Search results for: fully spatial signal processing

Authors: Farida Mellah, Abdelhak Boutaleb, Bachir Henni, Dalila Berdous, Abdelhamid Mellah

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Context/Purpose: One of the largest mining operations for lead and zinc deposits in northwestern Algeria in more than thirty years, El Abed is now the abandoned mine that has been inactive since 2004, leaving large amounts of accumulated mining waste under the influence of Wind, erosion, rain, and near agricultural lands. Materials & Methods: This study aims to verify the concentrations and sources of heavy metals for surface samples containing randomly taken soil. Chemical analyses were performed using iCAP 7000 Series ICP-optical emission spectrometer, using a set of environmental quality indicators by calculating the enrichment factor using iron and aluminum references, geographic accumulation index and geographic information system (GIS). On the basis of the spatial distribution. Results: The results indicated that the average metal concentration was: (As = 30,82),(Pb = 1219,27), (Zn = 2855,94), (Cu = 5,3), mg/Kg,based on these results, all metals except Cu passed by GBV in the Earth's crust. Environmental quality indicators were calculated based on the concentrations of trace metals such as lead, arsenic, zinc, copper, iron and aluminum. Interpretation: This study investigated the concentrations and sources of trace metals, and by using quality indicators and statistical methods, lead, zinc, and arsenic were determined from human sources, while copper was a natural source. And based on the spatial analysis on the basis of GIS, many hot spots were identified in the El-Abed region. Conclusion: These results could help in the development of future treatment strategies aimed primarily at eliminating materials from mining waste.

Keywords: soil contamination, trace metals, geochemical indices, El Abed mine, Algeria

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Authors: Firdous Nizar, Carmela Cucuzzella

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This paper explores how the integrated design process (IDP) was adopted for an architectural project. The IDP is a relatively new approach to collaborative design in architectural design projects in Canada. It has gained much traction recently as the closest possible approach to the successful management of low energy building projects and has been advocated as a productive method for multi-disciplinary collaboration within complex projects. This study is based on the premise that there are explicit and implicit dimensions of power within the integrated design process (IDP) in the green building industry that may or may not lead to irreconcilable differences in a process that demands consensus. To gain insight on the potential gap between the theoretical promises and practical realities of the IDP, a review of existing IDP literature is compared with a case study analysis of a competition-based architectural project in Canada, a first to incorporate the IDP in its overall design format. This paper aims to address the undertheorized power relations of the IDP in a real project. It presents a critical assessment through the lens of the combined theories of deliberative democracy by Jürgen Habermas, with that of agonistic pluralism by political theorist Chantal Mouffe. These two theories are intended to more appropriately embrace the conflictual situations in collaborative environments, and shed light on the relationships of power, between engineers, city officials, architects, and designers in this conventional consensus-based model. In addition, propositions for a shift in approach that embraces conflictual differences among its participants are put forth based on concepts of critical spatial practice by Markus Meissen. As IDP is a relatively new design process, it requires much deliberation on its structure from the theoretical framework built in this paper in order to unlock its true potential.

Keywords: agonistic pluralism, critical spatial practice, deliberative democracy, integrated design process

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Authors: Fereshteh Nazeri Bahadori

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In today's competitive environment, any company that aims to operate in a market, whether industrial or consumer markets, must know that it cannot address all the tastes and demands of customers at once and serve them all. The study of consumer memory is considered an important subject in marketing research, and many companies have conducted studies on this subject and the factors affecting it due to its importance. Therefore, the current study tries to investigate the relationship between consumers' attention, literacy, and memory. Memory has a very close relationship with learning. Memory is the collection of all the information that we have understood and stored. One of the important subjects in consumer behavior is information processing by the consumer. One of the important factors in information processing is the mental involvement of the consumer, which has attracted a lot of attention in the past two decades. Since consumers are the turning point of all marketing activities, successful marketing begins with understanding why and how consumers behave. Therefore, in the current study, the role of executive attention and literacy on consumers' memory has been investigated. The results showed that executive attention and literacy would play a significant role in the long-term and short-term memory of consumers.

Keywords: literacy, consumer memory, executive attention, psychology of consumer behavior

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Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

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Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Camellia Al-Ibrahim, Jenny Yiend, Sukhwinder S. Shergill

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Background: Attention bias to threat play a role in the development, maintenance, and exacerbation of delusional beliefs in schizophrenia in which patients emphasize the threatening characteristics of stimuli and prioritise them for processing. Cognitive control deficits arise when task-irrelevant emotional information elicits attentional bias and obstruct optimal performance. This study is investigating neural correlates of interference effect of linguistic threat and whether these effects are independent of delusional severity. Methods: Using an event-related functional magnetic resonance imaging (fMRI), neural correlates of interference effect of linguistic threat during the emotional Stroop task were investigated and compared patients with schizophrenia with high (N=17) and low (N=16) paranoid symptoms and healthy controls (N=20). Participants were instructed to identify the font colour of each word presented on the screen as quickly and accurately as possible. Stimuli types vary between threat-relevant, positive and neutral words. Results: Group differences in whole brain effects indicate decreased amygdala activity in patients with high paranoid symptoms compared with low paranoid patients and healthy controls. Regions of interest analysis (ROI) validated our results within the amygdala and investigated changes within the striatum showing a pattern of reduced activation within the clinical group compared to healthy controls. Delusional severity was associated with significant decreased neural activity in the striatum within the clinical group. Conclusion: Our findings suggest that the emotional interference mediated by the amygdala and striatum may reduce responsiveness to threat-related stimuli in schizophrenia and that attenuation of fMRI Blood-oxygen-level dependent (BOLD) signal within these areas might be influenced by the severity of delusional symptoms.

