Search results for: transportation network

Authors: Graciela Soares Marcelli, Elison Eduardo Jardim Bierhals, Luciane Teresa Salvi, Claudineia Brazil, Rafael Haag

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The sun is a source of renewable energy, and its use as both a source of heat and light is one of the most promising energy alternatives for the future. To measure the thermal or photovoltaic systems a solar irradiation database is necessary. Brazil still has a reduced number of meteorological stations that provide frequency tests, as an alternative to the radio data platform, with reanalysis systems, quite significant. ERA-Interim is a global fire reanalysis by the European Center for Medium-Range Weather Forecasts (ECMWF). The data assimilation system used for the production of ERA-Interim is based on a 2006 version of the IFS (Cy31r2). The system includes a 4-dimensional variable analysis (4D-Var) with a 12-hour analysis window. The spatial resolution of the dataset is approximately 80 km at 60 vertical levels from the surface to 0.1 hPa. This work aims to make a comparative analysis between the ERA-Interim data and the data observed in the Solarimmetric Atlas of the State of Rio Grande do Sul, to verify its applicability in the absence of an observed data network. The analysis of the results obtained for a study region as an alternative to the energy potential of a given region.

Keywords: energy potential, reanalyses, renewable energy, solar radiation

Procedia PDF Downloads 159

Authors: Abdulfetah Shobole, Arif Karakas, Ugur Savas Selamogullari, Mustafa Baysal

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The concern of reducing emissions of Co2 from the fossil fuel generating units and using renewable energy sources increased in our world. Due this fact the integration ratio of wind farms to grid reached 20-30% in some part of our world. With increased integration of large MW scaled wind farms to the electric grid, the stability of the electrical system is a great concern. Thus, operators of power systems usually deman the wind turbine generators to obey the same rules as other traditional kinds of generation, such as thermal and hydro, i.e. not affect the grid stability. FACTS devices such as SVC or STATCOM are mostly installed close to the connection point of the wind farm to the grid in order to increase the stability especially during faulty conditions. In this paper wind farm with DFIG turbine type and STATCOM are dynamically modeled and simulated under three phase short circuit fault condition. The dynamic modeling is done by DigSILENT PowerFactory for the wind farm, STATCOM and the network. The simulation results show improvement of system stability near to the connection point of the STATCOM.

Keywords: DFIG wind turbine, statcom, dynamic modeling, digsilent

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Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

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The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

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Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Yassamina Berkane, Leila Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a new methodology for predicting peoples' intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples' intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: urban mobility, decongestion, machine learning, neural network

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Authors: Deeba N. Baig, Ateeqa Ijaz, Saloome Rafiq

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Camel is a religious and culturally significant animal in Asian and African regions. In Pakistan Dromedary and Bactrian are common camel breeds. Other than the transportation use, it is a pivotal source of milk and meat. The quality of its milk and meat is predominantly dependent on the geographical location and variety of vegetation available for the diet. Camel milk (CM) is highly nutritious because of its reduced cholesterol and sugar contents along with enhanced minerals and vitamins level. The absence of beta-lactoglobulin (like human milk), makes CM a safer alternative for infants and children having Cow Milk Allergy (CMA). In addition to this, it has a unique probiotic profile both in raw and fermented form. Number of Lactic acid bacteria (LAB) including lactococcus, lactobacillus, enterococcus, streptococcus, weissella, pediococcus and many other bacteria have been detected. From these LAB Lactobacilli, Bifidobacterium and Enterococcus are widely used commercially for fermentation purpose. CM has high therapeutic value as its effectiveness is known against various ailments like fever, arthritis, asthma, gastritis, hepatitis, Jaundice, constipation, postpartum care of women, anti-venom, dropsy etc. It also has anti-diabetic, anti-microbial, antitumor potential along with its robust efficacy in the treatment of auto-immune disorders. Recently, the role of CM has been explored in brain-gut axis for the therapeutics of neurodevelopmental disorders. In this connection, a lot of grey area was available to explore the probiotics and therapeutics latent in the CM available in Pakistan. Thus, current study was designed to explore the predominant probiotic flora and antimicrobial potential of CM from different local breeds of Pakistan. The probiotics have been identified through biochemical, physiological and ribo-typing methods. In addition to this, bacteriocins (antimicrobial-agents) were screened through PCR-based approach. Results of this study revealed that CM from different breeds of camel depicted a number of similar probiotic candidates along with the range of limited variability. However, the nucleotide sequence analysis of selected anti-listerial bacteriocins exposed least variability. As a conclusion, the CM has sufficient probiotic availability and significant anti-microbial potential.

