Search results for: IoT applications

Authors: Malo Pocheau-Lesteven, Olivier Le Maître

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Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.

Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program

Procedia PDF Downloads 157

Authors: Nikolina Ribarić

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E-learning is most commonly defined as a set of applications and processes, such as Web-based learning, computer-based learning, virtual classrooms, and digital collaboration, that enable access to instructional content through a variety of electronic media. The main goal of an e-learning system is learning, and the way to evaluate the impact of an e-learning system is by examining whether students learn effectively with the help of that system. Testmoz is a program for online preparation of knowledge evaluation assignments. The program provides teachers with computer support during the design of assignments and evaluating them. Students can review and solve assignments and also check the correctness of their solutions. Research into the increase of motivation by the practice of providing teaching content by applying online tests prepared in the Testmoz program was carried out with students of the 8th grade of Ljubo Babić Primary School in Jastrebarsko. The students took the tests in their free time, from home, for an unlimited number of times. SPSS was used to process the data obtained by the research instruments. The results of the research showed that students preferred to practice teaching content and achieved better educational results in chemistry when they had access to online tests for repetition and practicing in relation to subject content which was checked after repetition and practicing in "the classical way" -i.e., solving assignments in a workbook or writing assignments in worksheets.

Keywords: chemistry class, e-learning, motivation, Testmoz

Procedia PDF Downloads 160

Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of machine learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. With the application of learning methods in such diverse domains, artificial intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been on developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and three defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt machine learning techniques in security-critical areas such as the nuclear industry without rigorous testing since they may be vulnerable to adversarial attacks. While common defence methods can effectively defend against different attacks, none of the three considered can provide protection against all five adversarial attacks analysed.

Keywords: adversarial machine learning, attacks, defences, nuclear industry, crack detection

Procedia PDF Downloads 158

Authors: Rashad Al-Gaashani, Viktor Kochkodan, Jenny Lawler

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Physico-chemical activation method wasused to produce high-quality activated carbon (AC) with a large surface area of about 2000 m2/g from low-cost and abundant biomasswastes in Qatar, namely date seeds. X-Ray diffraction (XRD), scanning electron spectroscopy (SEM), energy dispersive X-Ray spectroscopy (EDS), and Brunauer-Emmett-Teller (BET) surface area analysis were used to evaluate the AC samples. AC produced from date seeds have a wide range of the pores available, including micro- andnano-pores. This type of AC with a well-developed pore structure may be very attractive for different applications, including air and water purification from micro and nano pollutants. Heavy metalsiron (III) and copper (II) ions were removed from wastewater using the AC producedusinga batch adsorption technique. The AC produced from date seeds biomass wastes show high removal of heavy metals such as iron (III) ions (100%) and copper (II) ions (97.25%). The highest removal of copper (II) ions (100%) with AC produced from date seeds was found at pH 8, whereas the lowest removal (22.63%) occurred at pH 2. The effect of adsorption time, adsorbent dose, pH on the removal of heavy metalswere studied.

Keywords: activated carbon, date seeds, biomass, heavy metals removal, water treatment

Procedia PDF Downloads 102

Authors: Yu Zhang, Il Kim

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Polypeptides with defined peptide sequences illustrate the power of remarkable applications in drug delivery, tissue engineering, sensing and catalysis. Especially the cyclic polypeptides, the distinctive topological architecture imparts many characteristic properties comparing to linear polypeptides. Here, a facile and highly efficient strategy for the synthesis of linear and cyclic polypeptides is reported using N-heterocyclic carbenes (NHCs)-mediated ring-opening polymerization (ROP) of α-amino acid N-carboxyanhydrides (NCA) in the presence or absence of primary amine initiator. The polymerization proceeds rapidly in a quasi-living manner, allowing access to linear and cyclic polypeptides of well-defined chain length and narrow polydispersity, as evidenced by nuclear magnetic resonance spectrum (1H NMR and 13C NMR spectra) and size exclusion chromatography (SEC) analysis. The cyclic architecture of the polypeptides was further verified by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectra (MALDI-TOF MS) and electrospray ionization (ESI) mass spectra, as well as viscosity studies. This approach can also simplify workup procedures and make bulk scale synthesis possible, which thereby opens avenues for practical uses in diverse areas, opening up the new generation of polypeptide synthesis.

