Search results for: automatic processing

Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Jagmeet S. Kanwal, Julia F. Langley

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Music can heal the body, but a mechanistic understanding of this phenomenon is lacking. This study explores the effects of music presentation on neurologic and physiologic responses leading to metabolic changes in the human body. The mind and body co-exist in a corporeal entity and within this framework, sickness ensues when the mind-body balance goes awry. It is further hypothesized that music has the capacity to directly reset this balance. Two lines of inquiry taken together can provide a mechanistic understanding of this phenomenon 1) Empirical evidence for a sound-sensitive pressure sensor system in the body, and 2) The notion of a “healing center” within the brain that is activated by specific patterns of sounds. From an acoustics perspective, music is spatially distributed as pressure waves ranging from a few cm to several meters in wavelength. These waves interact and propagate in three-dimensions in unique ways, depending on the wavelength. Furthermore, music creates dynamically changing wave-fronts. Frequencies between 200 Hz and 1 kHz generate wavelengths that range from 5'6" to 1 foot. These dimensions are in the range of the body size of most people making it plausible that these pressure waves can geometrically interact with the body surface and create distinct patterns of pressure stimulation across the skin surface. For humans, short wavelength, high frequency (> 200 Hz) sounds are best received via cochlear receptors. For low frequency (< 200 Hz), long wavelength sound vibrations, however, the whole body may act as an ideal receiver. A vast array of highly sensitive pressure receptors (Pacinian corpuscles) is present just beneath the skin surface, as well as in the tendons, bones, several organs in the abdomen, and the sexual organs. Per the available empirical evidence, these receptors contribute to music perception by allowing the whole body to function as a sound receiver, and knowledge of how they function is essential to fully understanding the therapeutic effect of music. Neuroscientific studies have established that music stimulates the limbic system that can trigger states of anxiety, arousal, fear, and other emotions. These emotional states of brain activity play a crucial role in filtering top-down feedback from thoughts and bottom-up sensory inputs to the autonomic system, which automatically regulates bodily functions. Music likely exerts its pleasurable and healing effects by enhancing functional and effective connectivity and feedback mechanisms between brain regions that mediate reward, autonomic, and cognitive processing. Stimulation of pressure receptors under the skin by low-frequency music-induced sensations can activate multiple centers in the brain, including the amygdala, the cingulate cortex, and nucleus accumbens. Melodies in music in the low (< 600 Hz) frequency range may augment auditory inputs after convergence of the pressure-sensitive inputs from the vagus nerve onto emotive processing regions within the limbic system. The integration of music-generated auditory and somato-visceral inputs may lead to a synergistic input to the brain that promotes healing. Thus, music can literally heal humans through “touch” as it energizes the brain’s autonomic system for restoring homeostasis.

Keywords: acoustics, brain, music healing, pressure receptors

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Authors: Widhi Hanantyo Suryadinata

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Efforts undertaken by Indonesia and China to shift the strategies and security of renewable energy on a global stage involve approaches through policy construction related to rare minerals processing or value-adding in Indonesia and manufacturing policies through the New Energy Vehicles (NEVs) policy in China. Both policies encompass several practical regulations and policies that can be utilized for the implementation of Indonesia and China's grand efforts and ideas. Policy development in Indonesia and China can be analyzed using a comparative analysis method, as well as employing a pyramid illustration to identify policy construction phases based on the real conditions of the domestic market and implemented policies. This approach also helps to identify the potential integration of policies needed to enhance the policy development phase of a country within the pyramid. It also emphasizes the significance of integration policy to redefine renewable energy strategy and security on the global stage.

Keywords: global renewable energy security, global energy security, policy development, comparative analysis, shifting of global energy security, Indonesia, China

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Authors: Sarah Kinyanjui, Mahnaaz Mohamed

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The child justice system in Kenya is organically departing from a managerial and retributive model to one that espouses restorative justice. Notably, the Children Act 2001, and the most recent, Children Act 2022, signalled an aspiration to facilitate meaningful interventions as opposed to ‘processing’ children through the justice system. In this vein, the Children Act 2022 formally recognises diversion and provides modalities for its implementation. This paper interrogates the diversion promise and reflects on the implementation of diversion as envisaged by the 2022 Act. Using restorative justice, labelling and differential association theories as well as the value of care lenses, the paper discusses diversion as a meaningful response to child offending. It further argues that while diversion presents a strong platform for the realisation of the restorative and rehabilitative ideals, in the absence of a well-planned, coordinated, and resourced framework, diversion may remain a mere alternative ‘conveyor belt’. Strategic multi-agency planning, capacity building and cooperation are highlighted as essential minimums for the realisation of the goals of diversion.