Keywords: attention bias, fMRI, Schizophrenia, Stroop

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Authors: Abdulhakeem Sulyman, Olukotun Zainab, Hammed Abdulquadri

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Lipases (triacylglycerol acyl hydrolases) (EC 3.1.1.3) are class of enzymes that catalyses the hydrolysis of triglycerides to glycerol and free fatty acids. They account for up to 10% of the enzyme in the market and have a wide range of applications in biofuel production, detergent formulation, leather processing and in food and feed processing industry. This research was conducted to study the effect of some metal ions on the activity of purified lipase produced by Aspergillus niger cultured on Vitellaria paradoxa shells. Purified lipase in 12.5 mM p-NPL was incubated with different metal ions (Zn²⁺, Ca²⁺, Mn²⁺, Fe²⁺, Na⁺, K⁺ and Mg²⁺). The final concentrations of metal ions investigated were 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and 1.0 mM. The results obtained from the study showed that Zn²⁺, Ca²⁺, Mn²⁺ and Fe²⁺ ions increased the activity of lipase up to 3.0, 3.0, 1.0, and 26.0 folds respectively. Lipase activity was partially inhibited by Na⁺ and Mg²⁺ with up to 88.5% and 83.7% loss of activity respectively. Lipase activity was also inhibited by K⁺ with up to 56.7% loss in the activity as compared to in the absence of metal ions. The study concluded that lipase produced by Aspergillus niger cultured on Vitellaria paradoxa shells can be activated by the presence of Zn²⁺, Ca²⁺, Mn²⁺ and Fe²⁺ and inhibited by Na⁺, K⁺ and Mg²⁺.

Keywords: Aspergillus niger, Vitellaria paradoxa, lipase, metal ions

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Authors: Chung-Chih Lin, Chien-Liang Wu

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The detection of the polymer melt state during manufacture process is regarded as an efficient way to control the molded part quality in advance. Online monitoring rheological property of polymer melt during processing procedure provides an approach to understand the melt state immediately. Rheological property reflects the polymer melt state at different processing parameters and is very important in injection molding process especially. An approach that demonstrates how to calculate rheological property of polymer melt through in-process measurement, using injection molding as an example, is proposed in this study. The system consists of two sensors and a data acquisition module can process the measured data, which are used for the calculation of rheological properties of polymer melt. The rheological properties of polymer melt discussed in this study include shear rate and viscosity which are investigated with respect to injection speed and melt temperature. The results show that the effect of injection speed on the rheological properties is apparent, especially for high melt temperature and should be considered for precision molding process.

Keywords: injection molding, melt viscosity, shear rate, monitoring

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Authors: Gavin Hutama Farandiarta, Hegi Adi Prabowo, Istiara Rizqillah Hanifah, Millati Hanifah Saprudin, Raden Iqrafia Ashna

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The increase of the petroleum’s consumption rate encourages industries to optimize and increase the activity in processing crude oil into petroleum. However, although the result gives a lot of benefits to humans worldwide, it also gives negative impact to the environment. One of the negative impacts of processing crude oil is the soil will be contaminated by petroleum sewage sludge. This petroleum sewage sludge, contains hydrocarbon compound and it can be calculated by Total Petroleum Hydrocarbon (TPH).Petroleum sludge waste is accounted as hazardous and toxic. The soil contamination caused by the petroleum sludge is very hard to get rid of. However, there is a way to manage the soil that is contaminated by petroleum sludge, which is by using heat (thermal desorption) in the process of remediation. There are several factors that affect the success rate of the remediation with the help of heat which are temperature, time, and air pressure in the desorption column. The remediation process using the help of heat is an alternative in soil recovery from the petroleum pollution which highly effective, cheap, and environmentally friendly that produces uncontaminated soil and the petroleum that can be used again.

Keywords: petroleum sewage sludge, remediation soil, thermal desorption, total petroleum hydrocarbon (TPH)

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Authors: Vitaliy Petrov, Natalia Shusharina, Vitaliy Kasymov, Maksim Patrushev, Evgeny Bogdanov