Keywords: α-amino acid N-carboxyanhydrides, living polymerization, polypeptides, N-heterocyclic carbenes, ring-opening polymerization

Procedia PDF Downloads 167

Authors: Andreas Kornmaaler Hansen, Carlos Gomez Cubero, Elizabeth Jochum

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The use of the arts in therapy and rehabilitation is well established, and there is growing recognition of the value of the arts for improving health and well-being across diverse populations. Combining arts with socially assistive robots is a relatively under-explored research area. This paper presents the results of a feasibility study conducted within an existing arts and health program to scope the possibility of combining visual arts with socially assistive robots to promote mental health and well-being. Using a participatory research design with participant-led perspectives, we present the results of our feasibility study with a collaborative drawing robot among an adult population with mild to severe mental illness. We identify key methodological challenges and advantages of working with participatory and human-centered approaches. Based on the results of three pilot workshops with participants and lay health workers, we outline suggestions for authentic engagement with real stakeholders toward the development of socially assistive robots in community health contexts. Working closely with a patient population at all levels of the research process is key for developing tools and interventions that center patient experience and priorities while minimizing the risks of alienating patients and communities.

Keywords: arts and health, visual art, health promotion, mental health, collaborative robots, creativity, socially assistive robots

Procedia PDF Downloads 64

Authors: Bethany Wood

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The world pandemic led to a change in the way social media was used by audiences, with young people spending more hours on the platform due to lockdown. Reports by Ofcom have demonstrated that the internet is the second most popular platform for accessing news after television in the UK with social media and the internet ranked as the most popular platform to access news for those aged between 16-24. These statistics are unsurprising considering that at the time of writing, 98 percent of Generation Z (Gen Z) owned a smartphone and the subsequent ease and accessibility of social media. Technology is constantly developing and with this, its importance is becoming more prevalent with each generation: the Baby Boomers (1946-1964) consider it something useful whereas millennials (1981-1997) believe it a necessity for day to day living. Gen Z, otherwise known as the digital native, have grown up with this technology at their fingertips and social media is a norm. It helps form their identity, their affiliations and opens gateways for them to engage with news in a new way. It is a common misconception that Gen Z do not consume news, they are simply doing so in a different way to their predecessors. Using a sample of 800 18-20 year olds whilst utilising Generational theory, Actor Network Theory and the Social Shaping of Technology, this research provides a critical analyse regarding how Gen Z’s news consumption and engagement habits are developing along with technology to sculpture the future format of news and its distribution. From that perspective, allied with the empirical approach, it is possible to provide research orientated advice for the industry and even help to redefine traditional concepts of journalism.

Keywords: journalism, generation z, digital, social media

Procedia PDF Downloads 86

Authors: Asal Pournaghshband, Roham Maher

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This study presents a numerical analysis of the cyclic behavior of H-shaped steel columns, focusing on different steel grades, including austenitic, ferritic, duplex stainless steel, and carbon steel. Finite Element (FE) models were developed and validated against experimental data, demonstrating a predictive accuracy of up to 6.5%. The study examined key parameters such as energy dissipation and failure modes. Results indicate that duplex stainless steel offers the highest strength, with superior energy dissipation but a tendency for brittle failure at maximum strains of 0.149. Austenitic stainless steel demonstrated balanced performance with excellent ductility and energy dissipation, showing a maximum strain of 0.122, making it highly suitable for seismic applications. Ferritic stainless steel, while stronger than carbon steel, exhibited reduced ductility and energy absorption. Carbon steel displayed the lowest performance in terms of energy dissipation and ductility, with significant strain concentrations leading to earlier failure. These findings provide critical insights into optimizing material selection for earthquake-resistant structures, balancing strength, ductility, and energy dissipation under seismic conditions.

