Search results for: images processing

Authors: M. Ahmadi, M. Kafil, H. Ebrahimi

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Nowadays, induction motors have a significant role in industries. Condition monitoring (CM) of this equipment has gained a remarkable importance during recent years due to huge production losses, substantial imposed costs and increases in vulnerability, risk, and uncertainty levels. Motor current signature analysis (MCSA) is one of the most important techniques in CM. This method can be used for rotor broken bars detection. Signal processing methods such as Fast Fourier transformation (FFT), Wavelet transformation and Empirical Mode Decomposition (EMD) are used for analyzing MCSA output data. In this study, these signal processing methods are used for broken bar problem detection of Mobarakeh steel company induction motors. Based on wavelet transformation method, an index for fault detection, CF, is introduced which is the variation of maximum to the mean of wavelet transformation coefficients. We find that, in the broken bar condition, the amount of CF factor is greater than the healthy condition. Based on EMD method, the energy of intrinsic mode functions (IMF) is calculated and finds that when motor bars become broken the energy of IMFs increases.

Keywords: broken bar, condition monitoring, diagnostics, empirical mode decomposition, fourier transform, wavelet transform

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Authors: Adegbite Bashiru Adeniyi, Olaniyi Akeem Olawale, Ayobamidele Sinatu Juliet

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The study was carried out in the 6 South Western states of Nigeria to analyze socio-economic characteristic of garri processors and their anthropometric qualities with respect to modern technologies used in garri processing. About 20 respondents were randomly selected from each of the 6 workstations purposively considered for the study due to their daily processing activities already attracted high patronage of customers. These include Oguntolu village (Ogun State), Igoba-Akure (Ondo State), Imo-Ilesa (Osun State), Odo Oba-Ileri (Oyo State), Irasa village (Ekiti State) and Epe in Lagos state. Interview schedule was conducted for 120 respondents to elicit information. Data were analyzed using descriptive statistical tools. It was observed from the findings that respondents were in their most productive age range (36-45 years) except Ogun state where majority (45%) were relatively older than 45 years. A fewer processors were much younger than 26 years old. It furthers revealed that not less than 55% have body weight greater than 50.0 kilogram, also not less than 70% were taller than 1.5 meter. So also, the hand length and hand thickness of the majority were long and bulky which are considered suitable for operating some modern and improved technologies in garri-frying process. This information could be used by various technological developers to enhance production of modern equipment and tools for a greater efficiency.

Keywords: agro-business, anthromorphic, modernization, proficiency

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Authors: Andrey V. Timofeev

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The practical efficient approach is suggested to estimate the high-speed objects instant bounds in C-OTDR monitoring systems. In case of super-dynamic objects (trains, cars) is difficult to obtain the adequate estimate of the instantaneous object localization because of estimation lag. In other words, reliable estimation coordinates of monitored object requires taking some time for data observation collection by means of C-OTDR system, and only if the required sample volume will be collected the final decision could be issued. But it is contrary to requirements of many real applications. For example, in rail traffic management systems we need to get data off the dynamic objects localization in real time. The way to solve this problem is to use the set of statistical independent parameters of C-OTDR signals for obtaining the most reliable solution in real time. The parameters of this type we can call as 'signaling parameters' (SP). There are several the SP’s which carry information about dynamic objects instant localization for each of C-OTDR channels. The problem is that some of these parameters are very sensitive to dynamics of seismoacoustic emission sources but are non-stable. On the other hand, in case the SP is very stable it becomes insensitive as a rule. This report contains describing the method for SP’s co-processing which is designed to get the most effective dynamic objects localization estimates in the C-OTDR monitoring system framework.

Keywords: C-OTDR-system, co-processing of signaling parameters, high-speed objects localization, multichannel monitoring systems

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Authors: Umar Shehu Umar

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Contract farming is a viable strategy agribusinesses rely on to strengthen vertical coordination. However, low contract compliance remains a significant setback to agribusinesses' contract performance. The present study aims to understand what drives smallholder farmers’ contract compliance behaviour. Qualitative information was collected through Focus Group Discussions to enrich the design of the survey questionnaire administered on a sample of 300 randomly selected farmers contracted by the Dangote Tomato Processing Plant (DTPP) in four regions of northern Nigeria. Novel transaction level data of tomato sales covering one season were collected in addition to socio-economic information of the sampled farmers. Binary logistic model results revealed that open fresh market tomato prices and payment delays negatively affect farmers' compliance behaviour while quantity harvested, education level and input provision correlated positively with compliance. The study suggests that contract compliance will increase if contracting firms devise a reliable and timely payment plan (e.g., digital payment), continue input and service provisions (e.g., improved seeds, extension services) and incentives (e.g., loyalty rewards, bonuses) in the contract.

