Search results for: multi terminal

Authors: Jiji Jose, R. Narayanacharyulu, Molly Mathew, Jisha Prems

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Introduction: Lercanidipine hydrochloride (LD), an effective calcium channel blocker, widely used for the treatment of chronic stable angina and hypertension seems to be potential transdermal therapeutic system candidate, mainly due to its low oral bio availability, short half life and high first-pass metabolism. Objective: To develop transdermal therapeutic systems for LD and to evaluate its in vivo performance in rabbits. Methodology: Transdermal patches of LD were formulated using the polymer blend of eudragit RL100 (ERL) and polyvinyl pyrolidone (PVP) by casting method Propylene glycol (PG) and tween 80 were used as plasticizer and permeation enhancer respectively. The pharmaco kinetic parameters of LD after the administration of transdermal patches was compared with that of oral administration. The study was carried out in a two way crossover design in male New Zealand albino rabbits. Results: The formulation with ERL: PVP ratio 1:4 with 15% w/w PG as plasticizer and 4% w/w tween 80 as permeation enhancer showed the best drug release results. The pharmacokinetic parameters such as Cmax, tmax, mean residence time (MRT) and area under the curve (AUC 0-∞) were significantly different following transdermal administration compared to oral administration. The terminal half life of transdermally administered LD was found to similar that of oral administration. A sustained drug release over a period of 24 hrs was observed after transdermal administration. Conclusion: The fabricated transdermal delivery system have the potential to provide controlled and extended drug release, better bio availability and thus, this may improve the patient compliance.

Keywords: transdermal therapeutic system, lercanidipine hydrochloride, eudragit, skinpermeation

Procedia PDF Downloads 615

Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

Procedia PDF Downloads 133

Authors: Hu-Jiang Shi, Li-Juan Zhu

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The implication of 5-hydroxytryptamine 2C receptor (5-HT2CR) in affective behaviors is a topic of debate, and the underlying mechanisms remain largely unclear. Here, we elucidate that the interaction between hippocampal neuronal nitric oxide synthase (nNOS) and carboxy-terminal PDZ ligand of nNOS (CAPON) contributes to the disruption of hippocampal excitation-inhibition (E/I) balance, which is responsible for the anxiety- and depressive-like behaviors caused by chronic stress-related 5-HT2CR signaling deficiency. In detail, activation or inhibition of 5-HT2CR by CP809101 or SB242084 modulates nNOS-CAPON interaction by influencing intracellular Ca²⁺ release. Notably, the dissociation of nNOS-CAPON abolishes SB242084-induced anxiety- and depressive-like behaviors, as well as the reduction in extracellular signal-regulated kinase (ERK)/cAMP-response element binding protein (CREB)/synapsin signaling and SNARE complex assembly. Furthermore, nNOS-CAPON blockers restore the impairments caused by SB242084, including the reduction in SNARE assembly-mediated γ-aminobutyric acid (GABA) vesicle release and a consequent shift of the E/I balance toward excitation in CA3 pyramidal neurons. Conclusively, our findings disclose the regulatory role of 5-HT2CR in anxiety- and depressive-like behaviors and highlight the hippocampal nNOS-CAPON coupling-triggered E/I imbalance as a pivotal cellular event underpinning the behavioral consequences of 5-HT2CR inhibition.

Keywords: 5-HT2CR, anxiety, depression, nNOS-CAPON coupling, excitation-inhibition balance, neurotransmitter release

Procedia PDF Downloads 65

Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

Procedia PDF Downloads 109

Authors: Felicia Segeth, Florian G. Klein, Lea Berger, Andreas Kolk, Per S. Holm

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The entry of oncolytic viruses (OVs) into clinical application opens groundbreaking changes in current and future treatment regimens. However, despite their potent anti-cancer activity in vitro, clinical studies revealed limitations of OVs as monotherapy. The same applies to CDK 4/6 inhibitors (CDK4/6i) targeting cell cycle as well as bromodomain and extra-terminal domain inhibitors (BETi) targeting gene expression. In this study, the anti-tumoral effect of XVir-N-31, an YB-1 dependent oncolytic adenovirus, was evaluated in combination with Ribociclib, a CDK4/6i, and JQ1, a BETi. The head and neck squamous cell carcinoma (HNSCC) cell lines Fadu, SAS, and Cal-33 were used. DNA replication and gene expression of XVir-N-31 was measured by RT-qPCR, protein expression by western blotting, and cell lysis by SRB assays. Treatment with CDK4/6i and BETi increased viral gene expression, viral DNA replication, and viral particle formation. The data show that the combination of oncolytic adenovirus XVir-N-31 with CDK4/6i & BETi acts highly synergistic in cancer cell lysis. Furthermore, additional molecular analyses on this subject demonstrate that the positive transcription elongation factor P-TEFb plays a decisive role in this regard, indicating an influence of the combinational therapy on gene transcription control. The combination of CDK4/6i & BETi and XVir-N-31 is an attractive strategy to achieve substantial cancer cell killing and is highly suitable for clinical testing.