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The leading worldwide death reasons are ischemic heart disease and other cardiovascular illnesses. Generally, the common symptom is high blood pressure. Long-time blood pressure control is very important for the prophylaxis, correct diagnosis and timely therapy. Non-invasive methods which are based on Korotkoff sounds are impossible to apply often and for a long time. Implantable devices can combine longtime monitoring with high accuracy of measurements. The main purpose of this work is to create a real-time monitoring system for decreasing the death rate from cardiovascular diseases. These days implantable electronic devices began to play an important role in medicine. Usually implantable devices consist of a transmitter, powering which could be wireless with a special made battery and measurement circuit. Common problems in making implantable devices are short lifetime of the battery, big size and biocompatibility. In these work, blood pressure measure will be the focus because it’s one of the main symptoms of cardiovascular diseases. Our device will consist of three parts: the implantable pressure sensor, external transmitter and automated workstation in a hospital. The Implantable part of pressure sensors could be based on piezoresistive or capacitive technologies. Both sensors have some advantages and some limitations. The Developed circuit is based on a small capacitive sensor which is made of the technology of microelectromechanical systems (MEMS). The Capacitive sensor can provide high sensitivity, low power consumption and minimum hysteresis compared to the piezoresistive sensor. For this device, it was selected the oscillator-based circuit where frequency depends from the capacitance of sensor hence from capacitance one can calculate pressure. The external device (transmitter) used for wireless charging and signal transmission. Some implant devices for these applications are passive, the external device sends radio wave signal on internal LC circuit device. The external device gets reflected the signal from the implant and from a change of frequency is possible to calculate changing of capacitance and then blood pressure. However, this method has some disadvantages, such as the patient position dependence and static using. Developed implantable device doesn’t have these disadvantages and sends blood pressure data to the external part in real-time. The external device continuously sends information about blood pressure to hospital cloud service for analysis by a physician. Doctor’s automated workstation at the hospital also acts as a dashboard, which displays actual medical data of patients (which require attention) and stores it in cloud service. Usually, critical heart conditions occur few hours before heart attack but the device is able to send an alarm signal to the hospital for an early action of medical service. The system was tested with wireless charging and data transmission. These results can be used for ASIC design for MEMS pressure sensor.

Keywords: MEMS sensor, RF power, wireless data, oscillator-based circuit

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Authors: Nurhak Altın, Ayhan Kucukmanisa, Oguzhan Urhan

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It is important that highways are in good condition for traffic safety. Road crashes (road cracks, erosion of lane markings, etc.) can cause accidents by affecting driving. Image processing based methods for detecting road cracks are available in the literature. In this paper, a deep learning based road crack detection approach is proposed. YOLO (You Look Only Once) is adopted as core component of the road crack detection approach presented. The YOLO network structure, which is developed for object detection, is trained with road crack images as a new class that is not previously used in YOLO. The performance of the proposed method is compared using different training methods: using randomly generated weights and training their own pre-trained weights (transfer learning). A similar training approach is applied to the simplified version of the YOLO network model (tiny yolo) and the results of the performance are examined. The developed system is able to process 8 fps on NVIDIA Jetson TX1 development kit.

Keywords: deep learning, embedded platform, real-time processing, road crack detection

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Authors: Tayfun Salihoğlu

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Following the Law No: 6306 on Transformation of Disaster Risk Areas, urban transformation in Turkey found its legal basis. In the best practices all over the World, the urban transformation was shaped as part of comprehensive social programs through the discourses of renewing the economic, social and physical degraded parts of the city, producing spaces resistant to earthquakes and other possible disasters and creating a livable environment. In Turkish practice, a contradictory process is observed. In this study, it is aimed to develop a method for better understanding of the urban space in terms of disaster risks in order to constitute a basis for decisions in Kocaeli Urban Transformation Master Plan, which is being prepared by Kocaeli Metropolitan Municipality. The spatial unit used in the study is the 50x50 meter grids. In order to reflect the multidimensionality of urban transformation, three basic components that have spatial data in Kocaeli were identified. These components were named as 'Problems in Built-up Areas', 'Disaster Risks arising from Geological Conditions of the Ground and Problems of Buildings', and 'Inadequacy of Urban Services'. Each component was weighted and scored for each grid. In order to delimitate urban transformation zones Optimized Outlier Analysis (Local Moran I) in the ArcGIS 10.6.1 was conducted to test the type of distribution (clustered or scattered) and its significance on the grids by assuming the weighted total score of the grid as Input Features. As a result of this analysis, it was found that the weighted total scores were not significantly clustering at all grids in urban space. The grids which the input feature is clustered significantly were exported as the new database to use in further mappings. Total Score Map reflects the significant clusters in terms of weighted total scores of 'Problems in Built-up Areas', 'Disaster Risks arising from Geological Conditions of the Ground and Problems of Buildings' and 'Inadequacy of Urban Services'. Resulting grids with the highest scores are the most likely candidates for urban transformation in this citywide study. To categorize urban space in terms of urban transformation, Grouping Analysis in ArcGIS 10.6.1 was conducted to data that includes each component scores in significantly clustered grids. Due to Pseudo Statistics and Box Plots, 6 groups with the highest F stats were extracted. As a result of the mapping of the groups, it can be said that 6 groups can be interpreted in a more meaningful manner in relation to the urban space. The method presented in this study can be magnified due to the availability of more spatial data. By integrating with other data to be obtained during the planning process, this method can contribute to the continuation of research and decision-making processes of urban transformation master plans on a more consistent basis.