Keywords: contract farming, compliance, farmers and processors., smallholder

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Authors: Sirajdin Olagoke Adeshina, Haidi Ibrahim, Akeem Salawu

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large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM.

Keywords: automatic attendance, face detection, haar-like cascade, manual attendance

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Authors: Kartik Thakore, Anna-Roza Tamas, Adam Cole

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The world health organization (WHO) reports that by 2030 more than 23.7 million deaths annually will be caused by Cardiovascular Diseases (CVDs); with a 2008 economic impact of $3.76 T. Metabolic syndrome is a disorder of multiple metabolic risk factors strongly indicated in the development of cardiovascular diseases. Guided lifestyle intervention driven by live coaching has been shown to have a positive impact on metabolic risk factors. Individuals’ path to improved (decreased) metabolic risk factors are driven by personal motivation and personalized messages delivered by coaches and augmented by technology. Using interactions captured between 400 individuals and 3 coaches over a program period of 500 days, a preliminary model was designed. A novel real time event tracking system was created to track and classify clients based on their genetic profile, baseline questionnaires and usage of a mobile application with live coaching sessions. Classification of clients and coaches was done using a support vector machines application build on Apache Spark, Stanford Natural Language Processing Library (SNLPL) and decision-modeling.

Keywords: guided lifestyle intervention, metabolic risk factors, personal coaching, support vector machines application, Apache Spark, natural language processing

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Authors: Magdeline Pakeng Mohlala, R. L. Molatudi, M. A. Mofokeng

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Cowpea (Vigna unguiculata (L.) Walp) is an important annual leguminous crop in semi-arid and tropics. Most of cowpea grain production in South Africa is mainly used for domestic consumption, as seed planting and little or none gets to be used in industrial processing; thus, there is a need to expand the utilization of cowpea through industrial processing. Agronomic traits contribute to the understanding of the association between yield and its component traits to facilitate effective selection for yield improvement. The aim of this study was to evaluate cowpea genotypes using grain yield and canning quality traits. The field experiment was conducted in two locations in Limpopo Province, namely Syferkuil Agricultural Experimental farm and Ga-Molepo village during 2017/2018 growing season and canning took place at ARC-Grain Crops Potchefstroom. The experiment comprised of 100 cowpea genotypes laid out in a Randomized Complete Block Designs (RCBD). The grain yield, yield components, and canning quality traits were analysed using Genstat software. About 62 genotypes were suitable for canning, 38 were not due to their seed coat texture, and water uptake was less than 80% resulting in too soft (mushy) seeds. Grain yield for RV115, 99k-494-6, ITOOK1263, RV111, RV353 and 53 other genotypes recorded high positive association with number of branches, pods per plant, and number of seeds per pod, unshelled weight and shelled weight for Syferkuil than at Ga-Molepo are therefore recommended for canning quality.

Keywords: agronomic traits, canning quality, genotypes, yield

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Authors: Erzsebet Gulyas

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Our inner world, our imagination, and our way of thinking are invisible and inaudible to others, but they influence our behavior. To investigate the relationship between thinking and language use, we created a test in Hungarian using ideas from the literature. The test prompts participants to make decisions based on visual images derived from the written information presented. There is a correlation (r=0.5) between the test result and the self-assessment of the visual imagery vividness and the visual and verbal components of internal representations measured by self-report questionnaires, as well as with responses to language-use inquiries in the background questionnaire. 56 university students completed the tests, and SPSS was used to analyze the data.

Keywords: imagination, internal representations, verbalization, visualization

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Authors: R. Kumaravelu, S. Poornima, Sunil Kumar Kashyap

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The optimized and digitalized restoration of the information from the old and used manual jacquard punched card in textile industry is referred to as Jacquard Punch Card (JPC) reader. In this paper, we present a novel design and development of photo electronics based system for reading old and used punched cards and storing its binary information for transforming them into an effective image file format. In our textile industry the jacquard punched cards holes diameters having the sizes of 3mm, 5mm and 5.5mm pitch. Before the adaptation of computing systems in the field of textile industry those punched cards were prepared manually without digital design source, but those punched cards are having rich woven designs. Now, the idea is to retrieve binary information from the jacquard punched cards and store them in digital (Non-Graphics) format before processing it. After processing the digital format (Non-Graphics) it is converted into an effective image file format through either by Row major or Column major parsing technique.To accomplish these activities, an embedded system based device and software integration is developed. As part of the test and trial activity the device was tested and installed for industrial service at Weavers Service Centre, Kanchipuram, Tamilnadu in India.