Keywords: adenovirus, BET, CDK4/6, HNSCC, P-TEFb, YB-1

Procedia PDF Downloads 118

Authors: Ching-I Chen

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The continued rise of e-commerce is the main driver of the rapid growth of global online purchase. Consumers can nearly buy everything they want at one occasion through online shopping. The purchase behavior models which focus on single product category are insufficient to describe online shopping behavior. Therefore, analysis of multi-category purchase gets more and more popular. For example, market basket analysis explores customers’ buying tendency of the association between product categories. The information derived from market basket analysis facilitates to make cross-selling strategies and product recommendation system. To detect the association between different product categories, we use the market basket analysis with the multidimensional scaling technique to build an associated map which describes how likely multiple product categories are bought at the same time. Besides, we also build an inter-purchase time model for associated products to describe how likely a product will be bought after its associated product is bought. We classify inter-purchase time behaviors of multi-category products into nine types, and use a mixture regression model to integrate those behaviors under our assumptions of purchase sequences. Our sample data is from comScore which provides a panelist-label database that captures detailed browsing and buying behavior of internet users across the United States. Finding the inter-purchase time from books to movie is shorter than the inter-purchase time from movies to books. According to the model analysis and empirical results, this research finally proposes the applications and recommendations in the management.

Keywords: multiple-category purchase behavior, inter-purchase time, market basket analysis, e-commerce

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Authors: Omar Boutkhoum, Mohamed Hanine, Abdessadek Bendarag

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Sustainable economic growth is nowadays driving firms to extend toward the adoption of many green supply chain management (GSCM) solutions. However, the evaluation and selection of these solutions is a matter of concern that needs very serious decisions, involving complexity owing to the presence of various associated factors. To resolve this problem, a comparative analysis approach based on multi-criteria decision-making methods is proposed for adequate evaluation of sustainable supply chain management solutions. In the present paper, we propose an integrated decision-making model based on FAHP (Fuzzy Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) to contribute to a better understanding and development of new sustainable strategies for industrial organizations. Due to the varied importance of the selected criteria, FAHP is used to identify the evaluation criteria and assign the importance weights for each criterion, while TOPSIS and PROMETHEE methods employ these weighted criteria as inputs to evaluate and rank the alternatives. The main objective is to provide a comparative analysis based on TOPSIS and PROMETHEE processes to help make sound and reasoned decisions related to the selection problem of GSCM solution.

Keywords: GSCM solutions, multi-criteria analysis, decision support system, TOPSIS, FAHP, PROMETHEE

Procedia PDF Downloads 163

Authors: Niklas Bondesson

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Purpose: This paper attempts to examine how different facets of attitudinal brand loyalty are determined by different brand image elements in business markets. Design/Methodology/Approach: Statistical analysis is employed to data from a web survey, covering 226 professional packaging buyers in eight countries. Findings: The results reveal that different brand loyalty facets have different antecedents. Affective brand loyalties (or loyalty 'feelings') are mainly driven by customer associations to service relationships, whereas customers’ loyalty intentions (to purchase and recommend a brand) are triggered by associations to the general reputation of the company. The findings also indicate that willingness to pay a price premium is a distinct form of loyalty, with unique determinants. Research implications: Theoretically, the paper suggests that corporate B2B brand loyalty needs to be conceptualised with more refinement than has been done in extant B2B branding work. Methodologically, the paper highlights that single-item approaches can be fruitful when measuring B2B brand loyalty, and that multi-item scales can conceal important nuances in terms of understanding why customers are loyal. Practical implications: The idea of a loyalty 'silver metric' is an attractive idea, but this study indicates that firms who rely too much on one single type of brand loyalty risk to miss important building blocks. Originality/Value/Contribution: The major contribution is a more multi-faceted conceptualisation, and measurement, of corporate B2B brand loyalty and its brand image determinants than extant work has provided.

Keywords: brand equity, business-to-business branding, industrial marketing, buying behaviour

Procedia PDF Downloads 414

Authors: Yang Yu, Jian Wang, Jiqiang Liu, Lei Han, Xudong He, Shaohua Lv

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As the increasing network attacks become more and more complex, network situation assessment based on log analysis cannot meet the requirements to ensure network security because of the low quality of logs and alerts. This paper addresses the lack of consideration of security attributes of hosts and attacks in the network. Identity and effectiveness of Distributed Denial of Service (DDoS) are hard to be proved in risk assessment based on alerts and flow matching. This paper proposes a multi-dimension threat situation assessment method based on network security attributes. First, the paper offers an improved Common Vulnerability Scoring System (CVSS) calculation, which includes confident risk, integrity risk, availability risk and a weighted risk. Second, the paper introduces deterioration rate of properties collected by sensors in hosts and network, which aimed at assessing the time and level of DDoS attacks. Third, the paper introduces distribution of asset value in security attributes considering features of attacks and network, which aimed at assessing and show the whole situation. Experiments demonstrate that the approach reflects effectiveness and level of DDoS attacks, and the result can show the primary threat in network and security requirement of network. Through comparison and analysis, the method reflects more in security requirement and security risk situation than traditional methods based on alert and flow analyzing.