Keywords: bacteriocins, camel milk, probiotics potential, therapeutics

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Authors: Jean-Francois Feller

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The analysis of the volatolome emitted by the human body with a sensor array (e-nose) is a method for clinical applications full of promises to make an olfactive fingerprint characteristic of people's health state. But the amount of volatile organic compounds (VOC) to detect, being in the range of parts per billion (ppb), and their diversity (several hundred) justifies developing ever more sensitive and selective vapor sensors to improve the discrimination ability of the e-nose, is still of interest. Quantum resistive vapour sensors (vQRS) made with nanostructured conductive polymer nanocomposite transducers have shown a great versatility in both their fabrication and operation to detect volatiles of interest such as cancer biomarkers. However, it has been shown that their chemo-resistive response was highly dependent on the quality of the inter-particular junctions in the percolated architecture. The present work investigates the effectiveness of poly(hedral oligomeric silsesquioxane) acting as a nanospacer to amplify the disconnectability of the conducting network and thus maximize the vQRS's sensitivity to VOC.

Keywords: volatolome, quantum resistive vapour sensor, nanostructured conductive polymer nanocomposites, olfactive diagnosis

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Authors: Hung Nguyen

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With the rise of global supply chain network, the choice of supply chain orientation is critical. The alignment between cultural similarity and supply chain information exchange could help identify appropriate supply chain orientations, which would differentiate the stronger competitors and performers from the weaker ones. Through developing a taxonomy, this study examined whether the choices of action programs and manufacturing performance differ depending on the levels of attainment cultural similarity and information exchange. This study employed statistical tests on a large-scale dataset consisting of 680 manufacturing plants from various cultures and industries. Firms need to align cultural practices with the level of information exchange in order to achieve good overall business performance. There appeared to be consistent three major orientations: the Proactive, the Initiative and the Reactive. Firms are experiencing higher payoffs from various improvements are the ones successful alignment in both information exchange and cultural similarity The findings provide step-by-step decision making for supply chain information exchange and offer guidance especially for global supply chain managers. In including both cultural similarity and information exchange, this paper adds greater comprehensiveness and richness to the supply chain literature.

Keywords: culture, information exchange, supply chain orientation, similarity

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Authors: P. S. Jagadeesh Kumar, Yang Yung, Mingmin Pan, Xianpei Li, Wenli Hu

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Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines.

Keywords: corneal disorders, deep Boltzmann machines, deep graphical model, glaucoma, neural networks, ophthalmic hashing

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Authors: Smriti Agarwal, Ashish Payal, B. V. R. Reddy

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IEEE 802.15.4 is a Low Rate Wireless Personal Area Networks (LR-WPAN) standard combined with ZigBee, which is going to enable new applications in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) domain. In recent years, it has become a popular standard for WSNs. Wireless communication among sensor motes, enabled by IEEE 802.15.4 standard, is extensively replacing the existing wired technology in a wide range of monitoring and control applications. Researchers have proposed a routing framework and mechanism that interacts with the IEEE 802.15.4 standard using software platform. In this paper, we have designed and implemented MAC based routing (MBR) based on IEEE 802.15.4 standard using a hardware platform “SENSEnuts”. The experimental results include data through light and temperature sensors obtained from communication between PAN coordinator and source node through coordinator, MAC address of some modules used in the experimental setup, topology of the network created for simulation and the remaining battery power of the source node. Our experimental effort on a WSN Testbed has helped us in bridging the gap between theoretical and practical aspect of implementing IEEE 802.15.4 for WSNs applications.

