Search results for: intelligent tuning

Authors: Magdy Roman, Mostafa Shoeib, Mostafa Rostom

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Global vision, whether provided by overhead fixed cameras, on-board aerial vehicle cameras, or satellite images can always provide detailed information on the environment around mobile robots. In this paper, an intelligent vision-based method of path planning and obstacle avoidance for mobile robots is presented. The method integrates visual perception with a new proposed field-based path-planning method to overcome common path-planning problems such as local minima, unreachable destination and unnecessary lengthy paths around obstacles. The method proposes an exponential angle deviation field around each obstacle that affects the orientation of a close robot. As the robot directs toward, the goal point obstacles are classified into right and left groups, and a deviation angle is exponentially added or subtracted to the orientation of the robot. Exponential field parameters are chosen based on Lyapunov stability criterion to guarantee robot convergence to the destination. The proposed method uses obstacles' shape and location, extracted from global vision system, through a collision prediction mechanism to decide whether to activate or deactivate obstacles field. In addition, a search mechanism is developed in case of robot or goal point is trapped among obstacles to find suitable exit or entrance. The proposed algorithm is validated both in simulation and through experiments. The algorithm shows effectiveness in obstacles' avoidance and destination convergence, overcoming common path planning problems found in classical methods.

Keywords: path planning, collision avoidance, convergence, computer vision, mobile robots

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Authors: Khiari Nor El Houda, Hiouani Amira Sarra

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Many EFL students with high capacities are hidden because they suffer from speaking anxiety (SA). Most of them find public speaking much demanding. They feel unable to communicate, they fear to make mistakes and they fear negative evaluation or being called on. With the growing number of the learners who suffer from foreign language speaking anxiety (FLSA), it is becoming increasingly difficult to ignore its harmful outcomes on their performance and success, especially during their first contact with the pupils, as they will be teaching in the near future. Different researchers suggested different ways to minimize the negative effects of FLSA. The present study sheds light on emotional intelligence skills training as an effective strategy not only to influence public speaking success but also to help pre-service EFL teachers lessen their speaking anxiety and eventually to prepare them for their professional career. A quasi-experiment was used in order to examine the research hypothesis. We worked with two groups of third-year EFL students at Oum El Bouaghi University. The Foreign Language Classroom Anxiety Scale (FLCAS) and the Emotional Quotient Inventory (EQ-i) were used to collect data about the participants’ FLSA and EI levels. The analysis of the data has yielded that the assumption that there is a negative correlation between EI and FLSA was statistically validated by the Pearson Correlation Test, concluding that, the more emotionally intelligent the individual is the less anxious s/he will be. In addition, the lack of amelioration in the results of the control group and the noteworthy improvement in the experimental group results led us to conclude that EI skills training was an effective strategy in minimizing the FLSA level and therefore, we confirmed our research hypothesis.

Keywords: emotional intelligence, emotional intelligence skills training, EQ-I, FLCAS, foreign language speaking anxiety, pre-service EFL teachers

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Authors: N. Renuka, R. Ramesh Babu, N. Vijayan

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Ion beams play a significant role in the process of tuning the properties of materials. Based on the radiation behavior, the engineering materials are categorized into two different types. The first one comprises organic solids which are sensitive to the energy deposited in their electronic system and the second one comprises metals which are insensitive to the energy deposited in their electronic system. However, exposure to swift heavy ions alters this general behavior. Depending on the mass, kinetic energy and nuclear charge, an ion can produce modifications within a thin surface layer or it can penetrate deeply to produce long and narrow distorted area along its path. When a high energetic ion beam impinges on a material, it causes two different types of changes in the material due to the columbic interaction between the target atom and the energetic ion beam: (i) inelastic collisions of the energetic ion with the atomic electrons of the material; and (ii) elastic scattering from the nuclei of the atoms of the material, which is extremely responsible for relocating the atoms of matter from their lattice position. The exposure of the heavy ions renders the material return to equilibrium state during which the material undergoes surface and bulk modifications which depends on the mass of the projectile ion, physical properties of the target material, its energy, and beam dimension. It is well established that electronic stopping power plays a major role in the defect creation mechanism provided it exceeds a threshold which strongly depends on the nature of the target material. There are reports available on heavy ion irradiation especially on crystalline materials to tune their physical and chemical properties. L-Arginine Acetate [LAA] is a potential semi-organic nonlinear optical crystal and its optical, mechanical and thermal properties have already been reported The main objective of the present work is to enhance or tune the structural and optical properties of LAA single crystals by heavy ion irradiation. In the present study, a potential nonlinear optical single crystal, L-arginine acetate (LAA) was grown by slow evaporation solution growth technique. The grown LAA single crystal was irradiated with oxygen ions at the dose rate of 600 krad and 1M rad in order to tune the structural and optical properties. The structural properties of pristine and oxygen ions irradiated LAA single crystals were studied using Powder X- ray diffraction and Fourier Transform Infrared spectral studies which reveal the structural changes that are generated due to irradiation. Optical behavior of pristine and oxygen ions irradiated crystals is studied by UV-Vis-NIR and photoluminescence analyses. From this investigation we can concluded that oxygen ions irradiation modifies the structural and optical properties of LAA single crystals.

Keywords: heavy ion irradiation, NLO single crystal, photoluminescence, X-ray diffractometer

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Authors: Lina F. Ballesteros, Hafsae Lamsaf, Miguel A. Cerqueira, Lorenzo M. Pastrana, Sandra Carvalho, Jose A. Teixeira, S. Calderon V.