Keywords: urban transformation, GIS, disaster risk assessment, Kocaeli

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Authors: Maria Jędrzejczak, Patryk Pieniążek

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The European legal reality is on the eve of significant change. In European Union law, there is talk of a “fourth industrial revolution”, which is driven by massive data resources linked to powerful algorithms and powerful computing capacity. The above is closely linked to technological developments in the area of artificial intelligence, which has prompted an analysis covering both the legal environment as well as the economic and social impact, also from an ethical perspective. The discussion on the regulation of artificial intelligence is one of the most serious yet widely held at both European Union and Member State level. The literature expects legal solutions to guarantee security for fundamental rights, including privacy, in artificial intelligence systems. There is no doubt that personal data have been increasingly processed in recent years. It would be impossible for artificial intelligence to function without processing large amounts of data (both personal and non-personal). The main driving force behind the current development of artificial intelligence is advances in computing, but also the increasing availability of data. High-quality data are crucial to the effectiveness of many artificial intelligence systems, particularly when using techniques involving model training. The use of computers and artificial intelligence technology allows for an increase in the speed and efficiency of the actions taken, but also creates security risks for the data processed of an unprecedented magnitude. The proposed regulation in the field of artificial intelligence requires analysis in terms of its impact on the regulation on personal data protection. It is necessary to determine what the mutual relationship between these regulations is and what areas are particularly important in the personal data protection regulation for processing personal data in artificial intelligence systems. The adopted axis of considerations is a preliminary assessment of two issues: 1) what principles of data protection should be applied in particular during processing personal data in artificial intelligence systems, 2) what regulation on liability for personal data breaches is in such systems. The need to change the regulations regarding the rights and obligations of data subjects and entities processing personal data cannot be excluded. It is possible that changes will be required in the provisions regarding the assignment of liability for a breach of personal data protection processed in artificial intelligence systems. The research process in this case concerns the identification of areas in the field of personal data protection that are particularly important (and may require re-regulation) due to the introduction of the proposed legal regulation regarding artificial intelligence. The main question that the authors want to answer is how the European Union regulation against data protection breaches in artificial intelligence systems is shaping up. The answer to this question will include examples to illustrate the practical implications of these legal regulations.

Keywords: data protection law, personal data, AI law, personal data breach

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Authors: Michael Sinclair, Andrea Ghermandi, Sheela A. Moses, Joseph Sabu

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Passively crowdsourced data, such as geotagged photographs from social media, represent an opportunistic source of location-based and time-specific behavioral data for ecosystem services analysis. Such data have innovative applications for environmental management and protection, which are replicable at wide spatial scales and in the context of both developed and developing countries. Here we test one such innovation, based on the analysis of the metadata of online geotagged photographs, to investigate the provision of recreational services by the entire network of wetland ecosystems in the state of Kerala, India. We estimate visitation to individual wetlands state-wide and extend, for the first time to a developing region, the emerging application of cultural ecosystem services modelling using data from social media. The impacts of restoration of wetland areal extension and water quality improvement are explored as a means to inform more sustainable management strategies. Findings show that improving water quality to a level suitable for the preservation of wildlife and fisheries could increase annual visits by 350,000, an increase of 13% in wetland visits state-wide, while restoring previously encroached wetland area could result in a 7% increase in annual visits, corresponding to 49,000 visitors, in the Ashtamudi and Vembanad lakes alone, two large coastal Ramsar wetlands in Kerala. We discuss how passive crowdsourcing of social media data has the potential to improve current ecosystem service analyses and environmental management practices also in the context of developing countries.

Keywords: coastal wetlands, cultural ecosystem services, India, passive crowdsourcing, social media, wetland restoration

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Changyin Zhou

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Verb-argument relation is a very important aspect of syntax-semantics interaction in sentence processing. Previous ERP (event related potentials) studies in this field mainly concentrated on the relation between the verb and its core arguments. The present study aims to reveal the ERP pattern of Chinese pseudo-object structures (SOSs), in which a peripheral argument is promoted to occupy the position of the patient object, as compared with the patient object structures (POSs). The ERP data were collected when participants were asked to perform acceptability judgments about Chinese phrases. Our result shows that, similar to the previous studies of number-of-argument violations, Chinese SOSs show a bilaterally distributed N400 effect. But different from all the previous studies of verb-argument relations, Chinese SOSs demonstrate a sustained anterior positivity (SAP). This SAP, which is the first report related to complexity of argument structure operation, reflects the integration difficulty of the newly promoted arguments and the progressive nature of well-formedness checking in the processing of Chinese SOSs.

Keywords: Chinese pseudo-object structures, ERP, sustained anterior positivity, verb-argument relation

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Authors: Farzaneh Bahrami

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Different indicators, such as modal shares in mobility practices or car ownership, may suggest that the century of car dominance - at least in Europe and North America - is already behind us. If the emergence of the car had radical spatial and social consequences, what would be the implications of its gradual disappearance - which could be expected in the context of ecological consciousness, economic and energetic constraints as a result of both urban policies as well as lifestyle choices? To what extend shall urban experts account for this limited but visible transition from car-dominated systems towards alternative models of mobility in which the individual-motorized mobility (car) is not central; what models of urbanity could be imagined to support such a transformation? We have examined a selection of projects at different scales and within different contexts - new planned cities, dense urban areas or territories of dispersion – whose visions involve a significant shift from the current car system. We have been looking into their tools, strategies and different measures of car reduction, as well as their varied approaches to public space as an inevitable corollary to this change. The car’s dominance was formerly questioned by advocates of public space, rather than through interests in ecological urban design or other urban planning concerns. In the 60s already a universal longing for the qualities of traditional urban space led to a critique of the proliferation of fast roads, and thus the car’s colonization of everyday life. Reclamation of public space as the city’s quintessential social territory reappears today in contemporary discourses and reinforces the shift-provoking trends towards a new urbanity freed from car dominance. In a hypothetical process of the progressive phasing-out of the car, we shall expect fundamental transformations in spatial practices of the city, accompanied by the physical configuration of its public spaces. What will be the main characteristics of the new emerging spaces of sociability and where shall we encounter them? This contribution is an ongoing research within the framework of Post-Car World, an interdisciplinary project that explores the future of mobility through the role of the car.

