Search results for: ABC-VED inventory classification

Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Thinakar Mani Balusamy, Ratnakar S. Kini, Bharat Narasimhan, Venkateswaran A. R, Pugazhendi Thangavelu, Mohammed Ali, Prem Kumar K., Kani Sheikh M., Sibi Thooran Karmegam, Radhakrishnan N., Mohammed Noufal

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Introduction: Pancreatic injury remains a complicated condition requiring an individualized case by case approach to management. In this study, we aim to analyze the varied presentations and treatment outcomes of traumatic pancreatic injury in a tertiary care center. Methods: All consecutive patients hospitalized at our center with traumatic pancreatic injury between 2013 and 2017 were included. The American Association for Surgery of Trauma (AAST) classification was used to stratify patients into five grades of severity. Outcome parameters were then analyzed based on the treatment modality employed. Results: Of the 35 patients analyzed, 26 had an underlying blunt trauma with the remaining nine presenting due to penetrating injury. Overall in-hospital mortality was 28%. 19 of these patients underwent exploratory laparotomy with the remaining 16 managed nonoperatively. Nine patients had a severe injury ( > grade 3) – of which four underwent endotherapy, three had stents placed and one underwent an endoscopic pseudocyst drainage. Among those managed nonoperatively, three underwent a radiological drainage procedure. Conclusion: Mortality rates were clearly higher in patients managed operatively. This is likely a result of significantly higher degrees of major associated non-pancreatic injuries and not just a reflection of surgical morbidity. Despite this, surgical management remains the mainstay of therapy, especially in higher grades of pancreatic injury. However we would like to emphasize that endoscopic intervention definitely remains the preferred treatment modality when the clinical setting permits. This is especially applicable in cases of main pancreatic duct injury with ascites as well as pseudocysts.

Keywords: endotherapy, non-operative management, surgery, traumatic pancreatic injury

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Authors: Akintunde Alo

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The incessant encroachments into the forest ecosystem by the farmers and local contractors constitute a major threat to the conservation of genetic resources and biodiversity in Nigeria. To propose a viable monitoring system, this study employed Geographic Information System (GIS) technology to assess the changes that occurred for a period of five years (between 2011 and 2016) in Ikere forest reserve. Landsat imagery of the forest reserve was obtained. For the purpose of geo-referencing the acquired satellite imagery, ground-truth coordinates of some benchmark places within the forest reserve was relied on. Supervised classification algorithm, image processing, vectorization and map production were realized using ArcGIS. Various land use systems within the forest ecosystem were digitized into polygons of different types and colours for 2011 and 2016, roads were represented with lines of different thickness and colours. Of the six land-use delineated, the grassland increased from 26.50 % in 2011 to 45.53% in 2016 of the total land area with a percentage change of 71.81 %. Plantations of Gmelina arborea and Tectona grandis on the other hand reduced from 62.16 % in 2011 to 27.41% in 2016. The farmland and degraded land recorded percentage change of about 176.80 % and 8.70 % respectively from 2011 to 2016. Overall, the rate of deforestation in the study area is on the increase and becoming severe. About 72.59% of the total land area has been converted to non-forestry uses while the remnant 27.41% is occupied by plantations of Gmelina arborea and Tectona grandis. Interestingly, over 55 % of the plantation area in 2011 has changed to grassland, or converted to farmland and degraded land in 2016. The rate of change over time was about 9.79 % annually. Based on the results, rapid actions to prevail on the encroachers to stop deforestation and encouraged re-afforestation in the study area are recommended.

Keywords: land use change, forest reserve, satellite imagery, geographical information system

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Authors: O. Onoriode Emoyan, E. Eyitemi Akporhonor, Charles Otobrise

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Polycyclic Aromatic Hydrocarbons (PAHs) are formed mainly as a result of incomplete combustion of organic materials during industrial, domestic activities or natural occurrence. Their toxicity and contamination of terrestrial and aquatic ecosystem have been established. Though with limited validity index, previous research has focused on PAHs isomer pair ratios of variable physicochemical properties in source identification. The objective of this investigation was to determine the empirical validity of Pearson correlation coefficient (PCC) and cluster analysis (CA) in PAHs source identification along soil samples of different land uses. Therefore, 16 PAHs grouped as endocrine disruption substances (EDSs) were determined in 10 sample stations in top and sub soils seasonally. PAHs was determined the use of Varian 300 gas chromatograph interfaced with flame ionization detector. Instruments and reagents used are of standard and chromatographic grades respectively. PCC and CA results showed that the classification of PAHs along kinetically and thermodyanamically-favoured and those derived directly from plants product through biologically mediated processes used in source signature is about the predominance PAHs are likely to be. Therefore the observed PAHs in the studied stations have trace quantities of the vast majority of the sixteen un-substituted PAHs which may ultimately inhabit the actual source signature authentication. Type and extent of bacterial metabolism, transformation products/substrates, and environmental factors such as: salinity, pH, oxygen concentration, nutrients, light intensity, temperature, co-substrates and environmental medium are hereby recommended as factors to be considered when evaluating possible sources of PAHs.