Keywords: DDoS evaluation, improved CVSS, network security attribute, threat situation assessment

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Authors: Moustafa Osman Mohammed

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The photocurrent generations are influencing ultra-high efficiency solar cells based on self-assembled quantum dot (QD) nanostructures. Nanocrystal quantum dots (QD) provide a great enhancement toward solar cell efficiencies through the use of quantum confinement to tune absorbance across the solar spectrum enabled multi-exciton generation. Based on theoretical predictions, QDs have potential to improve systems efficiency in approximate regular electrons excitation intensity greater than 50%. In solar cell devices, an intermediate band formed by the electron levels in quantum dot systems. The spatial architecture is exploring how can solar cell integrate and produce not only high open circuit voltage (> 1.7 eV) but also large short-circuit currents due to the efficient absorption of sub-bandgap photons. In the proposed QD system, the structure allows barrier material to absorb wavelengths below 700 nm while multi-photon processes in the used quantum dots to absorb wavelengths up to 2 µm. The assembly of the electronic model is flexible to demonstrate the atoms and molecules structure and material properties to tune control energy bandgap of the barrier quantum dot to their respective optimum values. In terms of energy virtual conversion, the efficiency and cost of the electronic structure are unified outperform a pair of multi-junction solar cell that obtained in the rigorous test to quantify the errors. The milestone toward achieving the claimed high-efficiency solar cell device is controlling the edge causes of energy bandgap between the barrier material and quantum dot systems according to the media design limits. Despite this remarkable potential for high photocurrent generation, the achievable open-circuit voltage (Voc) is fundamentally limited due to non-radiative recombination processes in QD solar cells. The orientation of voltage recovery system is compared theoretically with experimental Voc variation in mediation upper–limit obtained one diode modeling form at the cells with different bandgap (Eg) as classified in the proposed spatial architecture. The opportunity for improvement Voc is valued approximately greater than 1V by using smaller QDs through QD solar cell recovery systems as confined to other micro and nano operations states.

Keywords: nanotechnology, photovoltaic solar cell, quantum systems, renewable energy, environmental modeling

Procedia PDF Downloads 156

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

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

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

Procedia PDF Downloads 59

Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

Procedia PDF Downloads 79

Authors: Cheng Shen, LaiHong Shen

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Ammonia is an important carrier of hydrogen energy, with the characteristics of high hydrogen content density and no carbon dioxide emission. Ammonia synthesis by the Haber process is the main method for industrial ammonia synthesis, but the conversion rate of ammonia per pass is only about 12%, while the conversion rate of biomass synthesis ammonia is as high as 56%. Therefore, safe and efficient ammonia capture for ammonia synthesis from biomass is an important way to alleviate the energy crisis and solve the energy problem. Metal chloride has a chemical adsorption effect on ammonia, and can be desorbed at high temperature to obtain high-concentration ammonia after combining with ammonia, which has a good development prospect in ammonia capture and separation technology. In this paper, the ammonia adsorption properties of CuCl₂ were measured, and the composite adsorbents were prepared by using silicon and multi-walled carbon nanotubes respectively to support CuCl₂, and the ammonia adsorption properties of the composite adsorbents were studied. The study found that the ammonia adsorption capacity of the three adsorbents decreased with the increase in temperature, so metal chlorides were more suitable for the low-temperature adsorption of ammonia. Silicon and multi-walled carbon nanotubes have an enhanced effect on the ammonia adsorption of CuCl₂. The reason is that the porous material itself has a physical adsorption effect on ammonia, and silicon can play the role of skeleton support in cupric chloride particles, which enhances the pore structure of the adsorbent, thereby alleviating sintering.

Keywords: ammonia, adsorption properties, metal chloride, silicon, MWCNTs

Procedia PDF Downloads 112

Authors: Rahul Shrivastava, Manish Tripathi, Mohmmad Yasir, Shailesh Singh

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Life style changes and depletion in atmospheric ozone layer in recent decades lead to increase in skin cancer including both melanoma and nonmelanomas. Natural products which were obtained from different plant species have the potential of anti skin cancer activity. In regard of this, present study focuses the potential effect of Glycosmis pentaphylla against anti skin cancer activity. Different Phytochemical constituents which were present in the roots of Glycosmis pentaphylla were identified and were used as ligands after sketching of their structures with the help of ACD/Chemsketch. These ligands are screened for their anticancer potential with proteins which are involved in skin cancer effects with the help of pyrx software. After performing docking studies, results reveal that Noracronycine secondary metabolite of Glycosmis pentaphylla shows strong affinity of their binding energy with Ribosomal S6 Kinase 2 (2QR8) protein. Ribosomal S6 Kinase 2 (2QR8) has an important role in the cell proliferation and transformation mediated through by N-terminal kinase domain and was induced by the tumour promoters such as epidermal growth factor. It also plays a key role in the neoplastic transformation of human skin cells and in skin cancer growth. Noracronycine interact with THR-493 and MET-496 residue of Ribosomal S6 Kinase 2 protein with binding energy ΔG = -8.68 kcal/mole. Thus on the basis of this study we can say that Noracronycine which present in roots of Glycosmis pentaphylla can be used as lead compound against skin cancer.