Keywords: IEEE 802.15.4, routing, WSN, ZigBee

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Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

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This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

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Authors: A. Yawootti, P. Intra, P. Sardyoung, P. Phoosomma, R. Puttipattanasak, S. Leeragreephol, N. Tippayawong

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The aim of this work is to design, develop and test the low-cost implementation of a particulate air pollution sensor system for continuous monitoring of outdoors and indoors particulate air pollution at a lower cost than existing instruments. In this study, measuring electrostatic charge of particles technique via high efficiency particulate-free air filter was carried out. The developed detector consists of a PM10 impactor, a particle charger, a Faraday cup electrometer, a flow meter and controller, a vacuum pump, a DC high voltage power supply and a data processing and control unit. It was reported that the developed detector was capable of measuring mass concentration of particulate ranging from 0 to 500 µg/m3 corresponding to number concentration of particulate ranging from 106 to 1012 particles/m3 with measurement time less than 1 sec. The measurement data of the sensor connects to the internet through a GSM connection to a public cellular network. In this development, the apparatus was applied the energy by a 12 V, 7 A internal battery for continuous measurement of about 20 hours. Finally, the developed apparatus was found to be close agreement with the import standard instrument, portable and benefit for air pollution and particulate matter measurements.

Keywords: particulate, air pollution, wireless communication, sensor

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Authors: Mojtaba Eshraghi Arani

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According to the international conventions for the carriage of goods by sea, the consignee is the person who is entitled to take delivery of the cargo from the carrier. Such a person is usually named in the relevant box of the bill of lading unless the latter is issued “To Order” or “To Bearer”. However, there are some cases in which the apparent consignee, as above, was not intended to take delivery of cargo, like the L/C issuing bank or the freight forwarder who are named as consignee only for the purpose of security or acceleration of transit process. In such cases as well as the BL which is issued “To Order”, the so-called “real consignee” can be found out in the “Notify Party” box. The dispute revolves around the choice between apparent consignee and real consignee for being entitled not only to take delivery of the cargo but also to sue the carrier for any damages or loss. While it is a generally accepted rule that only the apparent consignee shall be vested with such rights, some courts like France’s Cour de Cassation have declared that the “Notify Party”, as the real consignee, was entitled to sue the carrier and in some cases, the same court went far beyond and permitted the real consignee to take suit even where he was not mentioned on the BL as a “Notify Party”. The main argument behind such reasoning is that the real consignee is the person who suffered the loss and thus had a legitimate interest in bringing action; of course, the real consignee must prove that he incurred a loss. It is undeniable that the above-mentioned approach is contrary to the position of the international conventions on the express definition of consignee. However, international practice has permitted the use of BL in a different way to meet the business requirements of banks, freight forwarders, etc. Thus, the issue is one of striking a balance between the international conventions on the one hand and existing practices on the other hand. While the latest convention applicable for sea transportation, i.e., the Rotterdam Rules, dealt with the comparable issue of “shipper” and “documentary shipper”, it failed to cope with the matter being discussed. So a new study is required to propose the best solution for amending the current conventions for carriage of goods by sea. A qualitative method with the concept of interpretation of data collection has been used in this article. The source of the data is the analysis of domestic and international regulations and cases. It is argued in this manuscript that the judge is not allowed to recognize any one as real consignee, other than the person who is mentioned in the “Consingee” box unless the BL is issued “To Order” or “To Bearer”. Moreover, the contract of carriage is independent of the sale contract and thus, the consignee must be determined solely based on the facts of the BL itself, like “Notify Party” and not any other contract or document.

Keywords: real consignee, cargo, delivery, to order, notify the party

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Authors: Azam Abkhiz, Abolghasem Nasir

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To achieve sustainable competitive advantage in the market, it is necessary to provide and improve customer satisfaction and Loyalty. To reach this objective, companies need to identify and analyze their customers. Thus, it is critical to measure the level of customer satisfaction and Loyalty very carefully. This study attempts to build a conceptual model to provide clear insights of customer loyalty. Using Bayesian networks (BNs), a model is proposed to evaluate customer loyalty and its consequences, such as repurchase and positive word-of-mouth. BN is a probabilistic approach that predicts the behavior of a system based on observed stochastic events. The most relevant determinants of customer loyalty are identified by the literature review. Perceived value, service quality, trust, corporate image, satisfaction, and switching costs are the most important variables that explain customer loyalty. The data are collected by use of a questionnaire-based survey from 1430 customers of a home appliances manufacturer in Iran. Four scenarios and sensitivity analyses are performed to run and analyze the impact of different determinants on customer loyalty. The proposed model allows businesses to not only set their targets but proactively manage their customer behaviors as well.

