field applications.

Keywords: flow properties, geothermal grout, mixing time, mixing velocity, rheological properties

Procedia PDF Downloads 123

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 70

Authors: A. Monfared, S. Faghihi

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Metallic glasses (MGs) are newcomers in the field of metals that show great potential for soft and bone tissue engineering due to the amorphous structure that endows unique properties. Up to now, various MGs based on Ti, Zr, Mg, Zn, Fe, Ca, and Sr in the form of a ribbon, bulk, thin-film, and powder have been investigated for biomedical purposes. This article reviews the compositions and biomedical properties of MGs as well as analyzes results in order to guide new approaches and future development of MGs.

Keywords: metallic glasses, biomaterials, biocompatibility, biocorrosion

Procedia PDF Downloads 211

Authors: Emna Hlel, Salma Jamousi, Abdelmajid Ben Hamadou

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Every model for knowledge representation to model real-world applications must be able to cope with the effects of uncertain phenomena. One of main defects of classical ontology is its inability to represent and reason with uncertainty. To remedy this defect, we try to propose a method to construct probabilistic ontology for integrating uncertain information in an ontology modeling a set of basic publications DBLP (Digital Bibliography & Library Project) using a probabilistic model.

Keywords: classical ontology, probabilistic ontology, uncertainty, Bayesian network

Procedia PDF Downloads 345

Authors: Sherif Keddis, Mohamed Azzam, Norbert Schwesinger

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Harvesting mechanical energy from footsteps or other impacts is a possibility to enable wireless autonomous sensor nodes. These can be used for a highly efficient control of connected devices such as lights, security systems, air conditioning systems or other smart home applications. They can also be used for accurate location or occupancy monitoring. Converting the mechanical energy into useful electrical energy can be achieved using the piezoelectric effect offering simple harvesting setups and low deflections. The challenge facing piezoelectric transducers is the achievable amount of energy per impact in the lower mJ range and the management of such low energies. Simple setups for energy extraction such as a full wave bridge connected directly to a capacitor are problematic due to the mismatch between high impedance sources and low impedance storage elements. Efficient energy circuits for piezoelectric harvesters are commonly designed for vibration harvesters and require periodic input energies with predictable frequencies. Due to the sporadic nature of impact harvesters, such circuits are not well suited. This paper presents a self-powered circuit that avoids the impedance mismatch during energy extraction by disconnecting the load until the source reaches its charge peak. The switch is implemented with passive components and works independent from the input frequency. Therefore, this circuit is suited for impact harvesting and sporadic inputs. For the same input energy, this circuit stores 150% of the energy in comparison to a directly connected capacitor to a bridge rectifier. The total efficiency, defined as the ratio of stored energy on a capacitor to available energy measured across a matched resistive load, is 63%. Although the resulting energy is already sufficient to power certain autonomous applications, further optimization of the circuit are still under investigation in order to improve the overall efficiency.

Keywords: autonomous sensors, circuit design, energy harvesting, energy management, impact harvester, piezoelectricity

Procedia PDF Downloads 152

Authors: Naveen Kumar Perumalla, Devadas Kuna, Mohammed Akhter Ali

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Satellite Navigation System such as the United States Global Positioning System (GPS) plays a significant role in determining the user position. Similar to that of GPS, Indian Regional Navigation Satellite System (IRNSS) is a Satellite Navigation System indigenously developed by Indian Space Research Organization (ISRO), India, to meet the country’s navigation applications. This system is also known as Navigation with Indian Constellation (NavIC). The NavIC system’s main objective, is to offer Positioning, Navigation and Timing (PNT) services to users in its two service areas i.e., covering the Indian landmass and the Indian Ocean. Six NavIC satellites are already deployed in the space and their receivers are in the performance evaluation stage. Four NavIC dual frequency receivers are installed in the ‘Advanced GNSS Research Laboratory’ (AGRL) in the Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, India. The NavIC receivers can be operated in two positioning modes: Stand-alone IRNSS and Hybrid (IRNSS+GPS) modes. In this paper, analysis of various parameters such as Dilution of Precision (DoP), three Dimension (3D) Root Mean Square (RMS) Position Error and Horizontal Position Error with respect to Visibility of Satellites is being carried out using the real-time IRNSS data, obtained by operating the receiver in both positioning modes. Two typical days (6th July 2017 and 7th July 2017) are considered for Hyderabad (Latitude-17°24'28.07’N, Longitude-78°31'4.26’E) station are analyzed. It is found that with respect to the considered parameters, the Hybrid mode operation of NavIC receiver is giving better results than that of the standalone positioning mode. This work finds application in development of NavIC receivers for civilian navigation applications.