Keywords: diversion for child offenders, restorative justice, responsive criminal justice system, children act 2022 kenya

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Authors: Xinyuan Liu, Xiaojun Jing, Yuan He, Junsheng Mu

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Aspect-based Sentiment Analysis (ABSA) has attracted much attention due to its capacity to determine the sentiment polarity of the certain aspect in a sentence. In previous works, great significance of the interaction between aspect and sentence has been exhibited in ABSA. In consequence, a Multi-Level Attentional Networks (MLAN) is proposed. MLAN consists of four parts: Embedding Layer, Encoding Layer, Multi-Level Attentional (MLA) Layers and Final Prediction Layer. Among these parts, MLA Layers including Aspect Level Attentional (ALA) Layer and Interactive Attentional (ILA) Layer is the innovation of MLAN, whose function is to focus on the important information and obtain multiple levels’ attentional weighted representation of aspect and sentence. In the experiments, MLAN is compared with classical TD-LSTM, MemNet, RAM, ATAE-LSTM, IAN, AOA, LCR-Rot and AEN-GloVe on SemEval 2014 Dataset. The experimental results show that MLAN outperforms those state-of-the-art models greatly. And in case study, the works of ALA Layer and ILA Layer have been proven to be effective and interpretable.

Keywords: deep learning, aspect-based sentiment analysis, attention, natural language processing

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Authors: Sarthak Deshpande, Akshay Patil, Pradip Pandhare, Nikhil Wankhede, Rushali Deshmukh

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Among the highly significant duties for any language most effective is the sentiment analysis, which is also a key area of NLP, that recently made impressive strides. There are several models and datasets available for those tasks in popular and commonly used languages like English, Russian, and Spanish. While sentiment analysis research is performed extensively, however it is lagging behind for the regional languages having few resources such as Hindi, Marathi. Marathi is one of the languages that included in the Indian Constitution’s 8th schedule and is the third most widely spoken language in the country and primarily spoken in the Deccan region, which encompasses Maharashtra and Goa. There isn’t sufficient study on sentiment analysis methods based on Marathi text due to lack of available resources, information. Therefore, this project proposes the use of different ML/NLP models for the analysis of Marathi data from the comments below YouTube content, tweets or Instagram posts. We aim to achieve a short and precise analysis and summary of the related data using our dataset (Dates, names, root words) and lexicons to locate exact information.

Keywords: multilingual sentiment analysis, Marathi, natural language processing, text summarization, lexicon-based approaches

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Authors: Rodríguez Sánchez Carla, Sancho-Esper Franco, Guillen-Davo Marina

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Tourism is one of the most important sectors worldwide since it is an important economic engine for today's society. It is also one of the sectors that most negatively affect the environment in terms of CO₂ emissions due to this expansion. In light of this, airlines are developing Voluntary Carbon Offset (VCO). There is important evidence focused on analyzing the features of these VCO programs and their efficacy in reducing CO₂ emissions, and findings are mixed without a clear consensus. Different research approaches have centered on analyzing factors and consequences of VCO programs, such as economic modelling based on panel data, survey research based on traveler responses or experimental research analyzing customer decisions in a simulated context. This study belongs to the latter group because it tries to understand how different characteristics of an online ticket purchase website affect the willingness of a traveler to choose a sustainable one. The proposed behavioral model is based on several theories, such as the nudge theory, the dual processing ELM and the cognitive dissonance theory. This randomized experiment aims at overcoming previous studies based on self-reported measures that mainly study sustainable behavioral intention rather than actual decision-making. It also complements traditional self-reported independent variables by gathering objective information from an eye-tracking device. This experiment analyzes the influence of two characteristics of the online purchase website: i) the type of information regarding flight CO₂ emissions (quantitative vs. qualitative) and the comparison framework related to the sustainable purchase decision (negative: alternative with more emissions than the average flight of the route vs. positive: alternative with less emissions than the average flight of the route), therefore it is a 2x2 experiment with four alternative scenarios. A pretest was run before the actual experiment to refine the experiment features and to check the manipulations. Afterward, a different sample of students answered the pre-test questionnaire aimed at recruiting the cases and measuring several pre-stimulus measures. One week later, students came to the neurolab at the University setting to be part of the experiment, made their decision regarding online purchases and answered the post-test survey. A final sample of 21 students was gathered. The committee of ethics of the institution approved the experiment. The results show that qualitative information generates more sustainable decisions (less contaminant alternative) than quantitative information. Moreover, evidence shows that subjects are more willing to choose the sustainable decision to be more ecological (comparison of the average with the less contaminant alternative) rather than to be less contaminant (comparison of the average with the more contaminant alternative). There are also interesting differences in the information processing variables from the eye tracker. Both the total time to make the choice and the specific times by area of interest (AOI) differ depending on the assigned scenario. These results allow for a better understanding of the factors that condition the decision of a traveler to be part of a VCO program and provide useful information for airline managers to promote these programs to reduce environmental impact.