Keywords: mobility, urbanity, future visions, public space

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Authors: K. Sahithya, I. Balasundar, Pritapant, T. Raghua

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Ni-based superalloy with a nominal composition Ni-14% Cr-11% Co-5.8% Mo-2.4% Ti-2.4% Nb-2.8% Al-0.26 % Fe-0.032% Si-0.069% C (all in wt %) is used as turbine discs in a variety of aero engines. Like any other superalloy, the primary processing of the as-cast superalloy poses a major challenge due to its complex alloy chemistry. The challenge was circumvented by characterizing the different phases present in the material, optimizing the homogenization treatment, identifying a suitable thermomechanical processing window using dynamic materials modeling. The as-cast material was subjected to homogenization at 1200°C for a soaking period of 8 hours and quenched using different media. Water quenching (WQ) after homogenization resulted in very fine spherical γꞌ precipitates of sizes 30-50 nm, whereas furnace cooling (FC) after homogenization resulted in bimodal distribution of precipitates (primary gamma prime of size 300nm and secondary gamma prime of size 5-10 nm). MC type primary carbides that are stable till the melting point of the material were found in both WQ and FC samples. Deformation behaviour of both the materials below (1000-1100°C) and above gamma prime solvus (1100-1175°C) was evaluated by subjecting the material to series of compression tests at different constant true strain rates (0.0001/sec-1/sec). An in-detail examination of the precipitate dislocation interaction mechanisms carried out using TEM revealed precipitate shearing and Orowan looping as the mechanisms governing deformation in WQ and FC, respectively. Incoherent/semi coherent gamma prime precipitates in the case of FC material facilitates better workability of the material, whereas the coherent precipitates in WQ material contributed to higher resistance to deformation of the material. Both the materials exhibited discontinuous dynamic recrystallization (DDRX) above gamma prime solvus temperature. The recrystallization kinetics was slower in the case of WQ material. Very fine grain boundary carbides ( ≤ 300 nm) retarded the recrystallisation kinetics in WQ. Coarse carbides (1-5 µm) facilitate particle stimulated nucleation in FC material. The FC material was cogged (primary hot working) 1120˚C, 0.03/sec resulting in significant grain refinement, i.e., from 3000 μm to 100 μm. The primary processed material was subjected to intensive thermomechanical deformation subsequently by reducing the temperature by 50˚C in each processing step with intermittent heterogenization treatment at selected temperatures aimed at simultaneous coarsening of the gamma prime precipitates and refinement of the gamma matrix grains. The heterogeneous annealing treatment carried out, resulted in gamma grains of 10 μm and gamma prime precipitates of 1-2 μm. Further thermo mechanical processing of the material was carried out at 1025˚C to increase the homogeneity of the obtained micro-duplex structure.

Keywords: superalloys, dynamic material modeling, nickel alloys, dynamic recrystallization, superplasticity

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Authors: Roya Pournaghi

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Measuring the extent of daily use of the libraries study space is of utmost significance in order to develop, re-organize and maintain the efficiency of the study space. The current study aimed to employ GIS in analyzing the study halls space of the document center and central library of Tehran University and determine the extent of use of the study chairs and desks by the students-intended users. This combination of survey methods - descriptive design system. In order to collect the required data and a description of the method, To implement and entering data into ArcGIS software. It also analyzes the data and displays the results on the library floor map design method were used. And spatial database design and plan has been done at the Central Library of Tehran University through the amount of space used by members of the Library and Information halls plans. Results showed that Biruni's hall is allocated the highest occupancy rate to tables and chairs compared to other halls. In the Hall of Science and Technology, with an average occupancy rate of 0.39 in the tables represents the lowest users and Rashid al-Dins hall, and Science and Technology’s hall with an average occupancy rate (0.40) represents the lowest users of seats. In this study, the comparison of the space is occupied at different period as a study’s hall in the morning, evenings, afternoons, and several months was performed through GIS. This system analyzed the space relationship effectively and efficiently. The output of this study can be used by administrators and librarians to determine the exact amount of using the Equipment of study halls and librarians can use the output map to design more efficient space at the library.

Keywords: geospatial information system, spatial analysis, reading room, academic libraries, library’s user, central library of Tehran university

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Authors: Ruolan Zhang, Ryo Imai, Naoko Senda, Tomoyuki Sakai