Keywords: energy dissipation, finite element analysis, H-shaped columns, seismic performance, stainless steel grades

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Authors: Guijie Sang, Geo Davis, Momchil Terziev

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Geological CO2 storage has been regarded as a promising strategy to mitigate the emission of greenhouse gas generated from traditional power stations and energy-intensive industry. Caprocks with very low permeability and ultra-fine pores create viscous and capillary barriers to guarantee CO2 sealing efficiency. However, caprock fractures, either naturally existing or artificially induced due to injection, could provide preferential paths for CO₂ escaping. Seeking an efficient technique to seal and strengthen caprock fractures is crucial. We apply microbial-induced-calcite-precipitation (MICP) technique for sealing and strengthening caprock fractures in the laboratory scale. The MICP bio-grouting technique has several advantages over conventional cement grouting methods, including its low viscosity, micron-size microbes (accessible to fine apertures), and low carbon footprint, among others. Different injection strategies are tested to achieve relatively homogenous calcite precipitation along the fractures, which is monitored dynamically based on laser ultrasonic technique. The MICP process in caprock fractures, which integrates the coupled flow and bio-chemical precipitation, is also modeled and validated through the experiment. The study could provide an effective bio-mediated grouting strategy for caprock sealing and thus ensuring a long-term safe geological CO2 storage.

Keywords: caprock sealing, geological CO2 storage, grouting strategy, microbial induced calcite precipitation

Procedia PDF Downloads 189

Authors: Diogo Ferreira Lima Filho, José Roberto Amazonas

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Nanotechnology opens the door to new paradigms that introduces a variety of novel tools enabling a plethora of potential applications in the biomedical, industrial, environmental, and military fields. This work proposes an integrated node model by applying the same concepts of TCNet to networks of nanodevices where the nodes are cooperatively interconnected with a low-complexity Mealy Machine (MM) topology integrating in the same electronic system the modules necessary for independent operation in wireless sensor networks (WSNs), consisting of Rectennas (RF to DC power converters), Code Generators based on Finite State Machine (FSM) & Trellis Decoder and On-chip Transmit/Receive with autonomy in terms of energy sources applying the Energy Harvesting technique. This approach considers the use of a Graphene Composite Substrate (GCS) for the integrated electronic circuits meeting the following characteristics: mechanical flexibility, miniaturization, and optical transparency, besides being ecological. In addition, graphene consists of a layer of carbon atoms with the configuration of a honeycomb crystal lattice, which has attracted the attention of the scientific community due to its unique Electrical Characteristics.

Keywords: composite substrate, energy harvesting, finite state machine, graphene, nanotechnology, rectennas, wireless sensor networks

Procedia PDF Downloads 106

Authors: M. D. H. Beg, John O. Akindoyo, Suriati Ghazali, Nitthiyah Jeyaratnam

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Over the period of time, several approaches have been undertaken to mitigate the challenges associated with bone regeneration. This includes but not limited to xenografts, allografts, autografts as well as artificial substitutions like bioceramics, synthetic cements and metals. The former three techniques often come along with peculiar limitation and problems such as morbidity, availability, disease transmission, collateral site damage or absolute rejection by the body as the case may be. Synthetic routes remain the only feasible alternative option for treatment of bone defects. Hydroxyapatite (HA) is very compatible and suitable for this application. However, most of the common methods for HA synthesis are either expensive, complicated or environmentally unfriendly. Interestingly, extraction of HA from bio-wastes have been perceived not only to be cost effective, but also environment friendly. In this research, HA was synthesized from bio-waste: namely bovine bones through three different methods which are hydrothermal chemical processes, ultrasound assisted synthesis and ordinary calcination techniques. Structure and property analysis of the HA was carried out through different characterization techniques such as TGA, FTIR, and XRD. All the methods applied were able to produce HA with similar compositional properties to biomaterials found in human calcified tissues. Calcination process was however observed to be more efficient as it eliminated all the organic components from the produced HA. The HA synthesized is unique for its minimal cost and environmental friendliness. It is also perceived to be suitable for tissue and bone engineering applications.