Keywords: customer satisfaction, customer loyalty, Bayesian networks, home appliances industry

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Authors: M. Inzunza

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In contemporary society, victims of crime has been given more recognition, which have contributed to advancing the knowledge on the effects of crime. There exists a complexity of who gets the status of victim and that the typology of good versus bad can interfere with the contact situation of the victim with the police. The aim of this study is to identify the most central areas affecting the contact situation between crime victims and the police to develop a conceptual model to be useful empirically. By considering previously documented problem areas and different theoretical domains, a conceptual model has been developed. Preliminary findings suggest that an area that should be given attention is to get a better understanding of the victim, not only in terms of demographics but also in terms of risk behavior and social network. This area has been considered to influence the status of the crime victim. Another domain of value is the type of crime and the context of the incident in more detail. The police officer approach style in the contact situation is also a pertinent area that is influenced by how the police based victim services are organized and how individual police officers are suited for the mission. Suitability includes constructs from empathy models adapted to the police context and especially focusing on sub-constructs such as perspective taking. Discussion will focus on how these findings can be operationalized in practice and how they are used in ongoing empirical studies.

Keywords: empathy, perspective taking, police contact, victim of crime

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Authors: Dong-Cheol Jeong, Jookyung Lee, Yu Hyeon Ro, Changsik Song

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The design and synthesis of photo-active polymeric systems are important in regard to solar energy harvesting and utilization. In this study, we synthesized photo-active polymer, thin films, and polymer gel via iterative self-assembly using reversible metal-terpyridine (M-tpy) interactions. The photocurrent generated in the polymeric thin films with Zn(II) was much higher than those of other films. Apparent diffusion rate constant (kapp) was measured for the electron hopping process via potential-step chronoamperometry. As a result, the kapp for the polymeric thin films with Zn(II) was almost two times larger than those with other metal ions. We found that the anodic photocurrents increased with the inclusion of the multi-walled carbon nanotube (MWNT) layer. Inclusion of MWNTs can provide efficient electron transfer pathways. In addition, polymer gel based on interactions between terpyridine and metal ions was shown the photocatalytic activity. Interestingly, in the Mg-terpyridine gel, the reaction rate of benzylamine to imine photo-oxidative coupling was faster than Fe-terpyridine gel because the Mg-terpyridine gel has two steps electron transfer pathway but Fe-terpyridine gel has three steps electron transfer pathway.

Keywords: terpyridine, photocatalyst, self-assebly, metal-ligand

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Authors: Daniel Osorio, Janneth Gonzalez, Andres Pinzon

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In this work, proteins and metabolic pathways associated with the neuroprotective response mediated by the synthetic neurosteroid tibolone under a palmitate-induced inflammatory model were identified by flux balance analysis (FBA). Three different metabolic scenarios (‘healthy’, ‘inflamed’ and ‘medicated’) were modeled over a gene expression data-driven constructed tissue-specific metabolic reconstruction of mature astrocytes. Astrocyte reconstruction was built, validated and constrained using three open source software packages (‘minval’, ‘g2f’ and ‘exp2flux’) released through the Comprehensive R Archive Network repositories during the development of this work. From our analysis, we predict that tibolone executes their neuroprotective effects through a reduction of neurotoxicity mediated by L-glutamate in astrocytes, inducing the activation several metabolic pathways with neuroprotective actions associated such as taurine metabolism, gluconeogenesis, calcium and the Peroxisome Proliferator Activated Receptor signaling pathways. Also, we found a tibolone associated increase in growth rate probably in concordance with previously reported side effects of steroid compounds in other human cell types.

Keywords: astrocytes, flux balance analysis, genome scale metabolic reconstruction, inflammation, neuroprotection, tibolone

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Nathenal Thomas Lambamo

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Based on the fact that more than 85% of the labor force and 90% of the export earnings are taken by agriculture in Ethiopia and it can be said that it is the backbone of the overall socio-economic activities in the country. Among the cereal crops that the agriculture sector provides for the country, wheat is the third-ranking one preceding teff and maize. In the present day, wheat is in higher demand related to the expansion of industries that use them as the main ingredient for their products. The local supply of wheat for these companies covers only 35 to 40% and the rest 60 to 65% percent is imported on behalf of potential customers that exhaust the country’s foreign currency reserves. The above facts show that the need for this crop in the country is too high and in reverse, the productivity of the crop is very less because of these reasons. Wheat disease is the most devastating disease that contributes a lot to this unbalance in the demand and supply status of the crop. It reduces both the yield and quality of the crop by 27% on average and up to 37% when it is severe. This study aims to detect the most frequent and degrading wheat diseases, Septoria and Leaf rust, using the most efficiently used subset of machine learning technology, deep learning. As a state of the art, a deep learning class classification technique called Convolutional Neural Network (CNN) has been used to detect diseases and has an accuracy of 99.01% is achieved.