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Enzymes have attracted increasing attentions in industrial manufacturing for their applicability in catalyzing complex chemical reactions under mild conditions. Directed evolution has become a powerful approach to optimize enzymes and exploit their full potentials under the circumstance of insufficient structure-function knowledge. With the incorporation of cell-free synthetic biotechnology, rapid enzyme synthesis can be realized because no cloning procedure such as transfection is needed. Its open environment also enables direct enzyme measurement. These properties of cell-free biotechnology lead to excellent throughput of enzymes generation. However, the capabilities of current screening methods have limitations. Fluorescence-based assay needs applicable fluorescent label, and the reliability of acquired enzymatic activity is influenced by fluorescent label’s binding affinity and photostability. To acquire the natural activity of an enzyme, another method is to combine pre-screening step and high-performance liquid chromatography (HPLC) measurement. But its throughput is limited by necessary time investment. Hundreds of variants are selected from libraries, and their enzymatic activities are then identified one by one by HPLC. The turn-around-time is 30 minutes for one sample by HPLC, which limits the acquirable enzyme improvement within reasonable time. To achieve the real high-throughput enzyme screening, i.e., obtain reliable enzyme improvement within reasonable time, a widely applicable high-throughput measurement of enzymatic reactions is highly demanded. Here, a high-throughput screening method using broadband coherent anti-Stokes Raman spectroscopy (CARS) was proposed. CARS is one of coherent Raman spectroscopy, which can identify label-free chemical components specifically from their inherent molecular vibration. These characteristic vibrational signals are generated from different vibrational modes of chemical bonds. With the broadband CARS, chemicals in one sample can be identified from their signals in one broadband CARS spectrum. Moreover, it can magnify the signal levels to several orders of magnitude greater than spontaneous Raman systems, and therefore has the potential to evaluate chemical's concentration rapidly. As a demonstration of screening with CARS, alcohol dehydrogenase, which converts ethanol and nicotinamide adenine dinucleotide oxidized form (NAD+) to acetaldehyde and nicotinamide adenine dinucleotide reduced form (NADH), was used. The signal of NADH at 1660 cm⁻¹, which is generated from nicotinamide in NADH, was utilized to measure the concentration of it. The evaluation time for CARS signal of NADH was determined to be as short as 0.33 seconds while having a system sensitivity of 2.5 mM. The time course of alcohol dehydrogenase reaction was successfully measured from increasing signal intensity of NADH. This measurement result of CARS was consistent with the result of a conventional method, UV-Vis. CARS is expected to have application in high-throughput enzyme screening and realize more reliable enzyme improvement within reasonable time.

Keywords: Coherent Anti-Stokes Raman Spectroscopy, CARS, directed evolution, enzyme screening, Raman spectroscopy

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Authors: Yahoel Van Essen, Gordon Ho, Brett Russell, Hans-Georg Worms, Xiao Lin Long, Edward David Cooper, Avner Bachar

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Weeds reduce farm and forest productivity, invade crops, smother pastures and some can harm livestock. Farmers need to spend a significant amount of money to control weeds by means of biological, chemical, cultural, and physical methods. To solve the global agricultural labor shortage and remove poisonous chemicals, a fully autonomous, eco-friendly, and sustainable weeding technology is developed. This takes the form of a weeding robot, ‘Roboweeder’. Roboweeder includes a four-wheel-drive self-driving vehicle, a 4-DOF robotic arm which is mounted on top of the vehicle, an electromagnetic wave generator (magnetron) which is mounted on the “wrist” of the robotic arm, 48V battery packs, and a control/communication system. Cameras are mounted on the front and two sides of the vehicle. Using image processing and recognition, distinguish types of weeds are detected before being eliminated. The electromagnetic wave technology is applied to heat the individual weeds and clusters dielectrically causing them to wilt and die. The 4-DOF robotic arm was modeled mathematically based on its structure/mechanics, each joint’s load, brushless DC motor and worm gear’ characteristics, forward kinematics, and inverse kinematics. The Proportional-Integral-Differential control algorithm is used to control the robotic arm’s motion to ensure the waveguide aperture pointing to the detected weeds. GPS and machine vision are used to traverse the farm and avoid obstacles without the need of supervision. A Roboweeder prototype has been built. Multiple test trials show that Roboweeder is able to detect, point, and kill the pre-defined weeds successfully although further improvements are needed, such as reducing the “weeds killing” time and developing a new waveguide with a smaller waveguide aperture to avoid killing crops surrounded. This technology changes the tedious, time consuming and expensive weeding processes, and allows farmers to grow more, go organic, and eliminate operational headaches. A patent of this technology is pending.

Keywords: autonomous navigation, machine vision, precision heating, sustainable and eco-friendly

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Pinky Mae O. De Leon, Franchezka S. P. Flores

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This study focuses on applying an enhanced density-based spatial clustering algorithm with noise for fire risk assessments and warnings in Metro Manila. Unlike other clustering algorithms, DBSCAN is known for its ability to identify arbitrary-shaped clusters and its resistance to noise. However, its performance diminishes when handling high dimensional data, wherein it can read the noise points as relevant data points. Also, the algorithm is dependent on the parameters (eps & minPts) set by the user; choosing the wrong parameters can greatly affect its clustering result. To overcome these challenges, the study proposes three key enhancements: first is to utilize multiple MinHash and locality-sensitive hashing to decrease the dimensionality of the data set, second is to implement Jaccard Similarity before applying the parameter Epsilon to ensure that only similar data points are considered neighbors, and third is to use the concept of Jaccard Neighborhood along with the parameter MinPts to improve in classifying core points and identifying noise in the data set. The results show that the modified DBSCAN algorithm outperformed three other clustering methods, achieving fewer outliers, which facilitated a clearer identification of fire-prone areas, high Silhouette score, indicating well-separated clusters that distinctly identify areas with potential fire hazards and exceptionally achieved a low Davies-Bouldin Index and a high Calinski-Harabasz score, highlighting its ability to form compact and well-defined clusters, making it an effective tool for assessing fire hazard zones. This study is intended for assessing areas in Metro Manila that are most prone to fire risk.