Keywords: hydroxyapatite, bone, calcination, biowaste

Procedia PDF Downloads 249

Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

Procedia PDF Downloads 133

Authors: C. Fernanda da S. Sampaio, M. Gabriela Sadith Perez Paredes, V. Antonio de O. Martins

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Today the transport sector is undergoing a revolution, with the rise of Advanced Driver Assistance Systems (ADAS), industry and society itself will undergo a major transformation. However, the technological development of these applications is a challenge that requires new techniques and great machine learning and artificial intelligence. The study proposes to develop a vehicular perception system called Veibra, which consists of two front cameras for day/night viewing and an embedded device capable of working with Yolov2 image processing algorithms with low computational cost. The strategic version for the market is to assist the driver on the road with the detection of day/night objects, such as road signs, pedestrians, and animals that will be viewed through the screen of the phone or tablet through an application. The system has the ability to perform real-time driver detection and recognition to identify muscle movements and pupils to determine if the driver is tired or inattentive, analyzing the student's characteristic change and following the subtle movements of the whole face and issuing alerts through beta waves to ensure the concentration and attention of the driver. The system will also be able to perform tracking and monitoring through GSM (Global System for Mobile Communications) technology and the cameras installed in the vehicle.

Keywords: advanced driver assistance systems, tracking, traffic signal detection, vehicle perception system

Procedia PDF Downloads 155

Authors: Jayanth C., Anirudh Sai Yetikuri, Kavitha S. N.

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This paper explores the development and application of computer vision and machine learning techniques for real-time pose detection, facial recognition, and number plate extraction. Utilizing MediaPipe for pose estimation, the research presents methods for detecting hand raises and ducking postures through real-time video analysis. Complementarily, facial recognition is employed to compare and verify individual identities using the face recognition library. Additionally, the paper demonstrates a robust approach for extracting and storing vehicle number plates from images, integrating Optical Character Recognition (OCR) with a database management system. The study highlights the effectiveness and versatility of these technologies in practical scenarios, including security and surveillance applications. The findings underscore the potential of combining computer vision techniques to address diverse challenges and enhance automated systems for both individual and vehicular identification. This research contributes to the fields of computer vision and machine learning by providing scalable solutions and demonstrating their applicability in real-world contexts.

Keywords: computer vision, pose detection, facial recognition, number plate extraction, machine learning, real-time analysis, OCR, database management

Procedia PDF Downloads 26

Authors: N. Rassoul-ibrahim, E. Siahmed, L. Tadrist

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Water management plays a crucial role in the performance and durability of PEM fuel cells. Whereas the membrane must be hydrated enough, liquid droplets formed by water in excess can block the flow in the gas distribution channels and hinder the fuel cell performance. The main purpose of this work is to increase the understanding of liquid transport and mixing through mini- or micro-channels for various engineering or medical process applications including cool-ing of equipment according to the operations considered. For that purpose and as a first step, a technique was devel-oped to automatically detect and characterize two-phase flow patterns that may appear in such. The investigation, mainly experimental, was conducted on transparent channel with a 1mm x 1mm square cross section and a 0.3mm x 0.3 mm water injection normal to the gas channel. Three main flow patterns were identified liquid slug, bubble flow and annular flow. A flow map has been built accord-ing to the flow rate of both phases. As a sample the follow-ing figures show representative images of the flow struc-tures observed. An analysis and discussion of the flow pattern, in mini-channel, will be provided and compared to the case old micro-channel. . Keywords: Two phase flow, Clean Energy, Minichannels, Fuel Cells. Flow patterns, Maps.