Keywords: glycosmis pentaphylla, pyrx, ribosomal s6 kinase, skin cancer

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Authors: Rakesh Kumar, Fatima Electricwala

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One of the major thrusts of the Bus Rapid Transit System is to reduce the commuter’s dependency on private vehicles and increase the shares of public transport to make urban transportation system environmentally sustainable. In this study, commuter mode choice analysis is performed that examines behavioral responses to the proposed Bus Rapid Transit System (BRTS) in Surat, with estimation of the probable shift from private mode to public mode. Further, evaluation of the BRTS scenarios, using Surat’s transportation ecological footprint was done. A multi-modal simulation model was developed in Biogeme environment to explicitly consider private users behaviors and non-linear environmental impact. The data of the different factors (variables) and its impact that might cause modal shift of private mode users to proposed BRTS were collected through home-interview survey using revealed and stated preference approach. A multi modal logit model of mode-choice was then calibrated using the collected data and validated using proposed sample. From this study, a set of perception factors, with reliable and predictable data base, to explain the variation in modal shift behaviour and their impact on Surat’s ecological environment has been identified. A case study of the proposed BRTS connecting the Surat Industrial Hub to the coastal area is provided to illustrate the approach.

Keywords: BRTS, private modes, mode choice models, ecological footprint

Procedia PDF Downloads 519

Authors: Jihad Daba, Jean-Pierre Dubois

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Multi path fading noise degrades the performance of cellular communication, most notably in femto- and pico-cells in 3G and 4G systems. When the wireless channel consists of a small number of scattering paths, the statistics of fading noise is not analytically tractable and poses a serious challenge to developing closed canonical forms that can be analysed and used in the design of efficient and optimal receivers. In this context, noise is multiplicative and is referred to as stochastically local fading. In many analytical investigation of multiplicative noise, the exponential or Gamma statistics are invoked. More recent advances by the author of this paper have utilized a Poisson modulated and weighted generalized Laguerre polynomials with controlling parameters and uncorrelated noise assumptions. In this paper, we investigate the statistics of multi-diversity stochastically local area fading channel when the channel consists of randomly distributed Rayleigh and Rician scattering centers with a coherent specular Nakagami-distributed line of sight component and an underlying doubly stochastic Poisson process driven by a lognormal intensity. These combined statistics form a unifying triply stochastic filtered marked Poisson point process model.

Keywords: cellular communication, femto and pico-cells, stochastically local area fading channel, triply stochastic filtered marked Poisson point process

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Authors: Preecha Yupapin And Somnath

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From the emptiness, the vibration induces the fractal, and the strings are formed. From which the first elementary particle groups, known as quarks, were established. The neutrino and electron are created by them. More elementary particles and life are formed by organic and inorganic substances. The universe is constructed, from which the multi-universe has formed in the same way. universe assumes that the intense energy has escaped from the singularity cone from the multi-universes. Initially, the single mass energy is confined, from which it is disturbed by the space-time distortion. It splits into the entangled pair, where the circular motion is established. It will consider one side of the entangled pair, where the fusion energy of the strong coupling force has formed. The growth of the fusion energy has the quantum physic phenomena, where the moving of the particle along the circumference with a speed faster than light. It introduces the wave-particle duality aspect, which will be saturated at the stopping point. It will be re-run again and again without limitation, which can say that the universe has been created and expanded. The Bose-Einstein condensate (BEC) is released through the singularity by the wormhole, which will be condensed to become a mass associated with the Sun's size. It will circulate(orbit) along the Sun. the consideration of the uncertainty principle is applied, from which the breath control is followed by the uncertainty condition ∆p∆x=∆E∆t~ℏ. The flowing in-out air into a body via a nose has applied momentum and energy control respecting the movement and time, in which the target is that the distortion of space-time will have vanished. Finally, the body is clean which can go to the next procedure, where the mind can escape from the body by the speed of light. However, the borderline between contemplation to being an Arahant is a vacuum, which will be explained.

Keywords: space-time, relativity, enlightenment, emptiness

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Authors: Gaurav Kumar Sinha

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The oil and gas industry is a critical component of the global economy, relying heavily on industrial control systems (ICS) to manage and monitor operations. However, these systems are increasingly becoming targets for cyber-attacks, posing significant risks to operational continuity, safety, and environmental integrity. This paper explores comprehensive cybersecurity strategies for protecting oil and gas industrial control systems. It delves into the unique vulnerabilities of ICS in this sector, including outdated legacy systems, integration with IT networks, and the increased connectivity brought by the Industrial Internet of Things (IIoT). We propose a multi-layered defense approach that includes the implementation of robust network security protocols, regular system updates and patch management, advanced threat detection and response mechanisms, and stringent access control measures. We illustrate the effectiveness of these strategies in mitigating cyber risks and ensuring the resilient and secure operation of oil and gas industrial control systems. The findings underscore the necessity for a proactive and adaptive cybersecurity framework to safeguard critical infrastructure in the face of evolving cyber threats.