Keywords: DBSCAN, clustering, Jaccard similarity, MinHash LSH, fires

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Authors: Zahra Fotovatnia, Jeffery A. Jones, Alexandra Gottardo

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Speech communication often occurs in environments where noise conceals part of a message. Listeners should compensate for the lack of auditory information by picking up distinct acoustic cues and using semantic and sentential context to recreate the speaker’s intended message. This situation seems to be more challenging in a nonnative than native language. On the other hand, early bilinguals are expected to show an advantage over the late bilingual and monolingual speakers of a language due to their better executive functioning components. In this study, English monolingual speakers were compared with early and late nonnative speakers of English to understand speech in noise processing (SIN) and the underlying neurobiological features of this phenomenon. Auditory mismatch negativities (MMNs) were recorded using a double-oddball paradigm in response to a minimal pair that differed in their middle vowel (beat/bit) at Wilfrid Laurier University in Ontario, Canada. The results did not show any significant structural and electroneural differences across groups. However, vocabulary knowledge correlated positively with performance on tests that measured SIN processing in participants who learned English after age 6. Moreover, their performance on the test negatively correlated with the integral area amplitudes in the left superior temporal gyrus (STG). In addition, the STG was engaged before the inferior frontal gyrus (IFG) in noise-free and low-noise test conditions in all groups. We infer that the pre-attentive processing of words engages temporal lobes earlier than the fronto-central areas and that vocabulary knowledge helps the nonnative perception of degraded speech.

Keywords: degraded speech perception, event-related brain potentials, mismatch negativities, brain regions

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Authors: Wenyang Wan

Abstract:

The study of urban land use and landscape pattern is the current hotspot in the fields of planning and design, ecology, etc., which is of great significance for the construction of the overall humanistic ecosystem of the city and optimization of the urban spatial structure. Banan District, as the main part of the eastern eco-city planning of Chongqing Municipality, is a high ground for highlighting the ecological characteristics of Chongqing, realizing effective transformation of ecological value, and promoting the integrated development of urban and rural areas. The analytical methods of land use transfer matrix (GIS) and landscape pattern index (Fragstats) were used to study the characteristics and laws of the evolution of land use landscape pattern in Banan District from 2000 to 2020, which provide some reference value for Banan District to alleviate the ecological contradiction of landscape. The results of the study show that ① Banan District is rich in land use types, of which the area of cultivated land will still account for 57.15% of the total area of the landscape until 2020, accounting for an absolute advantage in land use structure of Banan District; ② From 2000 to 2020, land use conversion in Banan District is characterized as Cropland > woodland > grassland > shrubland > built-up land > water bodies > wetlands, with cropland converted to built-up land being the largest; ③ From 2000 to 2020, the landscape elements of Banan District were distributed in a balanced way, and the landscape types were rich and diversified, but due to the influence of human interference, it also presented the characteristics that the shape of the landscape elements tended to be irregular, and the dominant patches were distributed in a scattered manner, and the patches had poor connectivity. It is recommended that in future regional ecological construction, the layout should be rationally optimized, the relationship between landscape components should be coordinated, the connectivity between landscape patches should be strengthened, and the degree of landscape fragmentation should be reduced.

Keywords: land use transfer, landscape pattern evolution, GIS and Fragstats, Banan district

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Authors: Sylvain Amailland, Jean-Hugh Thomas, Charles Pézerat, Romuald Boucheron, Jean-Claude Pascal

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The noise requirements for naval and research vessels have seen an increasing demand for quieter ships in order to fulfil current regulations and to reduce the effects on marine life. Hence, new methods dedicated to the characterization of propeller noise, which is the main source of noise in the far-field, are needed. The study of cavitating propellers in closed-section is interesting for analyzing hydrodynamic performance but could involve significant difficulties for hydroacoustic study, especially due to reverberation and boundary layer noise in the tunnel. The aim of this paper is to present a numerical methodology for the identification of hydroacoustic sources on marine propellers using hydrophone arrays in a large hydrodynamic tunnel. The main difficulties are linked to the reverberation of the tunnel and the boundary layer noise that strongly reduce the signal-to-noise ratio. In this paper it is proposed to estimate the reflection coefficients using an inverse method and some reference transfer functions measured in the tunnel. This approach allows to reduce the uncertainties of the propagation model used in the inverse problem. In order to reduce the boundary layer noise, a cleaning algorithm taking advantage of the low rank and sparse structure of the cross-spectrum matrices of the acoustic and the boundary layer noise is presented. This approach allows to recover the acoustic signal even well under the boundary layer noise. The improvement brought by this method is visible on acoustic maps resulting from beamforming and DAMAS algorithms.

Keywords: acoustic imaging, boundary layer noise denoising, inverse problems, model adaptation

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Authors: Ming Wen, Nasim Nezamoddini

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Sophisticated numerical simulations like finite element analysis (FEA) involve a complicated process from model setup to post-processing tasks that require replication of time-consuming steps. Utilizing FEA automation program simplifies the complexity of the involved steps while minimizing human errors in analysis set up, calculations, and results processing. One of the main challenges in designing FEA automation programs is to identify user requirements and link them to possible design alternatives. This paper presents a decision-making framework to design a Python based FEA automation program for modal analysis, frequency response analysis, and random vibration fatigue (RVF) analysis procedures. Analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) are applied to evaluate design alternatives considering the feedback received from experts and program users.