Keywords: septoria, leaf rust, deep learning, CNN

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Authors: Seyed Abbas Tabatabaei, Fereydoon Moghadas Nejad, Mohammad Saed

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The bleeding prediction of the asphalt is one of the most complex subjects in the pavement engineering. In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) is used for modeling the effect of important parameters on bleeding is trained and tested with the experimental results. bleeding index based on the asphalt film thickness differential as target parameter,asphalt content, temperature depth of two centemeter, heavy traffic, dust to effective binder, Marshall strength, passing 3/4 sieves, passing 3/8 sieves,passing 3/16 sieves, passing NO8, passing NO50, passing NO100, passing NO200 as input parameters. Then, we randomly divided empirical data into train and test sections in order to accomplish modeling. We instructed ANFIS network by 72 percent of empirical data. 28 percent of primary data which had been considered for testing the approprativity of the modeling were entered into ANFIS model. Results were compared by two statistical criterions (R2, RMSE) with empirical ones. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can also be promoted to more general states.

Keywords: bleeding, asphalt film thickness differential, Anfis Modeling

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: A. N. Dagang, E. I. Ismail, Z. Zakaria

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This paper presents the analysis on the performance of monopole antenna with fluorescent tubes. In this research, the simulation and experimental approach is conducted. The fluorescent tube with different length and size is designed using Computer Simulation Technology (CST) software and the characteristics of antenna parameter are simulated throughout the software. CST was used to simulate antenna parameters such as return loss, resonant frequency, gain and directivity. Vector Network Analyzer (VNA) was used to measure the return loss of plasma antenna in order to validate the simulation results. In the simulation and experiment, the supply frequency is set starting from 1 GHz to 10 GHz. The results show that the return loss of plasma antenna changes when size of fluorescent tubes is varied, correspond to the different plasma properties. It shows that different values of plasma properties such as plasma frequency and collision frequency gives difference result of return loss, gain and directivity. For the gain, the values range from 2.14 dB to 2.36 dB. The return loss of plasma antenna offers higher value range from -22.187 dB to -32.903 dB. The higher the values of plasma frequency and collision frequency, the higher return loss can be obtained. The values obtained are comparative to the conventional type of metal antenna.

Keywords: plasma antenna, fluorescent tube, CST, plasma parameters

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Authors: Kulkarni Rohini D.

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While emerging as plausible sources of energy generation, new technologies, including photovoltaic (PV) solar panels, home battery energy storage systems, and electric vehicles (EVs), are exacerbating the operations of power distribution networks for distribution network operators (DNOs). Renewable energy production fluctuates, stemming in over- and under-generation energy, further complicating the issue of storing excess power and using it when necessary. Though renewable sources are non-exhausting and reoccurring, power storage of generated energy is almost as paramount as to its production process. Hence, to ensure smooth and efficient power storage at different levels, Grid balancing technologies are consequently the next theme to address in the sustainable space and growth sector. But, since hydrogen batteries were used in the earlier days to achieve this balance in power grids, new, recent advancements are more efficient and capable per unit of storage space while also being distinctive in terms of their underlying operating principles. The underlying technologies of "Flow batteries," "Gravity Solutions," and "Graphene Batteries" already have entered the market and are leading the race for efficient storage device solutions that will improve and stabilize Grid networks, followed by Grid balancing technologies.