Keywords: cybersecurity, industrial control systems, oil and gas, cyber-attacks, network security, IoT, threat detection, system updates, patch management, access control, cybersecurity awareness, critical infrastructure, resilience, cyber threats, legacy systems, IT integration, multi-layered defense, operational continuity, safety, environmental integrity

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Authors: Nuno M. Pereira

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In the last thirty years, European cities have been addressing global climate change and its local impacts by implementing mitigation and adaptation strategies. Lisbon Downtown is no exception with 10 plans under implementation since 2010 with completion scheduled for 2030 valued 1 billion euros of public investment. However, the gap between mitigation and adaptation strategies is not yet sufficiently studied alongside with its nuances- vulnerability and risk mitigation, resilience and adaptation. In Lisbon Downtown, these plans are being implemented separately, therefore compromising the effectiveness of public investment. The research reviewed the common ground of mitigation and adaptation strategies of the theoretical framework and analyzed the current urban development actions in Lisbon Downtown in order to identify potential conflicts and synergies. The empirical fieldwork supported by a sounding board of experts has been developed during two years and the results suggest that the largest public investment in Lisbon on flooding mitigation will conflict with the new Cruise ship terminal and old Downton building stock, therefore increasing risk and vulnerability factors. The study concludes that the Lisbon Downtown blue infrastructure plan should be redesigned in some areas in a trans- disciplinary and holistic approach and that the current theoretical framework on climate change should focus more on mitigation and adaptation synergies articulating the gray, blue and green infrastructures, combining old knowledge tested by resilient communities and new knowledge emerging from the digital era.

Keywords: adaptation, climate change, conflict, Lisbon Downtown, mitigation, synergy

Procedia PDF Downloads 200

Authors: Linmin Zhang

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Entrained-flow gasification technology is considered the most promising gasification technology because of its clean and efficient utilization characteristics. The stable fluidity of slag at high temperatures is the key to affecting the long-period operation of the gasifier. The diversity and differences of coal ash-slag systems make it difficult to meet the requirements for stable slagging in entrained-flow gasifiers. Therefore, coal blending or adding fluxes has been used in industry for a long time to improve the flow behavior of coal ash. As a by-product of the indirect coal liquefaction process, indirect coal liquefaction residue (ICLR) is a kind of industrial solid waste that is usually disposed of by stacking or landfilling. However, this disposal method will not only occupy land resources but also cause serious pollution to soil and water bodies by leachate containing toxic and harmful metals. As a carbon-containing matrix, ICLR is not only a kind of waste but also a kind of energy substance. Utilizing existing industrial gasifiers to blend combustion ICLR can not only transform industrial solid waste into fuel but also save coal resources. Moreover, the ICLR usually contains a unique ash chemical composition different from coal, which will affect the slagging performance of the gasifier. Therefore, exploring the effect of the ash addition in ICLR on the coal ash flow behavior can not only improve the slagging performance and gasification efficiency of entrained-flow gasifier by using the unique ash chemical composition of ICLR but also provide some theoretical support for the large-scale consumption of industrial solid waste. Combining molecular dynamics simulation with Raman spectroscopy experiment, the effect of ICLR addition on slag structure and fluidity was explained, and the relationship between the evolution law of slag short/medium range microstructure and macroscopic flow behavior was discussed. The research found that the high silicon and aluminum content in coal ash led to the formation of complex [SiO₄]⁴- tetrahedron and [AlO₄]⁵- tetrahedron structures at high temperature, and the [SiO₄]⁴- tetrahedron and [AlO₄]⁵- tetrahedron were connected by oxygen atoms to form a multi-membered ring structure with high polymerization degree. Due to the action of the multi-membered ring structure, the internal friction in the slag increased, and the viscosity value was higher on the macro-level. As a network-modified ion, Fe2+ could replace Si4+ and Al3+ in the multi-membered ring structure and combine with O2-, which will destroy the bridge oxygen (BO) structure and transform more complex tri cluster oxygen (TO) and bridge oxygen (BO) into simple non-bridge oxygen (NBO) structure. As a result, a large number of multi-membered rings with high polymerization degrees were depolymerized into low-membered rings with low polymerization degrees. The evolution of oxygen types and ring structures in slag reduced the structure complexity and polymerization degree of coal ash slag, resulting in a decrease in the viscosity of coal ash slag.

Keywords: ash slag, coal gasification, fluidity, industrial solid waste, slag structure

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Authors: Jalal Khan, Daniyal Ali Sehrai, Shakeel Ahmad

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This paper presents the design and performance evaluation of multiband metamaterial based antenna operating in the 3.6 GHz (4G), 14.33 GHz, and 28.86 GHz (5G) frequency bands, for future mobile and handheld devices. The radiating element of the proposed design is made up of a conductive material supported by a 1.524 mm thicker Rogers-4003 substrate, having a relative dielectric constant and loss tangent of 3.55 and 0.0027, respectively. The substrate is backed by truncated ground plane. The future mobile communication system is based on higher frequencies, which are highly affected by the atmospheric conditions. Therefore, to overcome the path loss problem, essential enhancements and improvements must be made in the overall performance of the antenna. The traditional ground plane does not provide the in-phase reflection and surface wave suppression due to which side and back lobes are produced. This will affect the antenna performance in terms of gain and efficiency. To enhance the overall performance of the antenna, a metamaterial acting as a high impedance surface (HIS) is used as a reflector in the proposed design. The simulated gain of the metamaterial based antenna is enhanced from {2.76-6.47, 4.83-6.71 and 7.52-7.73} dB at 3.6, 14.33 and 28.89 GHz, respectively relative to the gain of the antenna backed by a traditional ground plane. The proposed antenna radiated efficiently with a radiated efficiency (>85 %) in all the three frequency bands with and without metamaterial surface. The total volume of the antenna is (L x W x h=45 x 40 x 1.524) mm³. The antenna can be potentially used for wireless handheld devices and mobile terminal. All the simulations have been performed using the Computer Simulation Technology (CST) software.