Keywords: DoP, GPS, IRNSS, GNSS, position error, satellite visibility

Procedia PDF Downloads 210

Authors: Mohammed Tahir, Jonas Lagergren

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Vancron 40, a nitrided powder metallurgical tool Steel, is used in cold work applications where the predominant failure mechanisms are adhesive wear or galling. Typical applications of Vancron 40 are among others fine blanking, cold extrusion, deep drawing and cold work rolls for cluster mills. Vancron 40 positive results for cold work rolls for cluster mills and as a tool for some severe metal forming process makes it competitive compared to other type of work rolls that require higher precision, among others in cold rolling of thin stainless steel, which required high surface finish quality. In this project, three roll materials for cold rolling of stainless steel strip was examined, Vancron 40, Narva 12B (a high-carbon, high-chromium tool steel alloyed with tungsten) and Supra 3 (a Chromium-molybdenum tungsten-vanadium alloyed high speed steel). The purpose of this project was to study the depth profiles of the ironed stainless steel strips, emergence of galling and to study the lubrication performance used by steel industries. Laboratory experiments were conducted to examine scratch of the strip, galling and surface roughness of the roll materials under severe tribological conditions. The critical sliding length for onset of galling was estimated for stainless steel with four different lubricants. Laboratory experiments result of performance evaluation of resistance capability of rolls toward adhesive wear under severe conditions for low and high reductions. Vancron 40 in combination with cold rolling lubricant gave good surface quality, prevents galling of metal surfaces and good bearing capacity.

Keywords: Vancron 40, cold rolling, adhesive wear, galling, surface finish, lubricant, stainless steel

Procedia PDF Downloads 527

Authors: Doru Anastasiu Popescu, Raducanu Dragos Ionut

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In this paper the presentation of a tool which measures the similarity between two websites is made. The websites are compound only from webpages created with HTML. The tool uses three ways of calculating the similarity between two websites based on certain results already published. The first way compares all the webpages within a website, the second way compares a webpage with all the pages within the second website and the third way compares two webpages. Java programming language and technologies such as spring, Jsoup, log4j were used for the implementation of the tool.

Keywords: Java, Jsoup, HTM, spring

Procedia PDF Downloads 384

Authors: Weihao Wang, Zhulin Zong

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Two-dimensional ordered statistical constant false alarm detection is a widely used method for detecting weak target signals in radar signal processing applications. The method is based on analyzing the statistical characteristics of the noise and clutter present in the radar signal and then using this information to set an appropriate detection threshold. In this approach, the reference cell of the unit to be detected is divided into several reference subunits. These subunits are used to estimate the noise level and adjust the detection threshold, with the aim of minimizing the false alarm rate. By using an ordered statistical approach, the method is able to effectively suppress the influence of clutter and noise, resulting in a low false alarm rate. The detection process involves a number of steps, including filtering the input radar signal to remove any noise or clutter, estimating the noise level based on the statistical characteristics of the reference subunits, and finally, setting the detection threshold based on the estimated noise level. One of the main advantages of two-dimensional ordered statistical constant false alarm detection is its ability to detect weak target signals in the presence of strong clutter and noise. This is achieved by carefully analyzing the statistical properties of the signal and using an ordered statistical approach to estimate the noise level and adjust the detection threshold. In conclusion, two-dimensional ordered statistical constant false alarm detection is a powerful technique for detecting weak target signals in radar signal processing applications. By dividing the reference cell into several subunits and using an ordered statistical approach to estimate the noise level and adjust the detection threshold, this method is able to effectively suppress the influence of clutter and noise and maintain a low false alarm rate.