Keywords: flow batteries, grid balancing, hydrogen batteries, power storage, solar

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Authors: Wenting Ye

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The biological materials evolved in nature generally exhibit interpenetrating network structures, which may offer useful inspiration for the architectural design of wear-resistant composites. Here, a strategy for designing self-lubricating medium entropy alloy (MEA) composites with high strength and excellent anti-wear performance was proposed through quasi-continuously networked in-situ carbides and graphene nanosheets. The discontinuous coating of graphene on the MEA powder surface inhibits continuous metallurgy bonding of the MEA powders during sintering, generating the typical quasi-continuously networked architecture. A good combination of mechanical properties with high fracture strength over 2 GPa and large compressive plasticity over 30% benefits from metallurgy bonding that prevents crack initiation and extension. The wear rate of an order of 10-6 m3N-1m-1 ascribing to an amorphous-crystalline nanocomposite surface, tribo-film induced by graphene, as well as the gradient worn subsurface during friction was achieved by the MEA composite, which is an order of magnitude lower than the unreinforced MEA matrix.

Keywords: in-situ carbide, tribological behavior, medium entropy alloy matrix composite, graphene

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Authors: L. Parisi

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In this research work, neural networks were applied to classify two types of hip joint implants based on the relative hip joint implant side speed and three components of each ground reaction force. The condition of walking gait at normal velocity was used and carried out with each of the two hip joint implants assessed. Ground reaction forces’ kinetic temporal changes were considered in the first approach followed but discarded in the second one. Ground reaction force components were obtained from eighteen patients under such gait condition, half of which had a hip implant type I-II, whilst the other half had the hip implant, defined as type III by Orthoload®. After pre-processing raw gait kinetic data and selecting the time frames needed for the analysis, the ground reaction force components were used to train a MLP neural network, which learnt to distinguish the two hip joint implants in the abovementioned condition. Further to training, unknown hip implant side and ground reaction force components were presented to the neural networks, which assigned those features into the right class with a reasonably high accuracy for the hip implant type I-II and the type III. The results suggest that neural networks could be successfully applied in the performance assessment of hip joint implants.

Keywords: kinemic gait data, neural networks, hip joint implant, hip arthroplasty, rehabilitation engineering

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Authors: Simone Dell'Agnello

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There are laser retroreflectors on the Moon and no laser retroreflectors on Mars. Here we describe the design, construction, qualification and imminent deployment of next-generation, optimized laser retroreflectors on the Moon and on Mars (where they will be the first ones). These instruments are positioned by time-of-flight measurements of short laser pulses, the so-called 'laser ranging' technique. Data analysis is carried out with PEP, the Planetary Ephemeris Program of CfA (Center for Astrophysics). Since 1969 Lunar Laser Ranging (LLR) to Apollo/Lunokhod laser retro-reflector (CCR) arrays supplied accurate tests of General Relativity (GR) and new gravitational physics: possible changes of the gravitational constant Gdot/G, weak and strong equivalence principle, gravitational self-energy (Parametrized Post Newtonian parameter beta), geodetic precession, inverse-square force-law; it can also constraint gravitomagnetism. Some of these measurements also allowed for testing extensions of GR, including spacetime torsion, non-minimally coupled gravity. LLR has also provides significant information on the composition of the deep interior of the Moon. In fact, LLR first provided evidence of the existence of a fluid component of the deep lunar interior. In 1969 CCR arrays contributed a negligible fraction of the LLR error budget. Since laser station range accuracy improved by more than a factor 100, now, because of lunar librations, current array dominate the error due to their multi-CCR geometry. We developed a next-generation, single, large CCR, MoonLIGHT (Moon Laser Instrumentation for General relativity high-accuracy test) unaffected by librations that supports an improvement of the space segment of the LLR accuracy up to a factor 100. INFN also developed INRRI (INstrument for landing-Roving laser Retro-reflector Investigations), a microreflector to be laser-ranged by orbiters. Their performance is characterized at the SCF_Lab (Satellite/lunar laser ranging Characterization Facilities Lab, INFN-LNF, Frascati, Italy) for their deployment on the lunar surface or the cislunar space. They will be used to accurately position landers, rovers, hoppers, orbiters of Google Lunar X Prize and space agency missions, thanks to LLR observations from station of the International Laser Ranging Service in the USA, in France and in Italy. INRRI was launched in 2016 with the ESA mission ExoMars (Exobiology on Mars) EDM (Entry, descent and landing Demonstration Module), deployed on the Schiaparelli lander and is proposed for the ExoMars 2020 Rover. Based on an agreement between NASA and ASI (Agenzia Spaziale Italiana), another microreflector, LaRRI (Laser Retro-Reflector for InSight), was delivered to JPL (Jet Propulsion Laboratory) and integrated on NASA’s InSight Mars Lander in August 2017 (launch scheduled in May 2018). Another microreflector, LaRA (Laser Retro-reflector Array) will be delivered to JPL for deployment on the NASA Mars 2020 Rover. The first lunar landing opportunities will be from early 2018 (with TeamIndus) to late 2018 with commercial missions, followed by opportunities with space agency missions, including the proposed deployment of MoonLIGHT and INRRI on NASA’s Resource Prospectors and its evolutions. In conclusion, we will extend significantly the CCR Lunar Geophysical Network and populate the Mars Geophysical Network. These networks will enable very significantly improved tests of GR.