Keywords: CST MWS, fourth generation/fifth generation, 4G/5G, high gain, multiband, metamaterial

Procedia PDF Downloads 160

Authors: Yiping Meng, Yiming Sun

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The traditional construction industry is unable to meet the requirements for novel fabrication and construction. Automated construction and digital design have emerged as industry development trends that compensate for this shortcoming under the backdrop of Industrial Revolution 4.0. Benefitting from more flexible working space and more various end-effector tools compared to CNC methods, robot fabrication and construction techniques have been used in irregular architectural design. However, there is a lack of a systematic and comprehensive design and optimisation workflow considering geometric form, material, and fabrication methods. This paper aims to propose a design optimisation workflow for improving the rationality of a free-form timber structure fabricated by the robotic arm. Firstly, the free-form surface is described by NURBS, while its structure is calculated using the finite element analysis method. Then, by considering the characteristics and limiting factors of robotic timber fabrication, strain energy and robustness are set as optimisation objectives to optimise structural morphology by gradient descent method. As a result, an optimised structure with axial force as the main force and uniform stress distribution is generated after the structure morphology optimisation process. With the decreased strain energy and the improved robustness, the generated structure's bearing capacity and mechanical properties have been enhanced. The results prove the feasibility and effectiveness of the proposed optimisation workflow for free-form timber structure morphology design.

Keywords: robotic fabrication, free-form timber structure, Multi-objective optimisation, Structural morphology, rational design

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Authors: Yu-ding Du, Qi-lian Bao, Nassim Bessaad, Lin Liu

Abstract:

The Global Navigation Satellite System (GNSS) is regarded as an effective approach for the purpose of replacing the large amount used track-side balises in modern train localization systems. This paper describes a method based on the data fusion of a GNSS receiver sensor and an odometer sensor that can significantly improve the positioning accuracy. A digital track map is needed as another sensor to project two-dimensional GNSS position to one-dimensional along-track distance due to the fact that the train’s position can only be constrained on the track. A model trained by BP neural network is used to estimate the trend positioning error which is related to the specific location and proximate processing of the digital track map. Considering that in some conditions the satellite signal failure will lead to the increase of GNSS positioning error, a detection step for GNSS signal is applied. An adaptive weighted fusion algorithm is presented to reduce the standard deviation of train speed measurement. Finally an Extended Kalman Filter (EKF) is used for the fusion of the projected 1-D GNSS positioning data and the 1-D train speed data to get the estimate position. Experimental results suggest that the proposed method performs well, which can reduce positioning error notably.

Keywords: multi-sensor data fusion, train positioning, GNSS, odometer, digital track map, map matching, BP neural network, adaptive weighted fusion, Kalman filter

Procedia PDF Downloads 252

Authors: V. V. Lakshmi, Y. V. S. Annapurna

Abstract:

Urinary tract infection (UTI) is one of the most common infectious diseases seen in the community. Susceptible individuals experience multiple episodes, and progress to acute pyelonephritis or uro-sepsis or develop asymptomatic bacteriuria (ABU). Ability to cause extraintestinal infections depends on several virulence factors required for survival at extraintestinal sites. Presence of virulence phenotypes enhances the pathogenicity of these otherwise commensal organisms and thus augments its ability to cause extraintestinal infections, the most frequent in urinary tract infections(UTI). The present study focuses on detection of the virulence characters exhibited by the uropathogenic organism and most common factors exhibited in the local pathogens. A total of 700 isolates of E.coli and Klebsiella spp were included in the study. These were isolated from patients from local hospitals reported to be suffering with UTI over a period of three years. Isolation and identification was done based on Gram character and IMVIC reactions. Antibiotic sensitivity profile was carried out by disc diffusion method and multi drug resistant strains with MAR index of 0.7 were further selected.. Virulence features examined included their ability to produce exopolysaccharides, protease- gelatinase production, hemolysin production, haemagglutination and hydrophobicity test. Exopolysaccharide production was most predominant virulence feature among the isolates when checked by congo red method. The biofilms production examined by microtitre plates using ELISA reader confirmed that this is the major factor contributing to virulencity of the pathogens followed by hemolysin production

Keywords: Escherichia coli, Klebsiella sp, Uropathogens, Virulence features.