Keywords: file system, SPI. UART, ARM controller, jacquard, punched card, photo LED, photo diode

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Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, P. L. Goh, Grace H. B. Foo, M. L. Leong

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This paper presents the evaluation of various soil testing methods such as the four-probe soil electrical resistivity method and cone penetration test (CPT) that can complement a newly developed novel rapid soil classification scheme using computer vision, to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from the local construction industry are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labor-intensive. Thus, a rapid classification method is needed at the SGs. Four-probe soil electrical resistivity and CPT were evaluated for their feasibility as suitable additions to the computer vision system to further develop this innovative non-destructive and instantaneous classification method. The computer vision technique comprises soil image acquisition using an industrial-grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the following three items were targeted to be added onto the computer vision scheme: the apparent electrical resistivity of soil (ρ) measured using a set of four probes arranged in Wenner’s array, the soil strength measured using a modified mini cone penetrometer, and w measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay,” and a mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay” and are feasible as complementing methods to the computer vision system.

Keywords: computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: Ying Liang, Na Li

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Lipo-alkaloid is a kind of C19-norditerpenoid alkaloids existed in Aconitum species, which usually contains an aconitane skeleton and one or two fatty acid residues. The structures are very similar to that of diester-type alkaloids, which are considered as the main bioactive components in Aconitum carmichaelii. They have anti-inflammatory, anti-nociceptive, and anti-proliferative activities. So far, more than 200 lipo-alkaloids were reported from plants, semisynthesis, and biotransformations. In our research, by the combination of ultra-high performance liquid chromatography-quadruple-time of flight mass spectrometry (UHPLC-Q-TOF-MS) and an in-house database, 148 lipo-alkaloids were identified from A. carmichaelii, including 93 potential new compounds and 38 compounds with oxygenated fatty acid moieties. To our knowledge, this is the first time of the reporting of the oxygenated fatty acids as the side chains in naturally-occurring lipo-alkaloids. Considering the fatty acid residues in lipo-alkaloids should come from the free acids in the plant, the fatty acids and their relationship with lipo-alkaloids were further investigated by GC-MS and LC-MS. Among 17 fatty acids identified by GC-MS, 12 were detected as the side chains of lipo-alkaloids, which accounted for about 1/3 of total lipo-alkaloids, while these fatty acid residues were less than 1/4 of total fatty acid residues. And, total of 37 fatty acids were determined by UHPCL-Q-TOF-MS, including 18 oxidized fatty acids firstly identified from A. carmichaelii. These fatty acids were observed as the side chains of lipo-alkaloids. In addition, although over 140 lipo-alkaloids were identified, six lipo-alkaloids, 8-O-linoleoyl-14-benzoylmesaconine (1), 8-O-linoleoyl-14-benzoylaconine (2), 8-O-palmitoyl-14-benzoylmesaconine (3), 8-O-oleoyl-14-benzoylmesaconine (4), 8-O-pal-benzoylaconine (5), and 8-O-ole-Benzoylaconine (6), were found to be the main components, which accounted for over 90% content of total lipo-alkaloids. Therefore, using these six components as standards, a UHPLC-Triple Quadrupole-MS (UHPLC-QQQ-MS) approach was established to investigate the influence of processing on the contents of lipo-alkaloids. Although it was commonly supposed that the contents of lipo-alkaloids increased after processing, our research showed that no significant change was observed before and after processing. Using the same methods, the lipo-alkaloids in the lateral roots of A. carmichaelii and the roots of A. kusnezoffii were determined and quantified. The contents of lipo-alkaloids in A. kusnezoffii were close to that of the parent roots of A. carmichaelii, while the lateral roots had less lipo-alkaloids than the parent roots. This work was supported by Macao Science and Technology Development Fund (086/2013/A3 and 003/2016/A1).