Keywords: general relativity, laser retroreflectors, lunar laser ranging, Mars geodesy

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Authors: Hasan Ustun Basaran

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Diesel engines are widely used in transportation sector due to their high thermal efficiency. However, they also release high rates of NOₓ and PM (particulate matter) emissions into the environment which have hazardous effects on human health. Therefore, environmental protection agencies have issued strict emission regulations on automotive diesel engines. Recently, these regulations are even increasingly strengthened. Engine producers search novel on-engine methods such as advanced combustion techniques, utilization of renewable fuels, exhaust gas recirculation, advanced fuel injection methods or use exhaust after-treatment (EAT) systems in order to reduce emission rates on diesel engines. Although those aforementioned on-engine methods are effective to curb emission rates, they result in inefficiency or cannot decrease emission rates satisfactorily at all operating conditions. Therefore, engine manufacturers apply both on-engine techniques and EAT systems to meet the stringent emission norms. EAT systems are highly effective to diminish emission rates, however, they perform inefficiently at low loads due to low exhaust gas temperatures (below 250°C). Therefore, the objective of this study is to demonstrate that engine-out temperatures can be elevated above 250°C at low-loaded cases via cylinder disablement. The engine studied and modeled via Lotus Engine Simulation (LES) software is a six-cylinder turbocharged and intercooled diesel engine. Exhaust temperatures and mass flow rates are predicted at 1200 rpm engine speed and several low loaded conditions using LES program. It is seen that cylinder deactivation results in a considerable exhaust temperature rise (up to 100°C) at low loads which ensures effective EAT management. The method also improves fuel efficiency through reduced total pumping loss. Decreased total air induction due to inactive cylinders is thought to be responsible for improved engine pumping loss. The technique reduces exhaust gas flow rate as air flow is cut off on disabled cylinders. Still, heat transfer rates to the after-treatment catalyst bed do not decrease that much since exhaust temperatures are increased sufficiently. Simulation results are promising; however, further experimental studies are needed to identify the true potential of the method on fuel consumption and EAT improvement.

Keywords: cylinder disablement, diesel engines, exhaust after-treatment, exhaust temperature, fuel efficiency

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Authors: Camille Aouinaït, Danilo Christen, Christoph Carlen

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Introduction: Small and Medium-sized Enterprises (SMEs) are facing different challenges such as pressures on environmental resources, the rise of downstream power, and trade liberalization. Remaining competitive by implementing innovations and engaging in collaborations could be a strategic solution. In Switzerland, the Federal Institute for Research in Agriculture (Agroscope), the Federal schools of technology (EPFL and ETHZ), Cantonal universities and Universities of Applied Sciences (UAS) can provide substantial inputs. UAS were developed with specific missions to match the labor markets and society needs. Research projects produce patents, publications and improved networks of scientific expertise. The study’s goal is to measure the contribution of UAS and research organization to innovation and the impact of collaborations with partners in the non-academic environment in Swiss traditional fruit production. Materials and methods: The European projects Traditional Food Network to improve the transfer of knowledge for innovation (TRAFOON) and Social Impact Assessment of Productive Interactions between science and society (SIAMPI) frame the present study. The former aims to fill the gap between the needs of traditional food producing SMEs and innovations implemented following European projects. The latter developed a method to assess the impacts of scientific research. On one side, interviews with market players have been performed to make an inventory of needs of Swiss SMEs producing apricots and berries. The participative method allowed matching the current needs and the existing innovations coming from past European projects. Swiss stakeholders (e.g. producers, retailers, an inter-branch organization of fruits and vegetables) directly rated the needs on a five-Likert scale. To transfer the knowledge to SMEs, training workshops have been organized for apricot and berries actors separately, on specific topics. On the other hand, a mapping of a social network is drawn to characterize the links between actors, with a focus on the Swiss canton of Valais and UAS Valais Wallis. Type and frequency of interactions among actors have identified thanks to interviews. Preliminary results: A list of 369 SMEs needs grouped in 22 categories was produced with 37 fulfilled questionnaires. Swiss stakeholders rated 31 needs very important. Training workshops on apricot are focusing on varietal innovations, storage, disease (bacterial blight), pest (Drosophila suzukii), sorting and rootstocks. Entrepreneurship was targeted through trademark discussions in berry production. The UAS Valais Wallis collaborated on a few projects with Agroscope along with industries, at European and national levels. Political and public bodies interfere with the central area of agricultural vulgarization that induces close relationships between the research and the practical side. Conclusions: The needs identified by Swiss stakeholders are becoming part of training workshops to incentivize innovations. The UAS Valais Wallis takes part in collaboration projects with the research environment and market players that bring innovations helping SMEs in their contextual environment. Then, a Strategic Research and Innovation Agenda will be created in order to pursue research and answer the issues facing by SMEs.