Keywords: two-dimensional, ordered statistical, constant false alarm, detection, weak target signals

Procedia PDF Downloads 77

Authors: B. S. Akhmetov, S. T. Akhmetova, A. I. Ivanov, T. S. Kartbayev, A. Y. Malygin

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The problem of training of a network of artificial neurons in biometric appendices is that this process has to be completely automatic, i.e. the person operator should not participate in it. Therefore, this article discusses the issues of training the network of artificial neurons and the description of the non-iterative learning algorithm of artificial neuron.

Keywords: artificial neuron, biometrics, biometrical applications, learning of neuron, non-iterative algorithm

Procedia PDF Downloads 491

Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

Procedia PDF Downloads 71

Authors: Hossam E. Emam

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Metal–organic frameworks (MOFs) are new hybrid materials investigated from 15 years ago; they synthesized from metals as inorganic center joined with multidentate organic linkers to form a 1D, 2D or 3D network structure. MOFs have unique properties such as pore crystalline structure, large surface area, chemical tenability and luminescent characters. These significant properties enable MOFs to be applied in many fields such like gas storage, adsorption/separation, drug delivery/biomedicine, catalysis, polymerization, magnetism and luminescence applications. Recently, many of published reports interested in superiority of MOFs for functionalization of textiles to exploit the unique properties of MOFs. Incorporation of MOFs is found to acquire the textiles some additional formidable functions to be used in considerable fields such like water treatment and fuel purification. Modification of textiles with MOFs could be easily performed by two main techniques; Ex-situ (preparation of MOFs then applied onto textiles) and in-situ (ingrowth of MOFs within textiles networks). Uniqueness of MOFs could be assimilated in acquirement of decorative color, antimicrobial character, anti-mosquitos character, ultraviolet radiation protective, self-clean, photo-luminescent and sensor character. Additionally, textiles treatment with MOFs make it applicable as filter in the adsorption of toxic gases, hazardous materials (such as pesticides, dyes and aromatics molecules) and fuel purification (such as removal of oxygenated, nitrogenated and sulfur compounds). Also, the porous structure of MOFs make it mostly utilized in control release of insecticides from the surface of the textile. Moreover, MOF@textiles as recyclable materials lead it applicable as photo-catalyst composites for photo-degradation of different dyes in the day light. Therefore, MOFs is extensively considered for imparting textiles with formidable properties as ingeniousness way for textile functionalization.

Keywords: MOF, functional textiles, water treatment, fuel purification, environmental applications

Procedia PDF Downloads 144

Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 72

Authors: Ertuğ Yıldırım, Dile Kara, Salih Zeki Yıldız

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Metal containing polymers (MCPs) are macro molecules usually containing metal-ligand coordination units and are a multidisciplinary research field mainly based at the interface between coordination chemistry and polymer science. The progress of this area has also been reinforced by the growth of several other closely related disciplines including macro molecular engineering, crystal engineering, organic synthesis, supra molecular chemistry and colloidal and material science. Schiff base ligands are very effective in constructing supra molecular architectures such as coordination polymers, double helical and triple helical complexes. In addition, Schiff base derivatives incorporating a fluorescent moiety are appealing tools for optical sensing of metal ions. MCPs are well-known systems in which the combinations of local parameters are possible by means of fluoro metric techniques. Generally, without incorporation of the fluorescent groups with polymers is unspecific, and it is not useful to analyze their fluorescent properties. Therefore, it is necessary to prepare a new type epoxy polymers with fluorescent groups in terms of metal sensing prop and the other photo chemical applications. In the present study metal containing polymers were prepared via poly functional monomeric Schiff base metal chelate complexes in the presence of dis functional monomers such as diglycidyl ether Bisphenol A (DGEBA). The synthesized complexes and polymers were characterized by FTIR, UV-VIS and mass spectroscopies. The preparations of epoxy polymers have been carried out at 185 °C. The prepared composites having sharp and narrow excitation/emission properties are expected to be applicable in various systems such as heat-resistant polymers and photo voltaic devices. The prepared composite is also ideal for various applications, easily prepared, safe, and maintain good fluorescence properties.