Procedia PDF Downloads 421

Authors: Teresa Wills, Geraldine Mccarthy, Nicola Cornally

Abstract:

Background: Obesity is an emerging healthcare epidemic affecting virtually all age and socio-economic groups and is one of the most serious and prevalent diseases of the 21st century. It is a public health challenge because of its prevalence, associated costs and health effects. The increasing prevalence of obesity has created a social perception that overweight body sizes are healthy and normal. This normalization of obesity within our society and the acceptance of higher body weights have led to individuals being unaware of the reality of their weight status and gravity of this situation thus impeding recognition of obesity. Given the escalating global health problem of obesity and its co-morbidities, the need to re-appraise its management is more compelling than ever. It is widely accepted that the causes of obesity are complex and multi-factorial. Engagement of individuals in weight management programmes is difficult if they do not perceive they have a problem with their weight. Recognition of the problem is a key component of obesity management and identifying the main predictors of behaviour is key to designing health behaviour interventions. Aim: The aim of the research was to determine factors relating to motivation to change behaviours in individuals who perceive themselves to be overweight. Method: The research design was quantitative, correlational and cross-sectional. The design was guided by the Health Belief Model. Data were collected online using a multi-section and multi-item questionnaire, developed from a review of the theoretical and empirical research. A sample of 202 men and women who perceived themselves to be overweight participated in the research. Descriptive and inferential statistical analyses were employed to describe relationships between variables. Findings: Following multivariate regression analysis, perceived barriers to weight loss and perceived benefits of weight loss were significant predictors of motivation to change behaviour. The perceived barriers to weight loss which were significant were psychological barriers to weight loss (p = < 0.019) and environmental barriers to physical activity (p= < 0.032).The greatest predictor of motivation to change behaviour was the perceived benefits of weight loss (p < 0.001). Perceived susceptibility to obesity and perceived severity of obesity did not emerge as significant predictors in this model. Total variance explained by the model was 33.5%. Conclusion: Perceived barriers to weight loss and perceived benefits of weight loss are important determinants of motivation to change behaviour. These findings have important implications for health professionals to help inform their practice and for the development of intervention programmes to prevent and control obesity.

Keywords: motivation to change behaviours, obesity, predictors of behavior, interventions, overweight

Procedia PDF Downloads 414

Authors: Adel Nikfarjam, Hamed Tayebi, Sadoullah Ebrahimnejad

Abstract:

This paper considers a two-echelon inventory system with a number of warehouses and a single retailer. The retailer replenishes its required items from warehouses, and assembles them into a single final product. We assume that each warehouse supplies only one kind of the raw material for the retailer. The demand process of the final product is assumed to be Poissson, and unsatisfied demand of the final product will be lost. The retailer applies one-for-one-period ordering policy which is also known as (1, T) ordering policy. In this policy the retailer orders to each warehouse a fixed quantity of each item at fixed time intervals, which the fixed quantity is equal to the utilization of the item in the final product. Since, this policy eliminates all demand uncertainties at the upstream echelon, the standard lot sizing model can be applied at all warehouses. In this paper, we calculate the total cost function of the inventory system. Then, based on this function, we present a procedure to obtain the optimal time interval between two consecutive order placements from retailer to the warehouses, and the optimal order quantities of warehouses (assuming that there are positive ordering costs at warehouses). Finally, we present some numerical examples, and conduct numerical sensitivity analysis for cost parameters.

Keywords: two-echelon supply chain, multi-vendor-single-buyer inventory system, lost sales, Poisson demand, one-for-one-period policy, lot sizing model

Procedia PDF Downloads 312

Authors: V. V. Lakshmi, Y. V. S. Annapurna

Abstract:

Urinary tract infection (UTI) is one of the most common infectious diseases seen in the community. Susceptible individuals experience multiple episodes, and progress to acute pyelonephritis or uro-sepsis or develop asymptomatic bacteriuria (ABU). Ability to cause extraintestinal infections depends on several virulence factors required for survival at extraintestinal sites. Presence of virulence phenotypes enhances the pathogenicity of these otherwise commensal organisms and thus augments its ability to cause extraintestinal infections, the most frequent in urinary tract infections(UTI). The present study focuses on detection of the virulence characters exhibited by the uropathogenic organism and most common factors exhibited in the local pathogens. A total of 700 isolates of E.coli and Klebsiella spp were included in the study.These were isolated from patients from local hospitals reported to be suffering with UTI over a period of three years. Isolation and identification was done based on Gram character and IMVIC reactions. Antibiotic sensitivity profile was carried out by disc diffusion method and multi drug resistant strains with MAR index of 0.7 were further selected. Virulence features examined included their ability to produce exopolysaccharides, protease- gelatinase production, hemolysin production, haemagglutination and hydrophobicity test. Exopolysaccharide production was most predominant virulence feature among the isolates when checked by congo red method. The biofilms production examined by microtitre plates using ELISA reader confirmed that this is the major factor contributing to virulencity of the pathogens followed by hemolysin production.