Keywords: agriculture, innovation, knowledge transfer, university and research collaboration

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Authors: Mohamed Moustafa, Medhat Abdallah, Ramy Magdy, Ahmed Abdrabou, Mohamed Badr

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The scribe box under study dates back to the old kingdom. It was excavated by the Italian expedition in Qena (1935-1937). The box consists of 2pieces, the lid and the body. The inner side of the lid is decorated with ancient Egyptian inscriptions written with a black pigment. The box was made using several panels assembled together by wooden dowels and secured with plant ropes. The entire box is covered with a red pigment. This study aims to use analytical techniques in order to identify and have deep understanding for the box components. Moreover, the authors were significantly interested in using infrared reflectance transmission imaging (RTI-IR) to improve the hidden inscriptions on the lid. The identification of wood species included in this study. The visual observation and assessment were done to understand the condition of this box. 3Ddimensions and 2D programs were used to illustrate wood joints techniques. Optical microscopy (OM), X-ray diffraction (XRD), X-ray fluorescence portable (XRF) and Fourier Transform Infrared spectroscopy (FTIR) were used in this study in order to identify wood species, remains of insects bodies, red pigment, fibers plant and previous conservation adhesives, also RTI-IR technique was very effective to improve hidden inscriptions. The analysis results proved that wooden panels and dowels were identified as Acacia nilotica, wooden rail was Salix sp. the insects were identified as Lasioderma serricorne and Gibbium psylloids, the red pigment was Hematite, while the fiber plants were linen, previous adhesive was identified as cellulose nitrates. The historical study for the inscriptions proved that it’s a Hieratic writings of a funerary Text. After its transportation from the Egyptian museum storage to the wood conservation laboratory of the Grand Egyptian museum –conservation center (GEM-CC), conservation techniques were applied with high accuracy in order to restore the object including cleaning , consolidating of friable pigments and writings, removal of previous adhesive and reassembly, finally the conservation process that were applied were extremely effective for this box which became ready for display or storage in the grand Egyptian museum.

Keywords: scribe box, hieratic, 3D program, Acacia nilotica, XRD, cellulose nitrate, conservation

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Authors: Mohanad Alhabo, Naveed Nawaz

Abstract:

The ongoing call or data session must be maintained to ensure a good quality of service. This can be accomplished by performing the handover procedure while the user is on the move. However, the dense deployment of small cells in 5G networks is a challenging issue due to the extensive number of handovers. In this paper, a neighbour cell list method is proposed to reduce the number of target small cells and hence minimizing the number of handovers. The neighbour cell list is built by omitting cells that could cause an unnecessary handover and handover failure because of short time of stay of the user in these cells. A multi-attribute decision making technique, simple additive weighting, is then applied to the optimized neighbour cell list. Multi-tier small cells network is considered in this work. The performance of the proposed method is analysed and compared with that of the existing methods. Results disclose that our method has decreased the candidate small cell list, unnecessary handovers, handover failure, and short time of stay cells compared to the competitive method.

Keywords: handover, HetNets, multi-attribute decision making, small cells

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