Keywords: two phase flox, T-juncion, Micro and minichannels, clean energy, flow patterns, maps

Procedia PDF Downloads 76

Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

Procedia PDF Downloads 30

Authors: Nicole Nazario Bayon, Prathima Prabhu Tumkur, Nithin Krisshna Gunasekaran, Krishnan Prabhakaran, Joseph C. Hall, Govindarajan T. Ramesh

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Titanium nitride (TiN) nanoparticles have sparked interest over the past decade due to their characteristics such as thermal stability, extreme hardness, low production cost, and similar optical properties to gold. In this study, TiN nanoparticles were synthesized via a thermal benzene route to obtain a black powder of nanoparticles. The final product was drop cast onto conductive carbon tape and sputter coated with gold/palladium at a thickness of 4 nm for characterization by field emission scanning electron microscopy (FE-SEM) with energy dispersive X-Ray spectroscopy (EDX) that revealed they were spherical. ImageJ software determined the average size of the TiN nanoparticles was 79 nm in diameter. EDX revealed the elements present in the sample and showed no impurities. Further characterization by X-ray diffraction (XRD) revealed characteristic peaks of cubic phase titanium nitride, and crystallite size was calculated to be 14 nm using the Debye-Scherrer method. Dynamic light scattering (DLS) analysis revealed the size and size distribution of the TiN nanoparticles, with average size being 154 nm. Zeta potential concluded the surface of the TiN nanoparticles is negatively charged. Biocompatibility studies using MTT(3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay showed TiN nanoparticles are not cytotoxic at low concentrations (2, 5, 10, 25, 50, 75 mcg/well), and cell viability began to decrease at a concentration of 100 mcg/well.

Keywords: biocompatibility, characterization, cytotoxicity, nanoparticles, synthesis, titanium nitride

Procedia PDF Downloads 178

Authors: Rangoli Goyal, Rama Bhargava

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The triple diffusive boundary layer flow of nanofluid under the action of constant magnetic field over a non-linear stretching sheet has been investigated numerically. The model includes the effect of Brownian motion, thermophoresis, and cross-diffusion; slip mechanisms which are primarily responsible for the enhancement of the convective features of nanofluid. The governing partial differential equations are transformed into a system of ordinary differential equations (by using group theory transformations) and solved numerically by using variational finite element method. The effects of various controlling parameters, such as the magnetic influence number, thermophoresis parameter, Brownian motion parameter, modified Dufour parameter, and Dufour solutal Lewis number, on the fluid flow as well as on heat and mass transfer coefficients (both of solute and nanofluid) are presented graphically and discussed quantitatively. The present study has industrial applications in aerodynamic extrusion of plastic sheets, coating and suspensions, melt spinning, hot rolling, wire drawing, glass-fibre production, and manufacture of polymer and rubber sheets, where the quality of the desired product depends on the stretching rate as well as external field including magnetic effects.

Keywords: FEM, thermophoresis, diffusiophoresis, Brownian motion

Procedia PDF Downloads 420

Authors: Nuur Ezaini Akmar Ismail, Norbazilah Rahim, Norul Huda Md Rasdi, Maslina Daud

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A web application is the most targeted by the attacker because the web application is accessible by the end users. It has become more advantageous to the attacker since not all the end users aware of what kind of sensitive data already leaked by them through the Internet especially via social network in shake on ‘sharing’. The attacker can use this information such as personal details, a favourite of artists, a favourite of actors or actress, music, politics, and medical records to customize phishing attack thus trick the user to click on malware-laced attachments. The Phishing attack is one of the most popular attacks for social engineering technique against web applications. There are several methods to detect phishing websites such as Blacklist/Whitelist based detection, heuristic-based, and visual similarity-based detection. This paper illustrated a comparison between the heuristic-based technique using features of a uniform resource locator (URL) and visual similarity-based detection techniques that compares the content of a suspected phishing page with the legitimate one in order to detect new phishing sites based on the paper reviewed from the past few years. The comparison focuses on three indicators which are false positive and negative, accuracy of the method, and time consumed to detect phishing website.