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The use of bio-based packaging materials containing metallic and bimetallic nanostructures is relatively modern technology. In this sense, the food packaging industry has been investigating biological and renewable resources that can replace petroleum-based materials to reduce the environmental impact and, at the same time, including new functionalities using nanotechnology. Therefore, the main objective of the present work consisted of developing bio-based poly-lactic acid (PLA) films with Zinc (Zn) and Zinc-Iron (Zn-Fe) nanostructures deposited by magnetron sputtering. The structural, antimicrobial, and optical properties of the films were evaluated when exposed at 60% and 96% relative humidity (RH). The morphology and elemental analysis of the samples were determined by scanning (transmission) electron microscopy (SEM and STEM), and inductively coupled plasma optical emission spectroscopy (ICP-OES). The structure of the PLA was monitored before and after deposition by Fourier transform infrared spectroscopy (FTIR) analysis, and the antimicrobial and color assays were performed by using the zone of inhibition (ZOI) test and a Minolta colorimeter, respectively. Finally, the films were correlated in terms of the deposit conditions, Zn or Zn-Fe concentrations, and thickness. The results revealed PLA films with different morphologies, compositions, and thicknesses of Zn or Zn-Fe nanostructures. The samples showed a significant antibacterial and antifungal activity against E. coli, P. aeruginosa, P. fluorescens, S. aureus, and A. niger, and considerable changes of color and opacity at 96% RH, especially for the thinner nanostructures (150-250 nm). On the other hand, when the Fe fraction was increased, the lightness of samples increased, as well as their antimicrobial activity when compared to the films with pure Zn. Hence, these findings are relevant to the food packaging field since intelligent and active films with multiple properties can be developed.

Keywords: biopolymers, functional properties, magnetron sputtering, Zn and Zn-Fe nanostructures

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Authors: M. Pushparani, A. Sagaya

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Embedded systems have emerged as important elements in various domains with extensive applications in automotive, commercial, consumer, healthcare and transportation markets, as there is emphasis on intelligent devices. On the other hand, Business Intelligence (BI) has also been extensively used in a range of applications, especially in the agriculture domain which is the area of this research. The aim of this research is to create a framework for Embedded Weight Comparison Algorithm with Business Intelligence (EWCA-BI). The weight comparison algorithm will be embedded within the plantation management system and the weighbridge system. This algorithm will be used to estimate the weight at the site and will be compared with the actual weight at the plantation. The algorithm will be used to build the necessary alerts when there is a discrepancy in the weight, thus enabling better decision making. In the current practice, data are collected from various locations in various forms. It is a challenge to consolidate data to obtain timely and accurate information for effective decision making. Adding to this, the unstable network connection leads to difficulty in getting timely accurate information. To overcome the challenges embedding is done on a portable device that will have the embedded weight comparison algorithm to also assist in data capture and synchronize data at various locations overcoming the network short comings at collection points. The EWCA-BI will provide real-time information at any given point of time, thus enabling non-latent BI reports that will provide crucial information to enable efficient operational decision making. This research has a high potential in bringing embedded system into the agriculture industry. EWCA-BI will provide BI reports with accurate information with uncompromised data using an embedded system and provide alerts, therefore, enabling effective operation management decision-making at the site.

Keywords: embedded business intelligence, weight comparison algorithm, oil palm plantation, embedded systems

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Authors: Saeed Poormoaied

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Perishability and developing an intelligent control policy for perishable items are the major concerns of marketing managers in a supply chain. In this study, we address a two-echelon supply chain problem for perishable items with a single vendor and a single buyer. The buyer adopts an aged-based continuous review policy which works by taking both the stock level and the aging process of items into account. The vendor works under the warehouse framework, where its lot size is determined with respect to the batch size of the buyer. The model holds for a positive and fixed lead time for the buyer, and zero lead time for the vendor. The demand follows a Poisson process and any unmet demand is lost. We provide exact analytic expressions for the operational characteristics of the system by using the renewal reward theorem. Items have a fixed lifetime after which they become unusable and are disposed of from the buyer's system. The age of items starts when they are unpacked and ready for the consumption at the buyer. When items are held by the vendor, there is no aging process which results in no perishing at the vendor's site. The model is developed under the centralized framework, which takes the expected profit of both vendor and buyer into consideration. The goal is to determine the optimal policy parameters under the service level constraint at the retailer's site. A sensitivity analysis is performed to investigate the effect of the key input parameters on the expected profit and order quantity in the supply chain. The efficiency of the proposed age-based policy is also evaluated through a numerical study. Our results show that when the unit perishing cost is negligible, a significant cost saving is achieved.

Keywords: two-echelon supply chain, perishable items, age-based policy, renewal reward theorem

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Authors: Abdul Basit Kiani

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The software for the digital library system is a cutting-edge solution designed to revolutionize the way libraries manage and provide access to their vast collections of digital content. This advanced software leverages the power of technology to offer a seamless and user-friendly experience for both library staff and patrons. By implementing this software, libraries can efficiently organize, store, and retrieve digital resources, including e-books, audiobooks, journals, articles, and multimedia content. Its intuitive interface allows library staff to effortlessly manage cataloging, metadata extraction, and content enrichment, ensuring accurate and comprehensive access to digital materials. For patrons, the software offers a personalized and immersive digital library experience. They can easily browse the digital catalog, search for specific items, and explore related content through intelligent recommendation algorithms. The software also facilitates seamless borrowing, lending, and preservation of digital items, enabling users to access their favorite resources anytime, anywhere, on multiple devices. With robust security features, the software ensures the protection of intellectual property rights and enforces access controls to safeguard sensitive content. Integration with external authentication systems and user management tools streamlines the library's administration processes, while advanced analytics provide valuable insights into patron behavior and content usage. Overall, this software for the digital library system empowers libraries to embrace the digital era, offering enhanced access, convenience, and discoverability of their vast collections. It paves the way for a more inclusive and engaging library experience, catering to the evolving needs of tech-savvy patrons.