Keywords: Aconitum carmichaelii, fatty acids, GC-MS, LC-MS, lipo-alkaloids

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Pedro Cardoso Alves, Ana Lucia Galinkin, José Carlos Ribeiro

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Political participation, can be a somewhat tricky subject to define, not in small part due to the constant changes in the concept fruit of the effort to include new forms of participatory behavior that go beyond traditional institutional channels. With the advent of the internet and mobile technologies, defining political participation has become an even more complicated endeavor, given de amplitude of politicized behaviors that are expressed throughout these mediums, be it in the very organization of social movements, in the propagation of politicized texts, videos and images, or in the micropolitical behaviors that are expressed in daily interaction. In fact, the very frontiers that delimit physical and digital spaces have become ever more diluted due to technological advancements, leading to a hybrid existence that is simultaneously physical and digital, not limited, as it once was, to the temporal limitations of classic communications. Moving away from those institutionalized actions of traditional political behavior, an idea of constant and fluid participation, which occurs in our daily lives through conversations, posts, tweets and other digital forms of expression, is discussed. This discussion focuses on the factors that precede more direct forms of political participation, interpreting the relation between informational habits, ideology, and political efficacy. Though some of the informational habits can be considered political participation, by some authors, a distinction is made to establish a logical flow of behaviors leading to participation, that is, one must gather and process information before acting on it. To reach this objective, a quantitative survey is currently being applied in Brazilian social media, evaluating feelings of political efficacy, social and economic issue-based ideological stances and informational habits pertaining to collection, fact-checking, and diversity of sources and ideological positions present in the participant’s political information network. The measure being used for informational habits relies strongly on a mix of information literacy and political sophistication concepts, bringing a more up-to-date understanding of information and knowledge production and processing in contemporary hybrid (physical-digital) environments. Though data is still being collected, preliminary analysis point towards a strong correlation between information habits and political efficacy, while ideology shows a weaker influence over efficacy. Moreover, social ideology and economic ideology seem to have a strong correlation in the sample, such intermingling between social and economic ideals is generally considered a red flag for political polarization.

Keywords: political efficacy, ideology, information literacy, cyberpolitics

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Authors: Vathsala Kaluarachchi, Oshani Wimalarathna, Charith Vandebona, Gayani Chandrarathna, Lakmal Rupasinghe, Windhya Rankothge

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It is the fundamental function of a monitoring system to allow users to collect and process data. A worldwide threat, the corona outbreak has wreaked havoc in Sri Lanka, and the situation has gotten out of hand. Since the epidemic, the Sri Lankan government has been unable to establish a systematic system for monitoring corona patients and providing emergency care in the event of an outbreak. Most patients have been held at home because of the high number of patients reported in the nation, but they do not yet have access to a functioning medical system. It has resulted in an increase in the number of patients who have been left untreated because of a lack of medical care. The absence of competent medical monitoring is the biggest cause of mortality for many people nowadays, according to our survey. As a result, a smartphone app for analyzing the patient's state and determining whether they should be hospitalized will be developed. Using the data supplied, we are aiming to send an alarm letter or SMS to the hospital once the system recognizes them. Since we know what those patients need and when they need it, we will put up a desktop program at the hospital to monitor their progress. Deep learning, image processing and application development, natural language processing, and blockchain management are some of the components of the research solution. The purpose of this research paper is to introduce a mechanism to connect hospitals and patients even when they are physically apart. Further data security and user-friendliness are enhanced through blockchain and NLP.

Keywords: blockchain, deep learning, NLP, monitoring system

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Authors: Priyanka Bharti

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Decades ago designs were based on common sense and tradition, but after an enhancement in visualization technology and research, we are now able to comprehend the cognitive ability involved in the decoding of the visual information. However, many fields in visuals need intense research to deliver an efficient explanation for the events. Visuals are an information representation mode through images, symbols and graphics. It plays an impactful role in decision making by facilitating quick recognition, comprehension, and analysis of a situation. They enhance problem-solving capabilities by enabling the processing of more data without overloading the decision maker. As research proves that, visuals offer an improved learning environment by a factor of 400 compared to textual information. Visual information engages learners at a cognitive level and triggers the imagination, which enables the user to process the information faster (visuals are processed 60,000 times faster in the brain than text). Appropriate information, visualization, and its presentation are known to aid and intensify the decision-making process for the users. However, most literature discusses the role of visual aids in comprehension and decision making during normal conditions alone. Unlike emergencies, in a normal situation (e.g. our day to day life) users are neither exposed to stringent time constraints nor face the anxiety of survival and have sufficient time to evaluate various alternatives before making any decision. An emergency is an unexpected probably fatal real-life situation which may inflict serious ramifications on both human life and material possessions unless corrective measures are taken instantly. The situation demands the exposed user to negotiate in a dynamic and unstable scenario in the absence or lack of any preparation, but still, take swift and appropriate decisions to save life/lives or possessions. But the resulting stress and anxiety restricts cue sampling, decreases vigilance, reduces the capacity of working memory, causes premature closure in evaluating alternative options, and results in task shedding. Limited time, uncertainty, high stakes and vague goals negatively affect cognitive abilities to take appropriate decisions. More so, theory of natural decision making by experts has been understood with far more depth than that of an ordinary user. Therefore, in this study, the author aims to understand the role of visual aids in supporting rapid comprehension to take appropriate decisions during an emergency situation.