Keywords: heuristic-based technique, phishing detection, social engineering and visual similarity-based technique

Procedia PDF Downloads 177

Authors: Yun-Tao Zhang, Jong-Yeop Bae, Whoi-Yul Kim

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Background modeling and subtraction in video analysis has been widely proved to be an effective method for moving objects detection in many computer vision applications. Over the past years, a large number of approaches have been developed to tackle different types of challenges in this field. However, the dynamic background and illumination variations are two of the most frequently occurring issues in the practical situation. This paper presents a new two-layer model based on codebook algorithm incorporated with local binary pattern (LBP) texture measure, targeted for handling dynamic background and illumination variation problems. More specifically, the first layer is designed by block-based codebook combining with LBP histogram and mean values of RGB color channels. Because of the invariance of the LBP features with respect to monotonic gray-scale changes, this layer can produce block-wise detection results with considerable tolerance of illumination variations. The pixel-based codebook is employed to reinforce the precision from the outputs of the first layer which is to eliminate false positives further. As a result, the proposed approach can greatly promote the accuracy under the circumstances of dynamic background and illumination changes. Experimental results on several popular background subtraction datasets demonstrate a very competitive performance compared to previous models.

Keywords: background subtraction, codebook model, local binary pattern, dynamic background, illumination change

Procedia PDF Downloads 217

Authors: M. Dimitri, M. Ricci, F. Bigi, M. Romiti, A. Corvi

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Tissue engineering has reached a significant milestone with the integration of 3D printing for the creation of complex bioconstructs equipped with vascular networks, crucial for cell maintenance and growth. This study aims to demonstrate the effectiveness of a portable microperfusion system designed to adapt dynamically to the evolving conditions of cell growth within 3D-printed bioconstructs. The microperfusion system was developed to provide a constant and controlled flow of nutrients and oxygen through the integrated vessels in the bioconstruct, replicating in vivo physiological conditions. Through a series of preliminary experiments, we evaluated the system's ability to maintain a favorable environment for cell proliferation and differentiation. Measurements of cell density and viability were performed to monitor the health and functionality of the tissue over time. Preliminary results indicate that the portable microperfusion system not only supports but optimizes cell growth, effectively adapting to changes in metabolic needs during the bioconstruct maturation process. This research opens perspectives in tissue engineering, demonstrating that a portable microperfusion system can be successfully integrated into 3D-printed bioconstructs, promoting sustainable and uniform cell growth. The implications of this study are far-reaching, with potential applications in regenerative medicine and pharmacological research, providing a platform for the development of functional and complex tissues.

Keywords: biofabrication, microfluidic perfusion system, 4D bioprinting

Procedia PDF Downloads 32

Authors: Femy M. Makkar, S. Chandrakaran, N. Sankar

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The concept of reinforced earth has been used in the field of geotechnical engineering since 1960s, for many applications such as, construction of road and rail embankments, pavements, retaining walls, shallow foundations, soft ground improvement and so on. Conventionally, planar geosynthetic materials such as geotextiles and geogrids were used as the reinforcing elements. Recently, the use of three dimensional reinforcements becomes one of the emerging trends in this field. So, in the present investigation, three dimensional geogrid is proposed as a reinforcing material. Laboratory scaled plate load tests are conducted on a model square footing resting on 3D geogrid reinforced sand bed. The performance of 3D geogrids in triangular and square pattern was compared with conventional geogrids and the improvement in bearing capacity and reduction in settlement and heave are evaluated. When single layer of reinforcement was placed at an optimum depth of 0.25B from the bottom of the footing, the bearing capacity of conventional geogrid reinforced soil improved by 1.85 times compared to unreinforced soil, where as 3D geogrid reinforced soil with triangular pattern and square pattern shows 2.69 and 3.05 times improvement respectively compared to unreinforced soil. Also, 3D geogrids performs better than conventional geogrids in reducing the settlement and heave of sand bed around the model footing.