Keywords: software development, empowering digital libraries, digital cataloging and access, management system

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Authors: Thomas Schalow

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This paper posits the need to take a cross-cultural approach to communication with non-human cultures and intelligences in order to meet the following three imminent contingencies: communicating with sentient biological intelligences, communicating with extraterrestrial intelligences, and communicating with artificial super-intelligences. The paper begins with a discussion of how intelligence emerges. It disputes some common assumptions we maintain about consciousness, intention, and language. The paper next explores cross-cultural communication among humans, including non-sapiens species. The next argument made is that we need to become much more serious about communicating with the non-human, intelligent life forms that already exist around us here on Earth. There is an urgent need to broaden our definition of communication and reach out to the other sentient life forms that inhabit our world. The paper next examines the science and philosophy behind CETI (communication with extraterrestrial intelligences) and how it has proven useful, even in the absence of contact with alien life. However, CETI’s assumptions and methodology need to be revised and based on the cross-cultural approach to communication proposed in this paper if we are truly serious about finding and communicating with life beyond Earth. The final theme explored in this paper is communication with non-biological super-intelligences using a cross-cultural communication approach. This will present a serious challenge for humanity, as we have never been truly compelled to converse with other species, and our failure to seriously consider such intercourse has left us largely unprepared to deal with communication in a future that will be mediated and controlled by computer algorithms. Fortunately, our experience dealing with other human cultures can provide us with a framework for this communication. The basic assumptions behind intercultural communication can be applied to the many types of communication envisioned in this paper if we are willing to recognize that we are in fact dealing with other cultures when we interact with other species, alien life, and artificial super-intelligence. The ideas considered in this paper will require a new mindset for humanity, but a new disposition will prepare us to face the challenges posed by a future dominated by artificial intelligence.

Keywords: artificial intelligence, CETI, communication, culture, language

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Authors: Dorottya Guban, Irina Borbath, Istvan Bakos, Peter Nemeth, Andras Tompos

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One of the greatest challenges of the implementation of polymer electrolyte membrane fuel cells (PEMFCs) is to find active and durable electrocatalysts. The cell performance is always limited by the oxygen reduction reaction (ORR) on the cathode since it is at least 6 orders of magnitude slower than the hydrogen oxidation on the anode. Therefore high loading of Pt is required. Catalyst corrosion is also more significant on the cathode, especially in case of mobile applications, where rapid changes of loading have to be tolerated. Pt-Sn bulk alloys and SnO2-decorated Pt3Sn nanostructures are among the most studied bimetallic systems for fuel cell applications. Exclusive formation of supported Sn-Pt alloy phases with different Pt/Sn ratios can be achieved by using controlled surface reactions (CSRs) between hydrogen adsorbed on Pt sites and tetraethyl tin. In this contribution our results for commercial and a home-made 20 wt.% Pt/C catalysts modified by tin anchoring via CSRs are presented. The parent Pt/C catalysts were synthesized by modified NaBH4-assisted ethylene-glycol reduction method using ethanol as a solvent, which resulted either in dispersed and highly stable Pt nanoparticles or evenly distributed raspberry-like agglomerates according to the chosen synthesis parameters. The 20 wt.% Pt/C catalysts prepared that way showed improved electrocatalytic performance in the ORR and stability in comparison to the commercial 20 wt.% Pt/C catalysts. Then, in order to obtain Sn-Pt/C catalysts with Pt/Sn= 3 ratio, the Pt/C catalysts were modified with tetraethyl tin (SnEt4) using three and five consecutive tin anchoring periods. According to in situ XPS studies in case of catalysts with highly dispersed Pt nanoparticles, pre-treatment in hydrogen even at 170°C resulted in complete reduction of the ionic tin to Sn0. No evidence of the presence of SnO2 phase was found by means of the XRD and EDS analysis. These results demonstrate that the method of CSRs is a powerful tool to create Pt-Sn bimetallic nanoparticles exclusively, without tin deposition onto the carbon support. On the contrary, the XPS results revealed that the tin-modified catalysts with raspberry-like Pt agglomerates always contained a fraction of non-reducible tin oxide. At the same time, they showed increased activity and long-term stability in the ORR than Pt/C, which was assigned to the presence of SnO2 in close proximity/contact with Pt-Sn alloy phase. It has been demonstrated that the content and dispersion of the fcc Pt3Sn phase within the electrocatalysts can be controlled by tuning the reaction conditions of CSRs. The bimetallic catalysts displayed an outstanding performance in the ORR. The preparation of a highly dispersed 20Pt/C catalyst permits to decrease the Pt content without relevant decline in the electrocatalytic performance of the catalysts.

Keywords: anode catalyst, cathode catalyst, controlled surface reactions, oxygen reduction reaction, PtSn/C electrocatalyst

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Authors: Adeleye Johnson Adelagunayeja

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The concept of making cities, communities, and neighborhoods smart, intelligent, and responsive is relatively new to Africa and its urban renewal agencies. Efforts must be made by relevant agencies to begin a holistic review of the implementation of infrastructural facilities and urban renewal methodologies that will revolve around the appreciation and application of artificial intelligence. The propagation of the ideals and benefits of the smart city concept are key factors that can encourage governments of African nations, the African Union, and other regional organizations in Africa to embrace the ideology. The ability of this smart city concept to curb insecurities – armed robbery, assassination, terrorism, and civil disorder – is one major reason, amongst others, why African governments must speedily embrace this contemporary developmental concept whose time has come! The seamlessness to access information and virtually cross-pollinate ideas with people living in already established smart cities, when combined with the great efficiency that the emergence of smart cities brings with it, are other reasons why Africa must come up with action plans that can enable the existing cities to metamorphose into smart cities. Innovations will be required to enable Africa to develop a smart city concept that will be compatible with the basic patterns of livelihood because the essence of the smart city evolution is to make life better for people to co-exist, to be productive and to enjoy standard infrastructural facilities. This research paper enumerates the multifaceted adaptive factors that have the potentials of making the adoption of smartcity concept in Africa seamless. It also proffers solutions to potential bottlenecks capable of undermining the execution of the smart city concept in Africa.