Keywords: comparative correlation, kinetically and thermodynamically-favored PAHs, pearson correlation coefficient, cluster analysis, sources evaluation

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Authors: Chahrazed Zouaoui

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In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Sweeti, Anoop Kant Godiyal, Neha Singh, Sneh Anand, B. K. Panigrahi, Jayasree Santhosh

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This work is about developing a tool for visualization and segmentation of Electroencephalograph (EEG) signals based on frequency domain features. Change in the frequency domain characteristics are correlated with change in mental state of the subject under study. Proposed algorithm provides a way to represent the change in the mental states using the different frequency band powers in form of segmented EEG signal. Many segmentation algorithms have been suggested in literature having application in brain computer interface, epilepsy and cognition studies that have been used for data classification. But the proposed method focusses mainly on the better presentation of signal and that’s why it could be a good utilization tool for clinician. Algorithm performs the basic filtering using band pass and notch filters in the range of 0.1-45 Hz. Advanced filtering is then performed by principal component analysis and wavelet transform based de-noising method. Frequency domain features are used for segmentation; considering the fact that the spectrum power of different frequency bands describes the mental state of the subject. Two sliding windows are further used for segmentation; one provides the time scale and other assigns the segmentation rule. The segmented data is displayed second by second successively with different color codes. Segment’s length can be selected as per need of the objective. Proposed algorithm has been tested on the EEG data set obtained from University of California in San Diego’s online data repository. Proposed tool gives a better visualization of the signal in form of segmented epochs of desired length representing the power spectrum variation in data. The algorithm is designed in such a way that it takes the data points with respect to the sampling frequency for each time frame and so it can be improved to use in real time visualization with desired epoch length.

Keywords: de-noising, multi-channel data, PCA, power spectra, segmentation

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Authors: Álvaro de Pedro Múñez, Óscar García García, Tania Álvarez Yates, Virginia Serrano Gómez

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This study aimed to analyze professional basketball player´s neuromuscular behaviour. The main goal was to describe the neuromuscular performance of elite male basketball players and to analyze the validity and reliability of different tests. The tests used were Squat Jump (SJ), Countermovement Free), and 5m, 10m, and 20m sprint tests. All these tests were carried out during the preseason. 100 professional basketball players participated in this study; we used 2 classification variables: performance level (Leb Gold, BBL, and BCL), as well as position (Bigs and Guards). The application of the Kolmogorov-Smirnov test, in conjunction with the Lilliefors test, showed that the sample distribution was normal, linear, and homoscedastic. The relative reliability analysis was carried out by calculating the Intraclass Correlation Index (ICC). We found all variables to have a high validity and reliability. The coefficient of variation (CV) was calculated for raw data and after log-transformed and used as an absolute reliability indicator. The intraclass correlation coefficients (ICC) and coefficient of variation (CV) for the various tests are the following. For the Countermovement Jump (CMJ), the right leg showed an ICC of 0.94 (CV: 7.8%), and the left leg had an ICC of 0.84 (CV: 11.2%). For the sprint tests, the 5m sprint demonstrated excellent reliability with an intraclass correlation coefficient (ICC) of 0.81 and a coefficient of variation (CV) of 3.2%. The 10m sprint exhibited an ICC of 0.91 and a CV of 1.0%, while the 20m sprint achieved the highest reliability with an ICC of 0.92 and a CV of 0.8%. Regarding jump performance, the Squat Jump (SJ) displayed an ICC of 0.96 with a CV of 2.8%, and the Countermovement Jump (CMJ) showed a slightly lower but still strong reliability with an ICC of 0.93 and a CV of 6.7%. Lastly, the "CMJ free" test exhibited an ICC of 0.97 (CV: 5.2%). The tests demonstrated high reliability, with ICC values ranging from 0.81 to 0.97. The 5m, 10m, and 20m sprints, as well as the CMJ and SJ tests, showed strong consistency, particularly the 10m and 20m sprints (ICC 0.91-0.92). Coefficients of variation were low, indicating precise and stable measurements suitable for performance assessment.