Keywords: Escherichia coli, Klebsiella spp, Uropathogens, virulence features

Procedia PDF Downloads 319

Authors: Nastaran Hajiheydari, Mohammad Soltani Delgosha

Abstract:

Vendor (supplier) selection is a group decision-making (GDM) process, in which, based on some predetermined criteria, the experts’ preferences are provided in order to rank and choose the most desirable suppliers. In the real business environment, our attitudes or our choices would be made in an uncertain and indecisive situation could not be expressed in a crisp framework. Intuitionistic fuzzy sets (IFSs) could handle such situations in the best way. VIKOR method was developed to solve multi-criteria decision-making (MCDM) problems. This method, which is used to determine the compromised feasible solution with respect to the conflicting criteria, introduces a multi-criteria ranking index based on the particular measure of 'closeness' to the 'ideal solution'. Until now, there has been a little investigation of VIKOR with IFS, therefore we extended the intuitionistic fuzzy (IF) VIKOR to solve vendor selection problem under IF GDM environment. The present study intends to develop an IF VIKOR method in a GDM situation. Therefore, a model is presented to calculate the criterion weights based on entropy measure. Then, the interval-valued intuitionistic fuzzy weighted geometric (IFWG) operator utilized to obtain the total decision matrix. In the next stage, an approach based on the positive idle intuitionistic fuzzy number (PIIFN) and negative idle intuitionistic fuzzy number (NIIFN) was developed. Finally, the application of the proposed method to solve a vendor selection problem illustrated.

Keywords: group decision making, intuitionistic fuzzy set, intuitionistic fuzzy entropy measure, vendor selection, VIKOR

Procedia PDF Downloads 156

Authors: Chetna Mohabeer, Luis Reyes, Lokmane Abdelouahed, Bechara Taouk

Abstract:

In the transition to sustainability and the increasing use of renewable energy, hydrogen will play a key role as an energy carrier. Biomass has the potential to accelerate the realization of hydrogen as a major fuel of the future. Pyro-gasification allows the conversion of organic matter mainly into synthesis gas, or “syngas”, majorly constituted by CO, H2, CH4, and CO2. A second, condensable fraction of biomass pyro-gasification products are “tars”. Under certain conditions, tars may decompose into hydrogen and other light hydrocarbons. These conditions include two types of cracking: homogeneous cracking, where tars decompose under the effect of temperature ( > 1000 °C), and heterogeneous cracking, where catalysts such as olivine, dolomite or biochar are used. The latter process favors cracking of tars at temperatures close to pyro-gasification temperatures (~ 850 °C). Pyro-gasification of biomass coupled with water-gas shift is the most widely practiced process route for biomass to hydrogen today. In this work, an innovating solution will be proposed for this conversion route, in that all the pyro-gasification products, not only methane, will undergo processes that aim to optimize hydrogen production. First, a heterogeneous cracking step was included in the reaction scheme, using biochar (remaining solid from the pyro-gasification reaction) as catalyst and CO2 and H2O as gasifying agents. This process was followed by a catalytic steam methane reforming (SMR) step. For this, a Ni-based catalyst was tested under different reaction conditions to optimize H2 yield. Finally, a water-gas shift (WGS) reaction step with a Fe-based catalyst was added to optimize the H2 yield from CO. The reactor used for cracking was a fluidized bed reactor, and the one used for SMR and WGS was a fixed bed reactor. The gaseous products were analyzed continuously using a µ-GC (Fusion PN 074-594-P1F). With biochar as bed material, it was seen that more H2 was obtained with steam as a gasifying agent (32 mol. % vs. 15 mol. % with CO2 at 900 °C). CO and CH4 productions were also higher with steam than with CO2. Steam as gasifying agent and biochar as bed material were hence deemed efficient parameters for the first step. Among all parameters tested, CH4 conversions approaching 100 % were obtained from SMR reactions using Ni/γ-Al2O3 as a catalyst, 800 °C, and a steam/methane ratio of 5. This gave rise to about 45 mol % H2. Experiments about WGS reaction are currently being conducted. At the end of this phase, the four reactions are performed consecutively, and the results analyzed. The final aim is the development of a global kinetic model of the whole system in a multi-stage fluidized bed reactor that can be transferred on ASPEN PlusTM.

Keywords: multi-staged fluidized bed reactor, pyro-gasification, steam methane reforming, water-gas shift

Procedia PDF Downloads 138

Authors: Carlson Murray, Ali Lazrak

Abstract:

Robust experimental evidence of systematic violations of expected utility (EU) establishes that individuals facing risk overweight utility from low probability gains and losses when making choices. These findings motivated development of models of preferences with probability weighting functions, such as rank dependent utility (RDU). We solve for the optimal investing strategy of an RDU investor in a dynamic binomial setting from which we derive implications for investing behavior. We show that relative to EU investors with constant relative risk aversion, commonly measured probability weighting functions produce optimal RDU terminal wealth with significant downside protection and upside exposure. We additionally find that in contrast to EU investors, RDU investors optimally choose a portfolio that contains fair bets that provide payo↵s that can be interpreted as lottery outcomes or exposure to idiosyncratic returns. In a calibrated version of the model, we calculate that RDU investors would be willing to pay 5% of their initial wealth for the freedom to trade away from an optimal EU wealth allocation. The dynamic trading strategy that supports the optimal wealth allocation implies portfolio weights that are independent of initial wealth but requires higher risky share after good stock return histories. Optimal trading also implies the possibility of non-participation when historical returns are poor. Our model fills a gap in the literature by providing new quantitative and qualitative predictions that can be tested experimentally or using data on household wealth and portfolio choice.

Keywords: behavioral finance, probability weighting, portfolio choice

Procedia PDF Downloads 420