Keywords: smartcity compactibility innovation Africa government evolution, Africa as global village member, evolution in Africa, ways to make Africa adopt smartcity, localizing smartcity concept in Africa, bottleneck to smartcity developmet in Africa

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Somayeh Abedian, Pirhossein Kolivand, Hamid Reza Lornejad, Amin Karampour, Ebrahim Keshavarz Safari

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For emergency and relief service providers such as pre-hospital emergencies, quick arrival at the scene of an accident or any EMS mission is one of the most important requirements of effective service delivery. Response time (the interval between the time of the call and the time of arrival on scene) is a critical factor in determining the quality of pre-hospital Emergency Medical Services (EMS). This is especially important for heart attack, stroke, or accident patients. Location-based e-services can be broadly defined as any service that provides information pertinent to the current location of an active mobile handset or precise address of landline phone call at a specific time window, regardless of the underlying delivery technology used to convey the information. According to research, one of the effective methods of meeting this goal is determining the location of the caller via the cooperation of landline and mobile phone operators in the country. The follow-up of the Communications Regulatory Authority (CRA) organization has resulted in the receipt of two separate secured electronic web services. Thus, to ensure human privacy, a secure technical architecture was required for launching the services in the pre-hospital EMS information management system. In addition, to quicken medics’ arrival at the patient's bedside, rescue vehicles should make use of an intelligent transportation system to estimate road traffic using a GPS-based mobile navigation system independent of the Internet. This paper seeks to illustrate the architecture of the practical national model used by the Iranian EMS organization.

Keywords: response time, geographic location inquiry service (GLIS), location-based service (LBS), emergency medical services information system (EMSIS)

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Authors: M. Lehtihet, S. Rosado, C. Pradère, J. Leng

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Poly(3,4-ethylene dioxythiophene) polystyrene sulfonate (PEDOT: PSS), is a polymer mixture well-known for its semiconducting properties and is widely used in the coating industry for its visible transparency and high electronic conductivity (up to 4600 S/cm) as a transparent non-metallic electrode and in organic light-emitting diodes (OLED). It also possesses strong absorption properties in the Near Infra-Red (NIR) range (λ ranging between 900 nm to 2.5 µm). In the present work, we take advantage of this absorption to explore its potential use as a transparent light-to-heat converter. PEDOT: PSS aqueous dispersions are deposited onto a glass substrate using a blade-coating technique in order to produce uniform coatings with controlled thicknesses ranging in ≈ 400 nm to 2 µm. Blade-coating technique allows us good control of the deposit thickness and uniformity by the tuning of several experimental conditions (blade velocity, evaporation rate, temperature, etc…). This liquid coating technique is a well-known, non-expensive technique to realize thin film coatings on various substrates. For coatings on glass substrates destined to solar insulation applications, the ideal coating would be made of a material able to transmit all the visible range while reflecting the NIR range perfectly, but materials possessing similar properties still have unsatisfactory opacity in the visible too (for example, titanium dioxide nanoparticles). NIR absorbing thin films is a more realistic alternative for such an application. Under solar illumination, PEDOT: PSS thin films heat up due to absorption of NIR light and thus act as planar heaters while maintaining good transparency in the visible range. Whereas they screen some NIR radiation, they also generate heat which is then conducted into the substrate that re-emits this energy by thermal emission in every direction. In order to quantify the heating power of these coatings, a sample (coating on glass) is placed in a black enclosure and illuminated with a solar simulator, a lamp emitting a calibrated radiation very similar to the solar spectrum. The temperature of the rear face of the substrate is measured in real-time using thermocouples and a black-painted Peltier sensor measures the total entering flux (sum of transmitted and re-emitted fluxes). The heating power density of the thin films is estimated from a model of the thin film/glass substrate describing the system, and we estimate the Solar Heat Gain Coefficient (SHGC) to quantify the light-to-heat conversion efficiency of such systems. Eventually, the effect of additives such as dimethyl sulfoxide (DMSO) or optical scatterers (particles) on the performances are also studied, as the first one can alter the IR absorption properties of PEDOT: PSS drastically and the second one can increase the apparent optical path of light within the thin film material.

Keywords: PEDOT: PSS, blade-coating, heat, thin-film, Solar spectrum

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

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

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Authors: Dechao Meng, Yongqi Dong, Qiyuan Feng, Zhangzhang Cui, Xiang Hu, Haoliang Huang, Genhao Liang, Huanhua Wang, Hua Zhou, Hawoong Hong, Jinghua Guo, Qingyou Lu, Xiaofang Zhai, Yalin Lu

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Great efforts have been taken to reveal the intrinsic origins of emerging ferromagnetism (FM) in strained LaCoO₃ (LCO) films. However, some macro magnetic performances of LCO are still not well understood and even controversial, such as magnetic anisotropy. Determining and understanding magnetic anisotropy might help to find the true causes of FM in turn. Perpendicular magnetic anisotropy (PMA) was the first time to be directly observed in high-quality LCO films with different thickness. The in-plane (IP) and out of plane (OOP) remnant magnetic moment ratio of 30 unit cell (u.c.) films is as large as 20. The easy axis lays in the OOP direction with an IP/OOP coercive field ratio of 10. What's more, the PMA could be simply tuned by changing the thickness. With the thickness increases, the IP/OOP magnetic moment ratio remarkably decrease with magnetic easy axis changing from OOP to IP. Such a huge and tunable PMA performance exhibit strong potentials in fundamental researches or applications. What causes PMA is the first concern. More OOP orbitals occupation may be one of the micro reasons of PMA. A cluster-like magnetic domain pattern was found in 30 u.c. with no obvious color contrasts, similar to that of LaAlO₃/SrTiO₃ films. And the nanosize domains could not be totally switched even at a large OOP magnetic field of 23 T. It indicates strong IP characters or none OOP magnetism of some clusters. The IP magnetic domains might influence the magnetic performance and help to form PMA. Meanwhile some possible nonmagnetic clusters might be the reason why the measured moments of LCO films are smaller than the calculated values 2 μB/Co, one of the biggest confusions in LCO films.What tunes PMA seems much more interesting. Totally different magnetic domain patterns were found in 180 u.c. films with cluster magnetic domains surrounded by < 110 > cross-hatch lines. These lines were regarded as structure domain walls (DWs) determined by 3D reciprocal space mapping (RSM). Two groups of in-plane features with fourfold symmetry were observed near the film diffraction peaks in (002) 3D-RSM. One is along < 110 > directions with a larger intensity, which is well match the lines on the surfaces. The other is much weaker and along < 100 > directions, which is from the normal lattice titling of films deposited on cubic substrates. The < 110 > domain features obtained from (103) and (113) 3D-RSMs exhibit similar evolution of the DWs percentages and magnetic behavior. Structure domains and domain walls are believed to tune PMA performances by transform more IP magnetic moments to OOP. Last but not the least, thick films with lots of structure domains exhibit different electrical transport behaviors. A metal-to-insulator transition (MIT) and an angular dependent negative magnetic resistivity were observed near 150 K, higher than FM transition temperature but similar to that of spin-orbital coupling related 1/4 order diffraction peaks.