Keywords: voluntary carbon offset, airline, online purchase, carbon emission, sustainability, randomized experiment

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Authors: Ram Manohar Singh, Meenakshi Gupta

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Various terms such as ‘learning style’, ‘cognitive style’, ‘conceptual style’, ‘thinking style’, ‘intellectual style’ are used in literature to refer to an individual’s characteristic and consistent approach to organizing and processing information. However, style concepts are criticized for mutually overlapping definitions and confusing classification. This confusion should be addressed at the conceptual as well as empirical level. This paper is an attempt to bridge this gap in literature by proposing a new concept: expansive-restrictive intellectual style based on phenomenological analysis of an auto-ethnography and interview of 26 information technology (IT) professionals working in knowledge intensive organizations (KIOs) in India. Expansive style is an individual’s preference to expand his/her horizon of knowledge and understanding by gaining real meaning and structure of his/her work. On the contrary restrictive style is characterized by an individual’s preference to take minimalist approach at work reflected in executing a job efficiently without an attempt to understand the real meaning and structure of the work. The analysis suggests that expansive-restrictive style has three dimensions: (1) field dependence-independence (2) cognitive involvement and (3) epistemological beliefs.

Keywords: expansive, knowledge workers, restrictive, style

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Authors: S. P. Jordan, G. Christian, S. P. Jeffs

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Modern composite materials are increasingly being chosen in replacement of heavier metallic material systems within many engineering fields including aerospace and automotive industries. The increasing push towards satisfying environmental targets are fuelling new material technologies and manufacturing processes. This paper will introduce materials and manufacturing processes using metal matrix composites along with manufacturing processes optimized at Alvant Ltd., based in Basingstoke in the UK which offers modern, cost effective, selectively reinforced composites for light-weighting applications within engineering. An overview and introduction into modern optimized manufacturing methods capable of producing viable replacements for heavier metallic and lower temperature capable polymer composites are offered. A review of the capabilities and future applications of this viable material is discussed to highlight the potential involved in further optimization of old manufacturing techniques, to fully realize the potential to lightweight material using cost-effective methods.

Keywords: aluminium matrix composites, light-weighting, hybrid squeeze casting, strategically placed reinforcements

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Authors: Asifa Fitriani, Galih Prakoso

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This study aims to improve the design layout of a Micro, Small and Medium Enterprises (SMEs) to minimize material handling and redesigning the layout of production facilities based on the safety and health and standards of food production facilities. Problems layout in the one of chip making industry mushrooms in Indonesia is cross movement between work stations, work accidents, and the standard of facilities that do not conform with the standards of the food industry. Improvement layout design using CORELAP and 5S method to give recommendation and implementation of occupational health and safety standards of food production facilities. From the analysis, improved layout using CORELAP provide a smaller displacement distance is 155.84 meters from the initial displacement distance of 335.9 meters, and providing a shorter processing time than the original 112.726 seconds to 102.831 seconds. 5S method also has recommended the completion of occupational health and safety issues as well as the standard means of food production by changing the working environment better.