Keywords: cognition, visual, decision making, graphics, recognition

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Authors: Maria Zinnia Bardas Hoffmann

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Contrastive Analysis (CA) is the systematic comparison between two languages. It stems from the notion that errors are caused by interference of the L1 system in the acquisition process of an L2. CA represents a useful tool to understand the nature of learning and acquisition. Also, this particular method promises a path to un-derstand the nature of underlying cognitive processes, even when other factors such as intrinsic motivation and teaching strategies were found to best explain student’s problems in acquisition. CA study is justified not only from the need to get a deeper understanding of the nature of SLA, but as an invaluable source to provide clues, at a cognitive level, for those general processes involved in rule formation and abstract thought. It is relevant for cross disciplinary studies and the fields of Computational Thought, Natural Language processing, Applied Linguistics, Cognitive Linguistics and Math Theory. That being said, this paper intends to address here as well its own set of constraints and limitations. Finally, this paper: (a) aims at identifying some of the difficulties students may find in their learning process due to the nature of their specific variety of L1, Rio de la Plata Spanish (RPS), (b) represents an attempt to discuss the necessity for specific models to approach CA.

Keywords: second language acquisition, applied linguistics, contrastive analysis, applied contrastive analysis English language department, meta-linguistic rules, cross-linguistics studies, computational thought, natural language processing

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Authors: Lakshay Kharbanda, Aabhas Chauhan

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In this paper, an application of Normalized Compression Distance (NCD) to detect notable scene alterations occurring in videos is presented. Several research groups have been developing methods to perform image classification using NCD, a computable approximation to Normalized Information Distance (NID) by studying the degree of similarity in images. The timeframes where significant aberrations between the frames of a video have occurred have been identified by obtaining a threshold NCD value, using two compressors: LZMA and BZIP2 and defining scene alterations using Pixel Difference Percentage metrics.

Keywords: image compression, Kolmogorov complexity, normalized compression distance, root mean square error

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Authors: Ezil Sam Leni, Shalen S.

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Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes.

Keywords: federated Learning, pothole detection, distributed framework, federated averaging

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Authors: Hai L. Tran

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When covering events and issues, the news media often employ both personal accounts as well as facts and figures. However, the process of using numbers and narratives in the newsroom is mostly operated through trial and error. There is a demonstrated need for the news industry to better understand the specific effects of storytelling and data-driven reporting on the audience as well as explanatory factors driving such effects. In the academic world, anecdotal evidence and statistical evidence have been studied in a mutually exclusive manner. Existing research tends to treat pertinent effects as though the use of one form precludes the other and as if a tradeoff is required. Meanwhile, narratives and statistical facts are often combined in various communication contexts, especially in news presentations. There is value in reconceptualizing and theorizing about both relative and collective impacts of numbers and narratives as well as the mechanism underlying such effects. The current undertaking seeks to link theory to practice by providing a complete picture of how and why people are influenced by information conveyed through quantitative and qualitative accounts. Specifically, the cognitive-experiential theory is invoked to argue that humans employ two distinct systems to process information. The rational system requires the processing of logical evidence effortful analytical cognitions, which are affect-free. Meanwhile, the experiential system is intuitive, rapid, automatic, and holistic, thereby demanding minimum cognitive resources and relating to the experience of affect. In certain situations, one system might dominate the other, but rational and experiential modes of processing operations in parallel and at the same time. As such, anecdotes and quantified facts impact audience response differently and a combination of data and narratives is more effective than either form of evidence. In addition, the present study identifies several media variables and human factors driving the effects of statistics and anecdotes. An integrative model is proposed to explain how message characteristics (modality, vividness, salience, congruency, position) and individual differences (involvement, numeracy skills, cognitive resources, cultural orientation) impact selective exposure, which in turn activates pertinent modes of processing, and thereby induces corresponding responses. The present study represents a step toward bridging theoretical frameworks from various disciplines to better understand the specific effects and the conditions under which the use of anecdotal evidence and/or statistical evidence enhances or undermines information processing. In addition to theoretical contributions, this research helps inform news professionals about the benefits and pitfalls of incorporating quantitative and qualitative accounts in reporting. It proposes a typology of possible scenarios and appropriate strategies for journalists to use when presenting news with anecdotes and numbers.