Keywords: Schiff base ligands, crystal engineering, fluorescence properties, Metal Containing Polymers (MCPs)

Procedia PDF Downloads 345

Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

Procedia PDF Downloads 41

Authors: Bahieddine. Bouabdellah, Benameur. Amiri, Abdelkader.nouri

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Europium-doped gallium nitride (EuGaN) is a promising material for optoelectronic and thermoelectric devices. This study investigates its optical properties using density functional theory (DFT) with the FP-LAPW method and MBJ+U correction. The simulation substitutes a gallium atom with europium in a hexagonal GaN lattice (6% doping). Distinct absorption peaks are observed in the optical analysis. These results highlight EuGaN's potential for various applications and pave the way for further research on rare earth-doped materials.

Keywords: eugan, fp-lapw, dft, wien2k, mbj hubbard

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Authors: Sogand Barghi

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The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: Hummera Rafique, Aamer Saeed

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Thioureas are highly biologically active compounds, as many important applications are associated with this nucleus. They serve as exceptionally versatile building block for the synthesis of wide variety of heterocyclic systems, which also possess extensive range of bioactivities. These thioureas were converted into five-membered heterocycles with imino moiety like ethyl 4-[2-benzamido-4-methylthiazol-3(2H)-yl)]benzoates (2a-j) by base catalyzed cyclization of corresponding thioureas with 2-bromoacetone and triethylamine in good yields.

Keywords: ethyl 4-[2-benzamido-4-methylthiazol-3(2H)-yl)]benzoates, ethyl 4-(3-benzoylthioureido) benzoates, antibacterial activity

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Authors: Sara Mosallanejad, Bogdan Z. Dlugogorski, Jeff Gore, Mohammednoor Altarawneh

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Ammonium nitrate (NH­₄NO₃, AN) is commonly used as the main component of AN emulsion and fuel oil (ANFO) explosives, that use extensively in civilian and mining operations for underground development and tunneling applications. The emulsion formulation and wettability of AN prills, which affect the physical stability and detonation of ANFO, highly depend on the surface tension, density, viscosity of the used liquid. Therefore, for engineering applications of this material, the determination of density and surface tension of concentrated aqueous solutions of AN is essential. The molecular dynamics (MD) simulation method have been used to investigate the density and the surface tension of high concentrated ammonium nitrate solutions; up to its solubility limit in water. Non-polarisable models for water and ions have carried out the simulations, and the electronic continuum correction model (ECC) uses a scaling of the charges of the ions to apply the polarisation implicitly into the non-polarisable model. The results of calculated density and the surface tension of the solutions have been compared to available experimental values. Our MD simulations show that the non-polarisable model with full-charge ions overestimates the experimental results while the reduce-charge model for the ions fits very well with the experimental data. Ions in the solutions show repulsion from the interface using the non-polarisable force fields. However, when charges of the ions in the original model are scaled in line with the scaling factor of the ECC model, the ions create a double ionic layer near the interface by the migration of anions toward the interface while cations stay in the bulk of the solutions. Similar ions orientations near the interface were observed when polarisable models were used in simulations. In conclusion, applying the ECC model to the non-polarisable force field yields the density and surface tension of the AN solutions with high accuracy in comparison to the experimental measurements.