Keywords: structure domain, magnetic anisotropy, magnetic domain, domain wall, 3D-RSM, strain

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Authors: Bingying Liu, Dandong Ge, Xinlan Zhang, Haoyang Liang

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Over the past two years, a new generation of No-Pile Bike sharing, represented by the Ofo, Mobike and HelloBike, has sprung up in various cities in China, and spread rapidly in countries such as Britain, Japan, the United States and Singapore. As a new green public transportation mode, bike sharing can bring a series of benefits to urban space. At first, this paper analyzes the specific impact of bike sharing on urban space in China. Based on the market research and data analyzing, it is found that bike sharing can improve the quality of urban space in three aspects: expanding the radius of public transportation service, filling service blind spots, alleviating urban traffic congestion, and enhancing the vitality of urban space. On the other hand, due to the immature market and the imperfect system, bike sharing has gradually revealed some difficulties, such as parking chaos, malicious damage, safety problems, imbalance between supply and demand, and so on. Then the paper investigates the characteristics of shared bikes, business model, operating mechanism on Chinese market currently. Finally, in order to make bike sharing serve urban construction better, this paper puts forward some specific countermeasures from four aspects. In terms of market operations, it is necessary to establish a public-private partnership model and set up a unified bike-sharing integrated management platform. From technical methods level, the paper proposes to develop an intelligent parking system for regulating parking. From policy formulation level, establishing a bike-sharing assessment mechanism would strengthen supervision. As to urban planning, sharing data and redesigning slow roadway is beneficial for transportation and spatial planning.

Keywords: bike sharing, impact analysis, improvement strategy, urban space

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Authors: Susan Diamond

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Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

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Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

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Authors: Saheli Mitra, Ramesh Karri, Praveen K. Mylapalli, Arka. B. Dey, Gourav Bhattacharya, Gouriprasanna Roy, Syed M. Kamil, Surajit Dhara, Sunil K. Sinha, Sajal K. Ghosh

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The most well-known structures of lyotropic liquid crystalline systems are the two dimensional hexagonal phase of cylindrical micelles with a positive interfacial curvature and the lamellar phase of flat bilayers with zero interfacial curvature. In aqueous solution of surfactants, the concentration dependent phase transitions have been investigated extensively. However, instead of changing the surfactant concentrations, the local curvature of an aggregate can be altered by tuning the electrostatic interactions among the constituent molecules. Intermediate phases with non-uniform interfacial curvature are still unexplored steps to understand the route of phase transition from hexagonal to lamellar. Understanding such structural evolution in lyotropic liquid crystalline systems is important as it decides the complex rheological behavior of the system, which is one of the main interests of the soft matter industry. Sodium dodecyl sulfate (SDS) is an anionic surfactant and can be considered as a unique system to tune the electrostatics by cationic additives. In present study, imidazolium-based ionic liquids (ILs) with different number of carbon atoms in their single hydrocarbon chain were used as the additive in the aqueous solution of SDS. At a fixed concentration of total non-aqueous components (SDS and IL), the molar ratio of these components was changed, which effectively altered the electrostatic interactions between the SDS molecules. As a result, the local curvature is observed to modify, and correspondingly, the structure of the hexagonal liquid crystalline phases are transformed into other phases. Polarizing optical microscopy of SDS and imidazole-based-IL systems have exhibited different textures of the liquid crystalline phases as a function of increasing concentration of the ILs. The small angle synchrotron x-ray diffraction (SAXD) study has indicated the hexagonal phase of direct cylindrical micelles to transform to a rectangular phase at the presence of short (two hydrocarbons) chain IL. However, the hexagonal phase is transformed to a lamellar phase at the presence of long (ten hydrocarbons) chain IL. Interestingly, at the presence of a medium (four hydrocarbons) chain IL, the hexagonal phase is transformed to another hexagonal phase of direct cylindrical micelles through the lamellar phase. To the best of our knowledge, such a phase sequence has not been reported earlier. Even though the small angle x-ray diffraction study has revealed the lattice parameters of these phases to be similar to each other, their rheological behavior has been distinctly different. These rheological studies have shed lights on how these phases differ in their viscoelastic behavior. Finally, the packing parameters, calculated for these phases based on the geometry of the aggregates, have explained the formation of the self-assembled aggregates.