Keywords: data, narrative, number, anecdote, storytelling, news

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Authors: Katarzyna Pasternak

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Experiencing the meaning of life is widely recognised as a vital element of well-being and central human motivation. Studies have shown that a higher meaning of life is associated, among other things, with a higher quality of life, higher levels of happiness and better declared health. The subject of the study is the meaning in life measured with The Meaning in Life Questionnaire and the presence of such emotions as nostalgia, awe and hope, and the content of imaginations measured after temporal mental simulations in Americans and Poles. The respondents had to imagine themselves in future, in 40 years and describe two events that would take place at that time. Next, participants assessed the importance of the events described by them, recognised whether during their journey through time they felt awe, hope and nostalgia, and answered the questionnaire examining the meaning in life. 204 (102 from Poland 102 from the USA ) people aged 21 to 60 participated in the study. The study checked whether there were differences in the content of the imaginations of the respondents from Poland and USA, and whether there were statistically significant difference between the declared sense of meaning in life among participants from both countries. The result of the study hane shown that there were no differences in the overall result obtained by the participants in The Meaning in Life Questionnaire , while there were statistically significant differences among the subscales of the questionnaire. It turned out that Americans have a higher presence of meaning in life than Poles, but they obtained lower results in searching of meaning in life. Studies have also shown that there was a statistically significant difference between Poles and Americans in feeling awe after a mental simulation. Poles felt higher level of awe. Images about the future differed between Poles and Americans. Poles judged that the events they described were very important to them. Interestingly, the content of American participants’ imaginations was dominated by topics related to the future of the world, ecology and world peace. There were also ideas about nice moments spent with friends and family. Among Poles, ideas related to professional career and development as well as family events dominated. Research shows that despite the lack of differences in the general meaning in life, Poles are more focused on searching for meaning in life than Americans. The study shows interesting differences between the two cultures.

Keywords: meaning in life, mental simulations, imaginations, temporal mental simulations, future, cultural differences

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Authors: Rui Sousa, Aurora Futuro, António Fiúza

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The exploitation of gold ore deposits is highly dependent on efficient mineral processing methods, although actual perspectives based on life-cycle assessment introduce difficulties that were unforeseen in a very recent past. Cyanidation is the most applied gold processing method, but the potential environmental problems derived from the usage of cyanide as leaching reagent led to a demand for alternative methods. Ammoniacal thiosulfate leaching is one of the most important alternatives to cyanidation. In this article, some experimental studies carried out in order to assess the feasibility of thiosulfate as a leaching agent for the ore from the unexploited Portuguese gold mine of Castromil. It became clear that the process depends on the concentrations of ammonia, thiosulfate and copper. Based on this fact, a few leaching tests were performed in order to assess the best reagent prescription, and also the effects of different combination of these concentrations. Higher thiosulfate concentrations cause the decrease of gold dissolution. Lower concentrations of ammonia require higher thiosulfate concentrations, and higher ammonia concentrations require lower thiosulfate concentrations. The addition of copper increases the gold dissolution ratio. Subsequently, some alternative operatory conditions were tested such as variations in temperature and in the solid/liquid ratio as well as the application of a pre-treatment before the leaching stage. Finally, thiosulfate leaching was compared to cyanidation. Thiosulfate leaching showed to be an important alternative, although a pre-treatment is required to increase the yield of the gold dissolution.

Keywords: gold, leaching, pre-treatment, thiosulfate

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Authors: Giovanna Borges Bottino, Luis Felipe Freitas do Nascimento Alves Teixeira

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This article aims to present a case study of the National Civil Aviation Agency (ANAC) in Brazil, ANAC-id. ANAC-id is the artificial intelligence algorithm developed for image analysis that recognizes standard images of unobstructed and uprighted face without sunglasses, allowing to identify potential inconsistencies. It combines YOLO architecture and 3 libraries in python - face recognition, face comparison, and deep face, providing robust analysis with high level of accuracy.