Keywords: Layout Design, Corelap, 5S

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Authors: Mengfan Li, Filip Van Bockstaele, Wenyong Lou, Frank Devlighere

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The development of probiotics-encapsulated emulsions that maintain the viability of probiotics during processing, storage and human gastrointestinal (GI) tract environment receives great scientific and commercial interest. In this study, typical W/O and O/W emulsions with and without oil gelation were used to encapsulate L. plantarum. The effects of emulsion types on the viability of L. plantarum during storage and GI tract were investigated. Besides, the position of L. plantarum in emulsion system and its number of viable cells when threating by adverse environment was correlated in order to figure out which type of emulsion is more suitable as food carrier for probiotics encapsulation and protection. As a result, probiotics tend to migrate from oil to water phase due to the natural hydrophilicity; however, it’s harmful for cells viability when surrounding by water for a long time. Oil gelation in emulsions is one of the promising strategies for inhibiting the cells mobility and decreasing the contact with adverse factors (e.g., water, exogenous enzymes and gastric acid), thus enhancing the number of viable cells that enough to exert its beneficial effects in host.

Keywords: emulsion, gelation, encapsulation, probiotics

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Authors: M. D. Stelescu, E. Manaila, G. Craciun, D. Ighigeanu

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The goal of the paper is to present the results regarding the influence of the irradiation dose and amount of multifunctional monomer trimethylol-propane trimethacrylate (TMPT) on ethylene-propylene-diene terpolymer rubber (EPDM) mixtures irradiated in electron beam. Blends, molded on an electrically heated laboratory roller mill and compressed in an electrically heated hydraulic press, were irradiated using the ALID 7 of 5.5 MeV linear accelerator in the dose range of 22.6 kGy to 56.5 kGy in atmospheric conditions and at room temperature of 25 °C. The share of cross-linking and degradation reactions was evaluated by means of sol-gel analysis, cross-linking density measurements, FTIR studies and Charlesby-Pinner parameter (p₀/q₀) calculations. The blends containing different concentrations of TMPT (3 phr and 9 phr) and irradiated with doses in the mentioned range have present the increasing of gel content and cross-linking density. Modified and new bands in FTIR spectra have appeared, because of both cross-linking and chain scission reactions.

Keywords: electron beam irradiation, EPDM rubber, crosslinking density, gel fraction

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Authors: Natalia Muratova, Dmitry Klimov, Rafail Isemin, Sergey Kuzmin, Aleksandr Mikhalev, Oleg Milovanov

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We investigated the processing of poultry litter into biochar using dry torrefaction methods (DT) in a fixed and fluidized bed of quartz sand blown with nitrogen, as well as wet torrefaction (WT) in a fluidized bed in a medium of water steam at a temperature of 300 °C. Torrefaction technology affects the duration of the heat treatment process and the characteristics of the biochar: the process of separating CO₂, CO, H₂ and CH₄ from a portion of fresh poultry litter during torrefaction in a fixed bed is completed after 2400 seconds, but in a fluidized bed — after 480 seconds. During WT in a fluidized bed of quartz sand, this process ends in 840 seconds after loading a portion of fresh litter, but in a fluidized bed of litter particles previously subjected to torrefaction, the process ends in 350 - 450 seconds. In terms of the ratio between (H/C) and (O/C), the litter obtained after DT and WT treatment corresponds to lignite. WT in a fluidized bed allows one to obtain biochar, in which the specific pore area is two times larger than the specific pore area of biochar obtained after DT in a fluidized bed. Biochar, obtained as a result of the poultry litter treatment in a fluidized bed using DT or WT method, is recommended to be used not only as a biofuel but also as an adsorbent or the soil fertilizer.

Keywords: biochar, poultry litter, dry and wet torrefaction, fixed bed, fluidized bed

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Authors: Anderson Braga Mendes