Keywords: finite element analysis, FEA, random vibration fatigue, process automation, analytical hierarchy process, AHP, TOPSIS, multiple-criteria decision-making, MCDM

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Authors: Walid A. M. Ghoneim, Hamdy A. Ashour, Asmaa E. Abdo

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In this paper, linear analysis of a Switched Reluctance Generator (SRG) model is applied on the most common configurations (4/2, 6/4 and 8/6) for both conventional short-pitched and fully-pitched designs, in order to determine the optimum stator/rotor pole angles at which the maximum output voltage is generated per unit excitation current. This study is focused on SRG analysis and design as a proposed solution for renewable energy applications, such as wind energy conversion systems. The world’s potential to develop the renewable energy technologies through dedicated scientific researches was the motive behind this study due to its positive impact on economy and environment. In addition, the problem of rare earth metals (Permanent magnet) caused by mining limitations, banned export by top producers and environment restrictions leads to the unavailability of materials used for rotating machines manufacturing. This challenge gave authors the opportunity to study, analyze and determine the optimum design of the SRG that has the benefit to be free from permanent magnets, rotor windings, with flexible control system and compatible with any application that requires variable-speed operation. In addition, SRG has been proved to be very efficient and reliable in both low-speed or high-speed applications. Linear analysis was performed using MATLAB simulations based on the (Modified generalized matrix approach) of Switched Reluctance Machine (SRM). About 90 different pole angles combinations and excitation patterns were simulated through this study, and the optimum output results for each case were recorded and presented in detail. This procedure has been proved to be applicable for any SRG configuration, dimension and excitation pattern. The delivered results of this study provide evidence for using the 4-phase 8/6 fully pitched SRG as the main optimum configuration for the same machine dimensions at the same angular speed.

Keywords: generalized matrix approach, linear analysis, renewable applications, switched reluctance generator

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Authors: Dehni Abdellatif, Lounis Mourad

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The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil.

Keywords: geostatistical modelling, landsat, brightness temperature, conductivity

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Authors: Przemysław Adamczyk, Mirosław Wyczesany, Aleksandra Domagalik, Artur Daren, Kamil Cepuch, Piotr Błądziński, Tadeusz Marek, Andrzej Cechnicki

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The present study aimed at evaluation of neural correlates of humor comprehension impairments observed in schizophrenia. To investigate the nature of this deficit in schizophrenia and to localize cortical areas involved in humor processing we used functional magnetic resonance imaging (fMRI). The study included chronic schizophrenia outpatients (SCH; n=20), and sex, age and education level matched healthy controls (n=20). The task consisted of 60 stories (setup) of which 20 had funny, 20 nonsensical and 20 neutral (not funny) punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible (yes/no) and how funny it was (1-9 Likert-type scale). fMRI was performed on a 3T scanner (Magnetom Skyra, Siemens) using 32-channel head coil. Three contrasts in accordance with the three stages of humor processing were analyzed in both groups: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution; funny vs neutral - elaboration. Additionally, parametric modulation analysis was performed using both subjective ratings separately in order to further differentiate the areas involved in incongruity resolution processing. Statistical analysis for behavioral data used U Mann-Whitney test and Bonferroni’s correction, fMRI data analysis utilized whole-brain voxel-wise t-tests with 10-voxel extent threshold and with Family Wise Error (FWE) correction at alpha = 0.05, or uncorrected at alpha = 0.001. Between group comparisons revealed that the SCH subjects had attenuated activation in: the right superior temporal gyrus in case of irresolvable incongruity processing of nonsensical puns (nonsensical > neutral); the left medial frontal gyrus in case of incongruity resolution processing of funny puns (funny > nonsensical) and the interhemispheric ACC in case of elaboration of funny puns (funny > neutral). Additionally, the SCH group revealed weaker activation during funniness ratings in the left ventro-medial prefrontal cortex, the medial frontal gyrus, the angular and the supramarginal gyrus, and the right temporal pole. In comprehension ratings the SCH group showed suppressed activity in the left superior and medial frontal gyri. Interestingly, these differences were accompanied by protraction of time in both types of rating responses in the SCH group, a lower level of comprehension for funny punchlines and a higher funniness for absurd punchlines. Presented results indicate that, in comparison to healthy controls, schizophrenia is characterized by difficulties in humor processing revealed by longer reaction times, impairments of understanding jokes and finding nonsensical punchlines more funny. This is accompanied by attenuated brain activations, especially in the left fronto-parietal and the right temporal cortices. Disturbances of the humor processing seem to be impaired at the all three stages of the humor comprehension process, from incongruity detection, through its resolution to elaboration. The neural correlates revealed diminished neural activity of the schizophrenia brain, as compared with the control group. The study was supported by the National Science Centre, Poland (grant no 2014/13/B/HS6/03091).

Keywords: communication skills, functional magnetic resonance imaging, humor, schizophrenia

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Authors: Enver Kucukkulahli, Pakize Erdogmus, Kemal Polat

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The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software.

Keywords: image segmentation, clustering, GUI, 2D MRI

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