Keywords: lyotropic liquid crystals, polarizing optical microscopy, rheology, surfactants, small angle x-ray diffraction

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Authors: Mohamad Khairi Haji Othman, Mohd Zailani Mohd Yusoff, Rozalina Khalid

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The inculcation of noble values in teaching and learning is very important, especially to build students with good characters and values. Therefore, the purpose of this research is to identify the challenges of implementing the inculcation of noble values in teaching in primary schools. This study was conducted at four North Zone Peninsular Malaysia schools. This study was used a qualitative approach in the form of case studies. The qualitative approach aims at gaining meaning and a deep understanding of the phenomenon studied from the perspectives of the study participants and not intended to make the generalization. The sample in this study consists of eight teachers who teach in four types of schools that have been chosen purposively. The method of data collection is through semi-structured interviews used in this study. The comparative method is continuously used in this study to analyze the primary data collected. The study found that the main challenges faced by teachers were students' problems and class control so that teachers felt difficult to the inculcation of noble values in teaching. In addition, the language challenge is difficult for students to understand. Similarly, peers are also challenging because students are more easily influenced by friends rather than listening to teachers' instructions. The last challenge was the influence of technology and mass media electronic more widespread. The findings suggest that teachers need to innovate in order to assist the school in inculcating religious and moral education towards the students. The school through guidance and counseling teachers can also plan some activities that are appropriate to the student's present condition. Through this study, teachers and the school should work together to develop the values of students in line with the needs of the National Education Philosophy that wishes to produce intelligent, emotional, spiritual, intellectual and social human capital.

Keywords: challenges, implementation, inculcation, noble values

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Authors: Huixuan Xu

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The present study was set out to investigate the extent to which and how service-learning fostered a sample of 138 Hong Kong undergraduates’ interpersonal competence and cooperative orientation development. Interpersonal competence is presented when an individual shows empathy with others, provides intelligent advice to others and has practical judgment. Cooperative orientation reflects individuals’ willingness to work with others to achieve common goals. A quality service-learning programme may exhibit the features of provision of meaningful service, close link to curriculum, continuous reflection, youth voice, and diversity. Mixed methods were employed in the present study. Pre-posttest survey was administered to capture individual undergraduates’ development of interpersonal competence and cooperative orientation over a period of four months. The respondents’ evaluation of service-learning elements was administered in the post-test survey. Focus groups were conducted after the end of the service-learning to further explore how the certain service-learning elements promoted individual undergraduates’ development of interpersonal competence and cooperative orientation. Three main findings were reported from the study. (1) The scores of interpersonal competence increased significantly from the pretest to the posttest, while the change of cooperative orientation was not significant. (2) Cooperative orientation and interpersonal competence were correlated positively with the overall course quality respectively, which suggested that the more a service-learning course complied with quality practice, the students became more competent in interpersonal competence and cooperative orientation. (3) The following service-learning elements showed higher impacts: (a) direct contact with service recipients, which engaged students in practicing interpersonal skills; (b) individual participants’ being exposed to a situation that required communication and dialogue with people from diverse backgrounds with different views; (c) experiencing interpersonal conflicts among team members and having the conflicts solved; (d) students’ taking a leading role in a project-based service. The present study provides compelling evidence about what elements in a service-learning program may foster undergraduates’ development of cooperative orientation and interpersonal competence. Implications for the design of service-learning programmes are provided.

Keywords: undergraduates, interpersonal competence, cooperation orientation, service-learning

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Nutthan Inkhong, Sutthiphong Ruangchante

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Chiang Rai province is located at the northern border of Thailand. Most of the geography there is the northern continental highlands, and the population has many types of inhabitants, including Thai people, immigrants and ethnic groups such as Akha, Lahu, Lisu, Yao, etc. Most of these ethnic groups migrated from neighbouring countries such as Myanmar, Laos, China, etc. and settled in the mountains. Each ethnic group has their unique traditions, culture, and ways of life, including the musical culture that the ancestors of each ethnic group brought with them. In the present, the Akha have the largest population in the region and still live together in numerous villages in many districts. Thus, Akha musical culture still appears in the community traditions and cultural events of Chiang Rai province regularly. This article presents the situations of Akha musical culture in the present and the predictions for the future. The study method involves the analysis of music information and the related social contexts, which were collected from the fieldwork of ethnomusicological methodology by in-depth interviews, observations, audio and visual recordings, and related documents. The results found that the important persons who are related with Akha musical culture include (1) a musical instrument maker (lives in Mae Chan district) who produces various Akha musical instruments, including gourd mouth organs, Akha drums, two-way flutes, three-hole flutes, Jew’s harps (the sound of teenage love), buffalo horns (the sound symbol of hunting) and bird call instruments (the imitation of bird sounds), (2) a folk philosopher (lives in Mae Pha Luang district) who can teach music to the new generation of Akha people as well as lecture and demonstrate music to academics and tourists, and (3) a community leader (lives in Mae Chan district) who conserves Akha performances, singing and music through various activities of the students in an informal school. Because of the changes to the social contexts and ways of life of the Akha people, such as the educational system, religion, social media, etc., including the popularity of both Thai and international popular music among the new generation of Akha people, changes to and the fading away of Akha musical culture in the future may likely occur. Therefore, the conservation and inheritance of Akha music is an issue that should be resolved quickly. This primary study leads to the next step of the ethnomusicological work and plays a part in preventing or reducing the problems impacting Akha musical culture survival by the recording of Akha music in all of its dimensions, such as producing musical instruments, playing musical instruments, analysis of tuning systems, recording Akha music as musical notation using symbols, researching related social contexts, etc. and the transcription of this information to create lessons that can be returned to the Akha community.

Keywords: Akha music, Chiang Rai, ethnic music in Thailand, ethnomusicology

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Authors: Laheeb M. Ibrahim, Ibrahim A. Salih

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Biometrics is a technology that has been widely used in many official and commercial identification applications. The increased concerns in security during recent years (especially during the last decades) have essentially resulted in more attention being given to biometric-based verification techniques. Here, a novel fusion approach of palmprint, dental traits has been suggested. These traits which are authentication techniques have been employed in a range of biometric applications that can identify any postmortem PM person and antemortem AM. Besides improving the accuracy, the fusion of biometrics has several advantages such as increasing, deterring spoofing activities and reducing enrolment failure. In this paper, a first unimodel biometric system has been made by using (palmprint and dental) traits, for each one classification applying an artificial neural network and a hybrid technique that combines swarm intelligence and neural network together, then attempt has been made to combine palmprint and dental biometrics. Principally, the fusion of palmprint and dental biometrics and their potential application has been explored as biometric identifiers. To address this issue, investigations have been carried out about the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. Also the results of the multimodal approach have been compared with each one of these two traits authentication approaches. This paper studies the features and decision fusion levels in multimodal biometrics. To determine the accuracy of GAR to parallel system decision-fusion including (AND, OR, Majority fating) has been used. The backpropagation method has been used for classification and has come out with result (92%, 99%, 97%) respectively for GAR, while the GAR) for this algorithm using hybrid technique for classification (95%, 99%, 98%) respectively. To determine the accuracy of the multibiometric system for feature level fusion has been used, while the same preceding methods have been used for classification. The results have been (98%, 99%) respectively while to determine the GAR of feature level different methods have been used and have come out with (98%).