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Itaipu Hydroelectric Power Plant is settled on the Paraná River, which is a natural boundary between Brazil and Paraguay; thus, the facility is shared by both countries. Itaipu Power Plant is the biggest hydroelectric generator in the world, and provides clean and renewable electrical energy supply for 17% and 76% of Brazil and Paraguay, respectively. The plant started its generation in 1984. It counts on 20 Francis turbines and has installed capacity of 14,000 MWh. Its historic generation record occurred in 2016 (103,098,366 MWh), and since the beginning of its operation until the last day of 2016 the plant has achieved the sum of 2,415,789,823 MWh. The distinct sedimentologic aspects of the drainage area of Itaipu Power Plant, from its stretch upstream (Porto Primavera and Rosana dams) to downstream (Itaipu dam itself), were taken into account in order to best estimate the increase/decrease in the sediment yield by using data from 2001 to 2016. Such data are collected through a network of 14 automatic sedimentometric stations managed by the company itself and operating in an hourly basis, covering an area of around 136,000 km² (92% of the incremental drainage area of the undertaking). Since 1972, a series of lifespan studies for the Itaipu Power Plant have been made, being first assessed by Sir Hans Albert Einstein, at the time of the feasibility studies for the enterprise. From that date onwards, eight further studies were made through the last 44 years aiming to confer more precision upon the estimates based on more updated data sets. From the analysis of each monitoring station, it was clearly noticed strong increase tendencies in the sediment yield through the last 14 years, mainly in the Iguatemi, Ivaí, São Francisco Falso and Carapá Rivers, the latter situated in Paraguay, whereas the others are utterly in Brazilian territory. Five lifespan scenarios considering different sediment yield tendencies were simulated with the aid of the softwares SEDIMENT and DPOSIT, both developed by the author of the present work. Such softwares thoroughly follow the Borland & Miller methodology (empirical method of area-reduction). The soundest scenario out of the five ones under analysis indicated a lifespan foresight of 168 years, being the reservoir only 1.8% silted by the end of 2016, after 32 years of operation. Besides, the mass balance in the reservoir (water inflows minus outflows) between 1986 and 2016 shows that 2% of the whole Itaipu lake is silted nowadays. Owing to the convergence of both results, which were acquired by using different methodologies and independent input data, it is worth concluding that the mathematical modeling is satisfactory and calibrated, thus assigning credibility to this most recent lifespan estimate.

Keywords: Borland and Miller method, hydroelectricity, Itaipu Power Plant, lifespan, mass balance

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Authors: William Huang

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Retinal prostheses are currently limited to low resolution grayscale images that lack color and spatial information. This study develops a novel real-time image optimization framework and tools to encode maximum information to the prostheses which are constrained by the number of electrodes. One key idea is to localize main objects in images while reducing unnecessary background noise through region-contrast saliency maps. A novel color depth mapping technique was developed through MiniBatchKmeans clustering and color space selection. The resulting image was downsampled using bicubic interpolation to reduce image size while preserving color quality. In comparison to current schemes, the proposed framework demonstrated better visual quality in tested images. The use of the region-contrast saliency map showed improvements in efficacy up to 30%. Finally, the computational speed of this algorithm is less than 380 ms on tested cases, making real-time retinal prostheses feasible.

Keywords: retinal implants, virtual processing unit, computer vision, saliency maps, color quantization

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Authors: Bensaid A., Mostephaoui T., Nedjai R.

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Nowadays, wetlands are the subject of contradictory debates opposing scientific, political and administrative meanings. Indeed, given their multiple services (drinking water, irrigation, hydrological regulation, mineral, plant and animal resources...), wetlands concentrate many socio-economic and biodiversity issues. In some regions, they can cover vast areas (>100 thousand ha) of the landscape, such as the Camargue area in the south of France, inside the Rhone delta. The high biological productivity of wetlands, the strong natural selection pressures and the diversity of aquatic environments have produced many species of plants and animals that are found nowhere else. These environments are tremendous carbon sinks and biodiversity reserves depending on their age, composition and surrounding environmental conditions, wetlands play an important role in global climate projections. Covering more than 3% of the earth's surface, wetlands have experienced since the beginning of the 1990s a tremendous revival of interest, which has resulted in the multiplication of inventories, scientific studies and management experiments. The geographical and physical characteristics of the wetlands of the central region conceal a large number of natural habitats that harbour a great biological diversity. These wetlands, one of the natural habitats, are still influenced by human activities, especially agriculture, which affects its layout and functioning. In this perspective, decision-makers need to delimit spatial objects (natural habitats) in a certain way to be able to take action. Thus, wetlands are no exception to this rule even if it seems to be a difficult exercise to delimit a type of environment as whose main characteristic is often to occupy the transition between aquatic and terrestrial environment. However, it is possible to map wetlands with databases, derived from the interpretation of photos and satellite images, such as the European database Corine Land cover, which allows quantifying and characterizing for each place the characteristic wetland types. Scientific studies have shown limitations when using high spatial resolution images (SPOT, Landsat, ASTER) for the identification and characterization of small wetlands (1 hectare). To address this limitation, it is important to note that these wetlands generally represent spatially complex features. Indeed, the use of very high spatial resolution images (>3m) is necessary to map small and large areas. However, with the recent evolution of artificial intelligence (AI) and deep learning methods for satellite image processing have shown a much better performance compared to traditional processing based only on pixel structures. Our research work is also based on spectral and textural analysis on THR images (Spot and IRC orthoimage) using two object-oriented approaches, the nearest neighbour approach (k-NN) and the Super Vector Machine approach (SVM). The k-NN approach gave good results for the delineation of wetlands (wet marshes and moors, ponds, artificial wetlands water body edges, ponds, mountain wetlands, river edges and brackish marshes) with a kappa index higher than 85%.