Keywords: ammonium nitrate, electronic continuum correction, non-polarisable force field, surface tension

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Authors: Daniel F. Sava, Anton Ficai, Bogdan S. Vasile, Georgeta Voicu, Ecaterina Andronescu

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The energy crisis and environmental contamination are very serious problems, therefore searching for better and sustainable renewable energy is a must. It is predicted that the global energy demand will double until 2050. Solar water splitting and photocatalysis are considered as one of the solutions to these issues. The use of oxide semiconductors for solar water splitting and photocatalysis started in 1972 with the experiments of Fujishima and Honda on TiO2 electrodes. Since then, the evolution of nanoscience and characterization methods leads to a better control of size, shape and properties of materials. Although the past decade advancements are astonishing, for these applications the properties have to be controlled at a much finer level, allowing the control of charge-carrier lives, energy level positions, charge trapping centers, etc. Graphene has attracted a lot of attention, since its discovery in 2004, due to the excellent electrical, optical, mechanical and thermal properties that it possesses. These properties make it an ideal support for photocatalysts, thus graphene composites with oxide semiconductors are of great interest. We present in this work the synthesis and characterization of graphene-related materials and oxide semiconductors and their different composites. These materials can be used in constructing devices for different applications (batteries, water splitting devices, solar cells, etc), thus showing their application flexibility. The synthesized materials are different morphologies and sizes of TiO2, ZnO and Fe2O3 that are obtained through hydrothermal, sol-gel methods and graphene oxide which is synthesized through a modified Hummer method and reduced with different agents. Graphene oxide and the reduced form could also be used as a single material for transparent conductive films. The obtained single materials and composites were characterized through several methods: XRD, SEM, TEM, IR spectroscopy, RAMAN, XPS and BET adsorption/desorption isotherms. From the results, we see the variation of the properties with the variation of synthesis parameters, size and morphology of the particles.

Keywords: composites, graphene, hydrothermal, renewable energy

Procedia PDF Downloads 497

Authors: Leticia Leandro Rade, Amanda Silva de Sousa, Suman Das, Wesley Generoso, Mayara Chagas Ávila, Plinio Salmazo Vieira, Antonio Bonomi, Gabriela Persinoti, Mario Tyago Murakami, Thomas Michael Makris, Leticia Maria Zanphorlin

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Alkenes have an economic appeal, especially in the biofuels field, since they are precursors for drop-in biofuels production, which have similar chemical and physical properties to the conventional fossil fuels, with no oxygen in their composition. After the discovery of the first P450 CYP152 OleTJE in 2011, reported with its unique property of decarboxylating fatty acids (FA), by using hydrogen peroxide as a cofactor and producing 1-alkenes as the main product, the scientific and technological interest in this family of enzymes vastly increased. In this context, the present work presents a new decarboxylase (OleTRN) with low similarity with OleTJE (32%), its biochemical characterization, and structure elucidation. As main results, OleTRN presented a high yield of expression and purity, optimum reaction conditions at 35 °C and pH from 6.5 to 8.0, and higher specificity for oleic acid. Besides that, structure-guided mutations were performed and according to the functional characterizations, it was observed that some mutations presented different specificity and chemoselectivity by varying the chain-length of FA substrates from 12 to 20 carbons. These results are extremely interesting from a biotechnological perspective as those characteristics could diversify the applications and contribute to designing better cytochrome P450 decarboxylases. Considering that peroxygenases have the potential activity of decarboxylating and hydroxylating fatty acids and that the elucidation of the intriguing mechanistic involved in the decarboxylation preferential from OleTJE is still a challenge, the elucidation of OleTRN structure and the functional characterizations of OleTRN and its mutants contribute to new information about CYP152. Besides that, the work also contributed to the discovery of a new decarboxylase with a different selectivity profile from OleTJE, which allows a wide range of applications.