Keywords: artificial intelligence, deepface, face compare, face recognition, YOLO, computer vision

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Authors: Niyongabo Elyse

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Prefabricated building construction is a pioneering approach that combines design, production, and assembly to attain energy efficiency, environmental sustainability, and economic feasibility. Despite continuous development in the industry in China, the low technical maturity of standardized design, factory production, and construction assembly introduces uncertainties affecting prefabricated component production and on-site assembly processes. This research focuses on enhancing project management performance under uncertainty to help enterprises navigate these challenges and optimize project resources. The study introduces a perspective on how uncertain factors influence the implementation of prefabricated building construction projects. It proposes a theoretical model considering project process management ability, adaptability to uncertain environments, and collaboration ability of project participants. The impact of uncertain factors is demonstrated through case studies and quantitative analysis, revealing constraints on implementation time, cost, quality, and safety. To address uncertainties in prefabricated component production scheduling, a fuzzy model is presented, expressing processing times in interval values. The model utilizes a cooperative co-evolution evolution algorithm (CCEA) to optimize scheduling, demonstrated through a real case study showcasing reduced project duration and minimized effects of processing time disturbances. Additionally, the research addresses on-site assembly construction scheduling, considering the relationship between task processing times and assigned resources. A multi-objective model with fuzzy activity durations is proposed, employing a hybrid cooperative co-evolution evolution algorithm (HCCEA) to optimize project scheduling. Results from real case studies indicate improved project performance in terms of duration, cost, and resilience to processing time delays and resource changes. The study also introduces a multistage dynamic process control model, utilizing IoT technology for real-time monitoring during component production and construction assembly. This approach dynamically adjusts schedules when constraints arise, leading to enhanced project management performance, as demonstrated in a real prefabricated housing project. Key contributions include a fuzzy prefabricated components production scheduling model, a multi-objective multi-mode resource-constrained construction project scheduling model with fuzzy activity durations, a multi-stage dynamic process control model, and a cooperative co-evolution evolution algorithm. The integrated mathematical model addresses the complexity of prefabricated building construction project management, providing a theoretical foundation for practical decision-making in the field.

Keywords: prefabricated construction, project management performance, uncertainty, fuzzy scheduling

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Authors: Naruemon Phongaksorn

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The biofuel consumption has increased significantly over the decade resulting in the increasing request on agricultural land for biofuel feedstocks. However, the biofuel feedstocks are already stressed of having low productivity owing to inappropriate agricultural practices without considering suitability of crop land. This research evaluates the land suitability using GIS-integrated Analytic Hierarchy Processing (AHP) of biofuel crops: cassava, at Chachoengsao province, in Thailand. AHP method that has been widely accepted for land use planning. The objective of this study is compared between AHP method and the most limiting group of land characteristics method (classical approach). The reliable results of the land evaluation were tested against the crop performance assessed by the field investigation in 2015. In addition to the socio-economic land suitability, the expected availability of raw materials for biofuel production to meet the local biofuel demand, are also estimated. The results showed that the AHP could classify and map the physical land suitability with 10% higher overall accuracy than the classical approach. The Chachoengsao province showed high and moderate socio-economic land suitability for cassava. Conditions in the Chachoengsao province were also favorable for cassava plantation, as the expected raw material needed to support ethanol production matched that of ethanol plant capacity of this province. The GIS integrated AHP for biofuel crops land suitability evaluation appears to be a practical way of sustainably meeting biofuel production demand.

Keywords: Analytic Hierarchy Processing (AHP), Cassava, Geographic Information Systems, Land suitability

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Authors: Aliakbar Alijarahi, Ali Khaleghi, Azadehe Afrasiyabi

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One of the emerging technologies in the field of education that can be effectively profitable, called augmented reality, where the combination of real world and virtual images in real time produces new concepts that can facilitate learning. The paper, providing an introduction to the general concept of augmented reality, aims at surveying its capabitities in different areas, with an emphasis on Education, It seems quite necessary to have comparative study on virtual/e-learning and augmented reality and conclude their differences in education methods. As an review article, the paper is composed, instead of producing new concepts, to sum-up and analayze accomplished works related to the subject.