Keywords: land development, GIS, sand dunes, segmentation, remote sensing

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Authors: Sanjeev Kumar Sharma, Arnab Mondal, Ranjit Kumar Upadhyay

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Excitable cells often produce different oscillatory activities that help us to understand the transmitting and processing of signals in the neural system. We consider a FitzHugh-Rinzel (FH-R) model and studied the different dynamics of the model by considering the parameter c as the predominant parameter. The model exhibits different types of neuronal responses such as regular spiking, mixed-mode bursting oscillations (MMBOs), elliptic bursting, etc. Based on the bifurcation diagram, we consider the three regimes (MMBOs, elliptic bursting, and quiescent state). An analytical treatment for the occurrence of the supercritical Hopf bifurcation is studied. Further, we extend our study to a network of a hundred neurons by considering the bi-directional synaptic coupling between them. In this article, we investigate the alternation of spiking propagation and bursting phenomena of an uncoupled and coupled FH-R neurons. We explore that the complete graph of heterogenous desynchronized neurons can exhibit different types of bursting oscillations for certain coupling strength. For higher coupling strength, all the neurons in the network show complete synchronization.

Keywords: excitable neuron model, spiking-bursting, stability and bifurcation, synchronization networks

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Maryam Sadeghi, Claude Bazin, Daniel Hodouin, Laura Perez Barnuevo

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The performance of a separation process is usually evaluated using performance indices calculated from elemental assays readily available from the chemical analysis laboratory. However, the separation process performance is essentially related to the properties of the minerals that carry the elements and not those of the elements. Since elements or metals can be carried by valuable and gangue minerals in the ore and that each mineral responds differently to a mineral processing method, the use of only elemental assays could lead to erroneous or uncertain conclusions on the process performance. This paper discusses the advantages of using performance indices calculated from minerals content, such as minerals recovery, for process performance assessments. A method is presented that uses elemental assays to estimate the minerals content of the solids in various process streams. The method combines the stoichiometric composition of the minerals and constraints of mass conservation for the minerals through the concentration process to estimate the minerals content from elemental assays. The advantage of assessing a concentration process using mineral based performance indices is illustrated for an iron ore concentration circuit.

Keywords: data reconciliation, iron ore concentration, mineral composition, process performance assessment

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Authors: Mohammed Adamu Musa, Shehu Saminu Babba

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The technological trend towards waste utilization and cost reduction in industrial processing has attracted use of Rice Husk as a value added material. Both rice husk (RH) and Rice Husk Ash (RHA) has been found suitable for wide range of domestic as well as industrial applications. Therefore, the purpose of this research is to produce high grade sodium silicate from rice husk ash by considering the effect of temperature and time of heating as the process variables. The experiment was performed by heating the rice husk at temperatures 500 °C, 600 °C, 700 °C and 800 °C and time 60min, 90min, 120min and 150min were used to obtain the ash. 1.0M of aqueous sodium hydroxide solution was used to dissolve the silicate from the ash, which contained crude sodium silicate. In addition, the ash was neutralized by adding 5M of HCL until the pH reached 3.5 to give silica gel. At 6000C and 120mins, 94.23% silica was obtained from the RHA. At higher temperatures (700 °C and 800 °C) the percentage yield of silica reduced due to surface melting and carbon fixation in the lattice caused by presence of potassium. For this research, 600 °C is considered to be the optimum temperature for silica production from RHA. Silica produced from RHA can generate aggregate value and can be used in areas such as pulp and paper, plastic and rubber reinforcement industries.