Keywords: 3D reinforcing elements, bearing capacity, heavy, settlement

Procedia PDF Downloads 302

Authors: Ashkan Forootan, Reza Rashedi

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Polyethylene has wide usage areas such as blow molding, pipe, film, cable insulation. However, regardless to its growing applications, it has some constraints such as the limited 70C operating temperature. Polyethylene thermo setting procedure whose molecules are knotted and 3D-molecular-network formed , is developed to conquer the above problem and to raise the applicable temperature of the polymer. This paper reports the cross-linking for two different molecular weight grades of LLDPE by adding 0.5, 1, and 2% of DCP (Dicumyl Peroxide). DCP was chosen for its prevalence among various cross-linking agents. Structural parameters such as molecular weight, melt flow index, comonomer, number of branches,etc. were obtained through the use of relative tests as Gel Permeation Chromatography and Fourier Transform Infra Red spectrometer. After calculating the percentage of gel content, properties of the pure and cross-linked samples were compared by thermal and mechanical analysis with DMTA and FTIR and the effects of cross-linking like viscous and elastic modulus were discussed by using various structural paprameters such as MFI, molecular weight, short chain branches, etc. Studies showed that cross-linked polymer, unlike the pure one, had a solid state with thermal mechanical properties in the range of 110 to 120C and this helped overcome the problem of using polyethylene in temperatures near the melting point.

Keywords: LLDPE, cross-link, structural parameters, DCP, DMTA, GPC

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Authors: Nuray Ucar, Pelin Altay, Ilkay Ozsev Yuksek

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Graphene oxide fibers have recently received increasing attention due to their excellent properties such as high specific surface area, high mechanical strength, good thermal properties and high electrical conductivity. They have shown notable potential in various applications including batteries, sensors, filtration and separation and wearable electronics. Carbon nanotubes (CNTs) have unique structural, mechanical, and electrical properties and can be used together with graphene oxide fibers for several application areas such as lithium ion batteries, wearable electronics, etc. Metals salts that can be converted into metal ions and metal oxide can be also used for several application areas such as battery, purification natural gas, filtration, absorption. This study investigates the effects of CNT and metal complex compounds (MnCl₂, metal salts) on the morphological structure of graphene oxide fibers. The graphene oxide dispersion was manufactured by modified Hummers method, and continuous graphene oxide fibers were produced with wet spinning. The CNT and MnCl₂ were incorporated into the coagulation baths during wet spinning process. Produced composite continuous fibers were analyzed with SEM, SEM-EDS and AFM microscopies and as spun fiber counts were measured.

Keywords: continuous graphene oxide fiber, Hummers' method, CNT, MnCl₂

Procedia PDF Downloads 176

Authors: Shahin Farajifar, Azadeh Safadoust, Ali Akbar Mahboubi

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A study was conducted to evaluate hydraulic properties of a sandy loam soil and corn (Zea mays L.) crop production under a short-term tillage and manure combinations field experiment carried out in west of Iran. Treatments included composted cattle manure application rates [0, 30, and 60 Mg (dry weight) ha-1] and tillage systems [no-tillage (NT), chisel plowing (CP), and moldboard plowing (MP)] arranged in a split-plot design. Soil water characteristic curve (SWCC) and saturated hydraulic conductivity (Ks) were significantly affected by manure and tillage treatments. At any matric suction, the soil water content was in the order of MP>CP>NT. At all matric suctions, the amount of water retained by the soil increased as manure application rate increased (i.e. 60>30>0 Mg ha-1). Similar to the tillage effects, at high suctions the differences of water retained due to manure addition were less than that at low suctions. The change of SWCC from tillage methods and manure applications may attribute to the change of pore size and aggregate size distributions. Soil Ks was in the order of CP>MP>NT for the first two layers and in the order of MP>CP and NT for the deeper soil layer. The Ks also increased with increasing rates of manure application (i.e. 60>30>0 Mg ha-1). This was due to the increase in the total pore size and continuity.