Keywords: P450, decarboxylases, alkenes, biofuels

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Jeet Shende, Leonid Shpanin, Misko Abramiuk, Mattew Goodwin, Nicholas Pickett

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Energy harvesting on board the Unmanned Ariel Vehicle (UAV) is one of the most rapidly growing emerging technologies and consists of the collection of small amounts of energy, for different applications, from unconventional sources that are incidental to the operation of the parent system or device. Different energy harvesting techniques have already been investigated in the multirotor drones, where the energy collected comes from the systems surrounding ambient environment and typically involves the conversion of solar, kinetic, or thermal energies into electrical energy. The energy harvesting from the vibrated propeller using the piezoelectric components inside the propeller has also been proven to be feasible. However, the impact on the UAV flight performance using this technology has not been investigated. In this contribution the impact on the multirotor drone operation has been investigated at different flight control configurations which support the efficient performance of the propeller vibration energy harvesting. The industrially made MANTIS X8-PRO octocopter frame kit was used to explore the octocopter operation which was modelled using SolidWorks 3D CAD package for simulation studies. The octocopter flight control strategy is developed through integration of the SolidWorks 3D CAD software and MATLAB/Simulink simulation environment for evaluation of the octocopter behaviour under different simulated flight modes and octocopter geometries. Analysis of the two modelled octocopter geometries and their flight performance is presented via graphical representation of simulated parameters. The possibility of not using the landing gear in octocopter geometry is demonstrated. The conducted study evaluates the octocopter’s flight control technique and its impact on the energy harvesting mechanism developed on board the octocopter. Finite Element Analysis (FEA) simulation results of the modelled octocopter in operation are presented exploring the performance of the octocopter flight control and structural configurations. Applications of both octocopter structures and their flight control strategy are discussed.

Keywords: energy harvesting, flight control modelling, object modeling, unmanned aerial vehicle

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Authors: Navneet Kaur, S. D. Tiwari

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Superparamagnetism is an interesting phenomenon and observed in small particles of magnetic materials. It arises due to a reduction in particle size. In the superparamagnetic state, as the thermal energy overcomes magnetic anisotropy energy, the magnetic moment vector of particles flip their magnetization direction between states of minimum energy. Superparamagnetic nanoparticles have been attracting the researchers due to many applications such as information storage, magnetic resonance imaging, biomedical applications, and sensors. For information storage, thermal fluctuations lead to loss of data. So that nanoparticles should have high blocking temperature. And to achieve this, nanoparticles should have a higher magnetic moment and magnetic anisotropy constant. In this work, the magnetic anisotropy constant of the antiferromagnetic nanoparticles system is determined. Magnetic studies on nanoparticles of NiO (nickel oxide) are reported well. This antiferromagnetic nanoparticle system has high blocking temperature and magnetic anisotropy constant of order 105 J/m3. The magnetic study of NiO nanoparticles in the superparamagnetic region is presented. NiO particles of two different sizes, i.e., 6 and 8 nm, are synthesized using the chemical route. These particles are characterized by an x-ray diffractometer, transmission electron microscope, and superconducting quantum interference device magnetometry. The magnetization vs. applied magnetic field and temperature data for both samples confirm their superparamagnetic nature. The blocking temperature for 6 and 8 nm particles is found to be 200 and 172 K, respectively. Magnetization vs. applied magnetic field data of NiO is fitted to an appropriate magnetic expression using a non-linear least square fit method. The role of particle size distribution and magnetic anisotropy is taken in to account in magnetization expression. The source code is written in Python programming language. This fitting provides us the magnetic anisotropy constant for NiO and other magnetic fit parameters. The particle size distribution estimated matches well with the transmission electron micrograph. The value of magnetic anisotropy constants for 6 and 8 nm particles is found to be 1.42 X 105 and 1.20 X 105 J/m3, respectively. The obtained magnetic fit parameters are verified using the Neel model. It is concluded that the effect of magnetic anisotropy should not be ignored while studying the magnetization process of nanoparticles.

Keywords: anisotropy, superparamagnetic, nanoparticle, magnetization

Procedia PDF Downloads 131