Keywords: burning, rice husk, rice husk ash, silica, silica gel, temperature

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Authors: Sitthichai Laisema, Panita Wannapiroon

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This study is a research and development which is intended to: 1) design ubiquitous collaborative learning activities with virtual teams using CPS processes to develop creative thinking and collaboration skills, and 2) assess the suitability of the ubiquitous collaborative learning activities. Its methods are divided into 2 phases. Phase 1 is the design of ubiquitous collaborative learning activities with virtual teams using CPS processes, phase 2 is the assessment of the suitability of the learning activities. The samples used in this study are 5 professionals in the field of learning activity design, ubiquitous learning, information technology, creative thinking, and collaboration skills. The results showed that ubiquitous collaborative learning activities with virtual teams using CPS processes to develop creative thinking and collaboration skills consist of 3 main steps which are: 1) preparation before learning, 2) learning activities processing and 3) performance appraisal. The result of the learning activities suitability assessment from the professionals is in the highest level.

Keywords: ubiquitous learning, collaborative learning, virtual team, creative problem solving

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Authors: Khan Farhan Rafat, M. Junaid Hussain

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Seamless modification of an entity for the purpose of hiding a message of significance inside its substance in a manner that the embedding remains oblivious to an observer is known as steganography. Together with today's pervasive registering frameworks, steganography has developed into a science that offers an assortment of strategies for stealth correspondence over the globe that must, however, need a critical appraisal from security breach standpoint. Microsoft Word is amongst the preferably used word processing software, which comes as a part of the Microsoft Office suite. With a user-friendly graphical interface, the richness of text editing, and formatting topographies, the documents produced through this software are also most suitable for stealth communication. This research aimed not only to epitomize the fundamental concepts of steganography but also to expound on the utilization of Microsoft Word document as a carrier for furtive message exchange. The exertion is to examine contemporary message hiding schemes from security aspect so as to present the explorative discoveries and suggest enhancements which may serve a wellspring of information to encourage such futuristic research endeavors.

Keywords: hiding information in plain sight, stealth communication, oblivious information exchange, conceal, steganography

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Awatef Hicheur Cairns, Billel Gueni, Hind Hafdi, Christian Joubert, Nasser Khelifa

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Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to discover, analyze and provide a visual representation of complete educational processes. In this paper, we present our distributed computation platform which allows different education centers and institutions to load their data and access to advanced data mining and process mining services. To achieve this, we present also a comparative study of the different clustering techniques developed in the context of process mining to partition efficiently educational traces. Our goal is to find the best strategy for distributing heavy analysis computations on many processing nodes of our platform.

Keywords: educational process mining, distributed process mining, clustering, distributed platform, educational data mining, ProM

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Anurag Jain, Rajneesh Kumar

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Cloud computing is the outcome of rapid growth of internet. Due to elastic nature of cloud computing and unpredictable behavior of user, load balancing is the major issue in cloud computing paradigm. An efficient load balancing technique can improve the performance in terms of efficient resource utilization and higher customer satisfaction. Load balancing can be implemented through task scheduling, resource allocation and task migration. Various parameters to analyze the performance of load balancing approach are response time, cost, data processing time and throughput. This paper demonstrates a two level load balancer approach by combining join idle queue and join shortest queue approach. Authors have used cloud analyst simulator to test proposed two level load balancer approach. The results are analyzed and compared with the existing algorithms and as observed, proposed work is one step ahead of existing techniques.

Keywords: cloud analyst, cloud computing, join idle queue, join shortest queue, load balancing, task scheduling

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Authors: Cinzia Federico, Gaetano Giudice, Salvatore Inguaggiato, Marco Liuzzo, Maria Pedone, Fabio Vita, Christoph Kern, Leonardo La Pica, Giovannella Pecoraino, Lorenzo Calderone, Vincenzo Francofonte