Keywords: corn, manuure, saturated hydraulic conductivity, soil water characteristic curve, tillage

Procedia PDF Downloads 74

Authors: S. Assylbekova, D. Zolotareva, A. Dauletbakov, Ye. Belyankova, S. Bayazit, A. Basharimova, A. Zazybin, A. Isimberlenova, A. Kakimova, M. Aydemir, A. Kairullinova

Abstract:

Transfer hydrogenation (TH) is a key process in organic chemistry, especially in pharmaceutical and agrochemical synthesis, offering a safer and more sustainable approach compared to traditional methods. This work is devoted to the synthesis and use of ruthenium catalysts containing phosphinite ligands in TH reactions. Ruthenium complexes are particularly noteworthy for their effectiveness in asymmetric TH. Their stability and adaptability to different reaction environments make them ideal for both laboratory-scale and industrial applications. Phosphinite ligands (P(OR)R'2) are used in the synthesis of complexes to improve their properties. These ligands are known for their ability to finely tune the electronic and steric properties of metal centers. The electron-donating nature of the phosphorus atom, combined with the variability in the R and R' groups, allows for significant customization of the catalyst's properties. The purpose and difference of the work is to study the incorporation of a hydrophilic ionic liquid into the composition of a phosphinite ligand, which will then be converted into a catalyst. The technique involves the synthesis of a phosphinite ligand with an ionic liquid at room temperature under an inert atmosphere and then a ruthenium complex. Next, the TH reactions of acetophenone and its derivatives are carried out using the resulting catalyst. The conversion of ketone to alcohol is analyzed using a gas chromatograph. This study contributes to the understanding of the influence of catalyst physico-chemical properties on transfer hydrogenation results.

Keywords: transfer hydrogenation, ruthenium, catalysts, phosphinite ligands

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

Abstract:

Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: Anita Sadat Sadati Rostami, Reza Almasi Ghaleh

Abstract:

Surface electromyography (sEMG) offers important insights into muscle activation and has applications in fields including rehabilitation and human-computer interaction. The purpose of this work is to predict the degree of activation of two joints in the index finger using an LSTM-Transformer architecture trained on sEMG data from the Ninapro DB8 dataset. We apply advanced preprocessing techniques, such as multi-band filtering and customizable rectification methods, to enhance the encoding of sEMG data into features that are beneficial for regression tasks. The processed data is converted into spike patterns and simulated using Leaky Integrate-and-Fire (LIF) neuron models, allowing for neuromorphic-inspired processing. Our findings demonstrate that adjusting filtering parameters and neuron dynamics and employing the LSTM-Transformer model improves joint angle prediction performance. This study contributes to the ongoing development of deep learning frameworks for sEMG analysis, which could lead to improvements in motor control systems.

Keywords: surface electromyography, LSTM-transformer, spiking neural networks, hand kinematics, leaky integrate-and-fire neuron, band-pass filtering, muscle activity decoding

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Authors: Endalamaw Ewnu Kassa, Ade Kurniawan, Ya-Fen Wu, Sajal Biring

Abstract:

Perovskite-based gas sensors represent a highly promising materials within the realm of gas sensing technology, with a particular focus on detecting ammonia (NH3) due to its potential hazards. Our work conducted thorough comparison of various solvents, including dimethylformamide (DMF), DMF-dimethyl sulfoxide (DMSO), dimethylacetamide (DMAC), and DMAC-DMSO, for the preparation of our perovskite solution (MAPbI3). Significantly, we achieved an exceptional response at 10 ppm of ammonia gas by employing a binary solvent mixture of DMAC-DMSO. In contrast to prior reports that relied on single solvents for MAPbI3 precursor preparation, our approach using mixed solvents demonstrated a marked improvement in gas sensing performance. We attained enhanced surface coverage, a reduction in pinhole occurrences, and precise control over grain size in our perovskite films through the careful selection and mixtures of appropriate solvents. This study shows a promising potential of employing binary and multi-solvent mixture strategies as a means to propel advancements in gas sensor technology, opening up new opportunities for practical applications in environmental monitoring and industrial safety.

Keywords: sensors, binary solvents, ammonia, sensitivity, grain size, pinholes, surface coverage

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

Abstract:

The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

Procedia PDF Downloads 409