Keywords: back propagation neural network BP ANN, multibiometric system, parallel system decision-fusion, practical swarm intelligent PSO

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Authors: P. Dubcek, B. Pivac, J. Dasovic, V. Janicki, S. Bernstorff

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When grown only to nanometric sizes, metallic particles (e.g. Ag, Au and Cu) exhibit specific optical properties caused by the presence of plasmon band. The plasmon band represents collective oscillation of the conduction electrons, and causes a narrow band absorption of light in the visible range. When the nanoparticles are embedded in a dielectric, they also cause modifications of dielectrics optical properties. This can be fine-tuned by tuning the particle size. We investigated Cu nanoparticle growth with and without surrounding dielectric (SiO2 capping layer). The morphology and crystallinity were investigated by GISAXS and GIWAXS, respectively. Samples were produced by high vacuum thermal evaporation of Cu onto monocrystalline silicon substrate held at room temperature, 100°C or 180°C. One series was in situ capped by 10nm SiO2 layer. Additionally, samples were annealed at different temperatures up to 550°C, also in high vacuum. The room temperature deposited samples annealed at lower temperatures exhibit continuous film structure: strong oscillations in the GISAXS intensity are present especially in the capped samples. At higher temperatures enhanced surface dewetting and Cu nanoparticles (nanoislands) formation partially destroy the flatness of the interface. Therefore the particle type of scattering is enhanced, while the film fringes are depleted. However, capping layer hinders particle formation, and continuous film structure is preserved up to higher annealing temperatures (visible as strong and persistent fringes in GISAXS), compared to the non- capped samples. According to GISAXS, lateral particle sizes are reduced at higher temperatures, while particle height is increasing. This is ascribed to close packing of the formed particles at lower temperatures, and GISAXS deduced sizes are partially the result of the particle agglomerate dimensions. Lateral maxima in GISAXS are an indication of good positional correlation, and the particle to particle distance is increased as the particles grow with temperature elevation. This coordination is much stronger in the capped and lower temperature deposited samples. The dewetting is much more vigorous in the non-capped sample, and since nanoparticles are formed in a range of sizes, correlation is receding both with deposition and annealing temperature. Surface topology was checked by atomic force microscopy (AFM). Capped sample's surfaces were smoother and lateral size of the surface features were larger compared to the non-capped samples. Altogether, AFM results suggest somewhat larger particles and wider size distribution, and this can be attributed to the difference in probe size. Finally, the plasmonic effect was monitored by UV-Vis reflectance spectroscopy, and relative weak plasmonic effect could be explained by uncomplete dewetting or partial interconnection of the formed particles.

Keywords: coper, GISAXS, nanoparticles, plasmonics

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Authors: Gihan Thilakarathna, Fernando Ishara, Rathnayake Yasith, Bandara A. M. R. Y.

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E-learning is a crucial component of intelligent education. Even in the midst of a pandemic, E-learning is becoming increasingly important in the educational system. Several e-learning programs are accessible for students. Here, we decided to create an e-learning framework for children. We've found a few issues that teachers are having with their online classes. When there are numerous students in an online classroom, how does a teacher recognize a student's focus on academics and below-the-surface behaviors? Some kids are not paying attention in class, and others are napping. The teacher is unable to keep track of each and every student. Key challenge in e-learning is online exams. Because students can cheat easily during online exams. Hence there is need of exam proctoring is occurred. In here we propose an automated online exam cheating detection method using a web camera. The purpose of this project is to present an E-learning platform for math education and include games for kids as an alternative teaching method for math students. The game will be accessible via a web browser. The imagery in the game is drawn in a cartoonish style. This will help students learn math through games. Everything in this day and age is moving towards automation. However, automatic answer evaluation is only available for MCQ-based questions. As a result, the checker has a difficult time evaluating the theory solution. The current system requires more manpower and takes a long time to evaluate responses. It's also possible to mark two identical responses differently and receive two different grades. As a result, this application employs machine learning techniques to provide an automatic evaluation of subjective responses based on the keyword provided to the computer as student input, resulting in a fair distribution of marks. In addition, it will save time and manpower. We used deep learning, machine learning, image processing and natural language technologies to develop these research components.

Keywords: math, education games, e-learning platform, artificial intelligence

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Authors: Colin Schmidt, Adrien Lecossier, Pascal Crubleau, Philippe Blanchard, Simon Richir