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The periods of increased fumarolic activity at La Fossa volcano have been characterized, since early 80's, by changes in the gas chemistry and in the output rate of fumaroles. Excepting the direct measurements of the steam output from fumaroles performed from 1983 to 1995, the mass output of the single gas species has been recently measured, with various methods, only sporadically or for short periods. Since 2008, a scanning DOAS system is operating in the Palizzi area for the remote measurement of the in-plume SO₂ flux. On these grounds, the need of a cross-comparison of different methods for the in situ measurement of the output rate of different gas species is envisaged. In 2015, two field campaigns have been carried out, aimed at: 1. The mapping of the concentration of CO₂, H₂S and SO₂ in the fumarolic plume at 1 m from the surface, by using specific open-path diode tunable lasers (GasFinder Boreal Europe Ltd.) and an Active DOAS for SO₂, respectively; these measurements, coupled to simultaneous ultrasonic wind speed and meteorological data, have been elaborated to obtain the dispersion map and the output rate of single species in the overall fumarolic field; 2. The mapping of the concentrations of CO₂, H₂S, SO₂, H₂O in the fumarolic plume at 0.5 m from the soil, by using an integrated system, including IR spectrometers and specific electrochemical sensors; this has provided the concentration ratios of the analysed gas species and their distribution in the fumarolic field; 3. The in-fumarole sampling of vapour and measurement of the steam output, to validate the remote measurements. The dispersion map of CO₂, obtained from the tunable laser measurements, shows a maximum CO₂ concentration at 1m from the soil of 1000 ppmv along the rim, and 1800 ppmv in the inner slopes. As observed, the largest contribution derives from a wide fumarole of the inner-slope, despite its present outlet temperature of 230°C, almost 200°C lower than those measured at the rim fumaroles. Actually, fumaroles in the inner slopes are among those emitting the largest amount of magmatic vapour and, during the 1989-1991 crisis, reached the temperature of 690°C. The estimated CO₂ and H₂S fluxes are 400 t/d and 4.4 t/d, respectively. The coeval SO₂ flux, measured by the scanning DOAS system, is 9±1 t/d. The steam output, recomputed from CO₂ flux measurements, is about 2000 t/d. The various direct and remote methods (as described at points 1-3) have produced coherent results, which encourage to the use of daily and automatic DOAS SO₂ data, coupled with periodic in-plume measurements of different acidic gases, to obtain the total mass rates.

Keywords: DOAS, fumaroles, plume, tunable laser

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Authors: Kelly Danielly Da Silva Alcantara, Daniel Fernando Molina Junqueira, Graziella Colato Antonio

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The Law No. 12,305/10, established by the National Solid Waste Policy (PNRS), provides major changes in the management and managing scenario of solid waste in Brazil. The PNRS established since changes from population behavior as environmental and the consciousness and commitment of the companies with the waste produced. The objective of this project is to conduct a benchmarking study of the management models of Waste Management Municipal Solid (MSW) in national and international levels emphasizing especially in the European Union (Portugal, France and Germany), which are reference countries in energy development, sustainability and consequently recovery of waste generated. The management that encompasses all stages that are included in this sector will be analyzed by benchmarking, as the collection, transportation, processing/treatment and final disposal of waste. Considering the needs to produce clean energy in Brazil, this study will allow the determination to the best treatment of the waste in order to reduce the amount of waste and increase the lifetime of the landfill. Finally, it intends to identify the energy recovery potential through a study analysis of economic viability, energy and sustainable based on a holistic approach.

Keywords: benchmarking, energy recovery, landfill, municipal solid waste

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Authors: Julien Romieux, Fabio Verdicchio

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Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: cooperative signal processing, signal representation and approximation, power management, wireless sensor networks

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Authors: M. A. Ferhat, M. N. Boukhatem, F. Chemat

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Microwave Clevenger or microwave accelerated distillation (MAD) is a combination of microwave heating and distillation, performed at atmospheric pressure without added any solvent or water. Isolation and concentration of volatile compounds are performed by a single stage. MAD extraction of orange essential oil was studied using fresh orange peel from Valencia late cultivar oranges as the raw material. MAD has been compared with a conventional technique, which used a Clevenger apparatus with hydro-distillation (HD). MAD and HD were compared in term of extraction time, yields, chemical composition and quality of the essential oil, efficiency and costs of the process. Extraction of essential oils from orange peels with MAD was better in terms of energy saving, extraction time (30 min versus 3 h), oxygenated fraction (11.7% versus 7.9%), product yield (0.42% versus 0.39%) and product quality. Orange peels treated by MAD and HD were observed by scanning electronic microscopy (SEM). Micrographs provide evidence of more rapid opening of essential oil glands treated by MAD, in contrast to conventional hydro-distillation.

Keywords: clevenger, microwave, extraction; hydro-distillation, essential oil, orange peel

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