Keywords: augmented reality, education, virtual learning, e-learning

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Authors: Raj Mukta Sundaram

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The location of Ayodhya is distinctly described along river Sarayu in the epic Ramayan. On the contrary, even elaborate descriptions of Lanka and its environs are still proving elusive to human ingenuity to find a direct correlation on the ground. His-torically, there were hardly any attempts to locate Lanka, but some speculations have been made very recently, of which Sri Lanka has gained widespread public ac-ceptance for obvious reasons, such as Sri and Lanka. This belief is almost secured by the impression of Ram Setu on the satellite images, which has led the government to initiate a scientific mission to determine its age. In fact, other viewpoints believe Lanka to be somewhere far-flung along the equator, and another has long proclaimed it to be in central regions of India, but both are diminished by contemporary belief. This study emanates from the fact that Sri Lanka has no correlation to epic, and more importantly, satellite images are deceptive. So the objectives are twofold - firstly, to interpret the text from a holistic approach by analyzing the ecosystem, settlements, geological as-pects, and most importantly, the timeline of key events. Secondly, it explains the pit-falls in the rationale behind contemporary belief. At the outset, it categorically rejects the notion of Ram Setu, which, in geological terms, is merely a part of the continental shelf developed millions of years ago. It also refutes the misconception created by the word “Sri,” which is, in fact, an official name adopted by the country in the seventies with no correlation whatsoever with the events of Ramayana. Likewise, the study ar-gues for the establishment of a prosperous kingdom on a remote island with adverse climatic conditions for any civilization at that time. Eventually, the study demonstrates that travel time for the distances covered by Lord Rama does not corroborate with the description in the epic. It all leads to one conclusion that Lanka cannot be in Sri Lanka. Rather, it needs to be somewhere in the central-eastern parts of India. That region jus-tifies the environs and timelines for the journeys undertaken by Lord Rama, besides the fact that the tribes of the region show strong allegiance to Ravana. The study strongly recommends looking into the central-east region of India for the golden abode of a demon king and rejuvenating tourism of a scenic and culturally rich region hitherto marred by disturbances.

Keywords: spatial analysis, Ramayan, heritage, tourism

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Authors: Amirhosein Rostampour, Mehdi Sharif

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In this article, a series of high impact polystyrene/graphene (HIPS/Gr) nanocomposites were prepared by solution mixing method and their morphology and dynamic mechanical properties were investigated as a function of graphene content. SEM images and X-Ray diffraction data confirm that the graphene platelets are well dispersed in HIPS matrix for the nanocomposites with Gr contents up to 5.0 wt%. Mechanical properties analysis demonstrates that yielding strength and initial modulus of HIPS/Gr nanocomposites are highly improved with the increment of Gr content compared to pure HIPS.

Keywords: nanocomposite, graphene, dynamic mechanical properties, morphology

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: David Sinfield, Thomas Cochrane, Marcos Steagall

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While there is much hype around the potential and development of mobile virtual reality (VR), the two key critical success factors are the ease of user experience and the development of a simple user-generated content ecosystem. Educational technology history is littered with the debris of over-hyped revolutionary new technologies that failed to gain mainstream adoption or were quickly superseded. Examples include 3D television, interactive CDROMs, Second Life, and Google Glasses. However, we argue that this is the result of curriculum design that substitutes new technologies into pre-existing pedagogical strategies that are focused upon teacher-delivered content rather than exploring new pedagogical strategies that enable student-determined learning or heutagogy. Visual Communication design based learning such as Graphic Design, Illustration, Photography and Design process is heavily based on the traditional forms of the classroom environment whereby student interaction takes place both at peer level and indeed teacher based feedback. In doing so, this makes for a healthy creative learning environment, but does raise other issue in terms of student to teacher learning ratios and reduced contact time. Such issues arise when students are away from the classroom and cannot interact with their peers and teachers and thus we see a decline in creative work from the student. Using AR and VR as a means of stimulating the students and to think beyond the limitation of the studio based classroom this paper will discuss the outcomes of a student project considering the virtual classroom and the techniques involved. The Atelier learning environment is especially suited to the Visual Communication model as it deals with the creative processing of ideas that needs to be shared in a collaborative manner. This has proven to have been a successful model over the years, in the traditional form of design education, but has more recently seen a shift in thinking as we move into a more digital model of learning and indeed away from the classical classroom structure. This study focuses on the outcomes of a student design project that employed Augmented Reality and Virtual Reality technologies in order to expand the dimensions of the classroom beyond its physical limits. Augmented Reality when integrated into the learning experience can improve the learning motivation and engagement of students. This paper will outline some of the processes used and the findings from the semester-long project that took place.

Keywords: augmented reality, blogging, design in community, enhanced learning and teaching, graphic design, new technologies, virtual reality, visual communications

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