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Introduction:The widespread availability of artificial intelligence, exemplified by Generative Pre-trained Transformers (GPT) relying on large language models (LLM), has caused a seismic shift in the realm of knowledge. Everyone now has the capacity to swiftly learn how these models can either serve them well or not. Today, conversational AI like ChatGPT is grounded in neural transformer models, a significant advance in natural language processing facilitated by the emergence of renowned LLMs constructed using neural transformer architecture. Inventiveness of an LLM : OpenAI's GPT-3 stands as a premier LLM, capable of handling a broad spectrum of natural language processing tasks without requiring fine-tuning, reliably producing text that reads as if authored by humans. However, even with an understanding of how LLMs respond to questions asked, there may be lurking behind OpenAI’s seemingly endless responses an inventive model yet to be uncovered. There may be some unforeseen reasoning emerging from the interconnection of neural networks here. Just as a Soviet researcher in the 1940s questioned the existence of Common factors in inventions, enabling an Under standing of how and according to what principles humans create them, it is equally legitimate today to explore whether solutions provided by LLMs to complex problems also share common denominators. Theory of Inventive Problem Solving (TRIZ) : We will revisit some fundamentals of TRIZ and how Genrich ALTSHULLER was inspired by the idea that inventions and innovations are essential means to solve societal problems. It's crucial to note that traditional problem-solving methods often fall short in discovering innovative solutions. The design team is frequently hampered by psychological barriers stemming from confinement within a highly specialized knowledge domain that is difficult to question. We presume ChatGPT Utilizes TRIZ 40. Hence, the objective of this research is to decipher the inventive model of LLMs, particularly that of ChatGPT, through a comparative study. This will enhance the efficiency of sustainable innovation processes and shed light on how the construction of a solution to a complex problem was devised. Description of the Experimental Protocol : To confirm or reject our main hypothesis that is to determine whether ChatGPT uses TRIZ, we will follow a stringent protocol that we will detail, drawing on insights from a panel of two TRIZ experts. Conclusion and Future Directions : In this endeavor, we sought to comprehend how an LLM like GPT addresses complex challenges. Our goal was to analyze the inventive model of responses provided by an LLM, specifically ChatGPT, by comparing it to an existing standard model: TRIZ 40. Of course, problem solving is our main focus in our endeavours.

Keywords: artificial intelligence, Triz, ChatGPT, inventiveness, problem-solving

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Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

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Arrival of artificial intelligence or smart robots in the modern world put them in charge on pericise and at risk. So acting human activities with robots makes criminal or civil responsibilities for their acts or behavior. The practical usage of smart robots has entered them in to a unique situation when naturalization happens and smart robots are identifies as members of society. There would be some legal situation by adopting these new smart citizens. The first situation is about legal responsibility of robots. Recognizing the naturalization of robot involves some basic right , so humans have the rights of employment, property, housing, using energy and other human rights may be employed for robots. So how would be the practice of these rights in the society and if some problems happens with these rights, how would the civil responsibility and punishment? May we consider them as population and count on the social programs? The second episode is about the criminal responsibility of robots in important activity instead of human that is the aim of inventing robots with handling works in AI technology , but the problem arises when some accidents are happened by robots who are in charge of important activities like army, surgery, transporting, judgement and so on. Moreover, recognizing independent identification for robots in the legal world by register ID cards, naturalization and civilian rights makes and prepare the same rights and obligations of human. So, the civil responsibility is not avoidable and if the robot commit a crime it would have criminal responsibility and have to be punished. The basic component of criminal responsibility may changes in so situation. For example, if designation for criminal responsibility bounds to human by sane, maturity, voluntariness, it would be for robots by being intelligent, good programming, not being hacked and so on. So it is irrational to punish robots by prisoning , execution and other human punishments for body. We may determine to make digital punishments like changing or repairing programs, exchanging some parts of its body or wreck it down completely. Finally the responsibility of the smart robot creators, programmers, the boss in chief, the organization who employed robot, the government which permitted to use robot in important bases and activities , will be analyzing and investigating in their article.

Keywords: robot, artificial intelligence, personality, responsibility

Procedia PDF Downloads 147

Authors: Getaneh Berie Tarekegn

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

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

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Authors: Federico Capra, Emanuele Martelli, Matteo Gazzani, Marco Mazzotti, Maurizio Notaro

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Natural gas is a major energy source in the current global economy, contributing to roughly 21% of the total primary energy consumption. Production of natural gas starting from renewable energy sources is key to limit the related CO2 emissions, especially for those sectors that heavily rely on natural gas use. In this context, biomethane produced via biogas upgrading represents a good candidate for partial substitution of fossil natural gas. The upgrading process of biogas to biomethane consists in (i) the removal of pollutants and impurities (e.g. H2S, siloxanes, ammonia, water), and (ii) the separation of carbon dioxide from methane. Focusing on the CO2 removal process, several technologies can be considered: chemical or physical absorption with solvents (e.g. water, amines), membranes, adsorption-based systems (PSA). However, none emerged as the leading technology, because of (i) the heterogeneity in plant size, ii) the heterogeneity in biogas composition, which is strongly related to the feedstock type (animal manure, sewage treatment, landfill products), (iii) the case-sensitive optimal tradeoff between purity and recovery of biomethane, and iv) the destination of the produced biomethane (grid injection, CHP applications, transportation sector). With this contribution, we explore the use of a technology for biogas upgrading and we compare the resulting performance with benchmark technologies. The proposed technology makes use of a chemical sorbent, which is engineered by RSE and consists of Di-Ethanol-Amine deposited on a solid support made of γ-Alumina, to chemically adsorb the CO2 contained in the gas. The material is packed into fixed beds that cyclically undergo adsorption and regeneration steps. CO2 is adsorbed at low temperature and ambient pressure (or slightly above) while the regeneration is carried out by pulling vacuum and increasing the temperature of the bed (vacuum-temperature swing adsorption - VTSA). Dynamic adsorption tests were performed by RSE and were used to tune the mathematical model of the process, including material and transport parameters (i.e. Langmuir isotherms data and heat and mass transport). Based on this set of data, an optimal VTSA cycle was designed. The results enabled a better understanding of the interplay between material and cycle tuning. As exemplary application, the upgrading of biogas for grid injection, produced by an anaerobic digester (60-70% CO2, 30-40% CH4), for an equivalent size of 1 MWel was selected. A plant configuration is proposed to maximize heat recovery and minimize the energy consumption of the process. The resulting performances are very promising compared to benchmark solutions, which make the VTSA configuration a valuable alternative for biomethane production starting from biogas.

Keywords: biogas upgrading, biogas upgrading energetic cost, CO2 adsorption, VTSA process modelling

Procedia PDF Downloads 277