Keywords: neuromuscular performance, basketball players, validity and reliability, intraclass correlation coefficient, vertical jump, sprint tests

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Authors: Dana Alina Magdas, Nicoleta Simona Vedeanu, Ioana Feher, Rares Stiufiuc

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Food and beverages authentication using rapid and non-expensive analytical tools represents nowadays an important challenge. In this regard, the potential of vibrational techniques in food authentication has gained an increased attention during the last years. For wines discrimination, Raman spectroscopy appears more feasible to be used as compared with IR (infrared) spectroscopy, because of the relatively weak water bending mode in the vibrational spectroscopy fingerprint range. Despite this, the use of Raman technique in wine discrimination is in an early stage. Taking this into consideration, the wine discrimination potential of surface-enhanced Raman scattering (SERS) technique is reported in the present work. The novelty of this study, compared with the previously reported studies, concerning the application of vibrational techniques in wine discrimination consists in the fact that the present work presents the wines differentiation based on the individual signals obtained from deconvoluted spectra. In order to achieve wines classification with respect to variety, geographical origin and vintage, the peaks intensities obtained after spectra deconvolution were compared using supervised chemometric methods like Linear Discriminant Analysis (LDA). For this purpose, a set of 20 white Romanian wines from different viticultural Romanian regions four varieties, was considered. Chemometric methods applied directly to row SERS experimental spectra proved their efficiency, but discrimination markers identification found to be very difficult due to the overlapped signals as well as for the band shifts. By using this approach, a better general view related to the differences that appear among the wines in terms of compositional differentiation could be reached.

Keywords: chemometry, SERS, variety, wines discrimination

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Authors: Svetlana Cojocaru, Mircea Petic, Inga Titchiev

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Humanity faces more and more often with different social disasters, which in turn can generate new accidents and catastrophes. To mitigate their consequences, it is important to obtain early possible signals about the events which are or can occur and to prepare the corresponding scenarios that could be applied. Our research is focused on solving two problems in this domain: identifying signals related that an accident occurred or may occur and mitigation of some consequences of disasters. To solve the first problem, methods of selecting and processing texts from global network Internet are developed. Information in Romanian is of special interest for us. In order to obtain the mentioned tools, we should follow several steps, divided into preparatory stage and processing stage. Throughout the first stage, we manually collected over 724 news articles and classified them into 10 categories of social disasters. It constitutes more than 150 thousand words. Using this information, a controlled vocabulary of more than 300 keywords was elaborated, that will help in the process of classification and identification of the texts related to the field of social disasters. To solve the second problem, the formalism of Petri net has been used. We deal with the problem of inhabitants’ evacuation in useful time. The analysis methods such as reachability or coverability tree and invariants technique to determine dynamic properties of the modeled systems will be used. To perform a case study of properties of extended evacuation system by adding time, the analysis modules of PIPE such as Generalized Stochastic Petri Nets (GSPN) Analysis, Simulation, State Space Analysis, and Invariant Analysis have been used. These modules helped us to obtain the average number of persons situated in the rooms and the other quantitative properties and characteristics related to its dynamics.

Keywords: lexicon of disasters, modelling, Petri nets, text annotation, social disasters

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Shu-Ching Chen, Li-Yun Lee

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The purpose of the study was to identify the factors associated with head and neck cancer-related lymphoedema (HNCRL)-related symptoms, body image, and HNCRL-related functional outcomes among patients with resected advanced head and neck cancer. A cross-sectional correlational design was conducted to examine the predictors of HNCRL-related functional outcomes in patients with resected advanced head and neck cancer. Eligible patients were recruited from a single medical center in northern Taiwan. Consecutive patients were approached and recruited from the Radiation Head and Neck Outpatient Department of this medical center. Eligible subjects were assessed for the Symptom Distress Scale–Modified for Head and Neck Cancer (SDS-mhnc), Brief International Classification of Functioning, Disability and Health (ICF) Core Set for Head and Neck Cancer (BCSQ-H&N), Body Image Scale–Modified (BIS-m), The MD Anderson Head and Neck Lymphedema Rating Scale (MDAHNLRS), The Foldi’s Stages of Lymphedema (Foldi’s Scale), Patterson’s Scale, UCLA Shoulder Rating Scale (UCLA SRS), and Karnofsky’s Performance Status Index (KPS). The results showed that the worst problems with body HNCRL functional outcomes. Patients’ HNCRL symptom distress and performance status are robust predictors across over for overall HNCRL functional outcomes, problems with body HNCRL functional outcomes, and activity and social functioning HNCRL functional outcomes. Based on the results of this period research program, we will develop a Cancer Rehabilitation and Lymphedema Care Program (CRLCP) to use in the care of patients with resected advanced head and neck cancer.

Keywords: head and neck cancer, resected, lymphedema, symptom, body image, functional outcome

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Authors: Munmunlisa Mohanty, K. D. Raju

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Rail industry is one of the model employers in India has separate national legislation (Railways Act 1989) to regulate its vast employment structure, functioning across the country. Indian Railway is not only the premier transport industry of the country; indeed, it is Asia’s most extensive rail network organisation and the world’s second-largest industry functioning under one management. With the growth of globalization of industrial products, the scope of anti-employment discrimination is no more confined to gender aspect only; instead, it extended to the unregularized classification of labour force applicable in the various industrial establishments in India. And the Indian Rail Industry inadvertently enhanced such discriminatory employment trends by engaging contractual labour in an unprecedented manner. The engagement of contractual labour by rail industry vanished the core “Employer-Employee” relationship between rail management and contractual labour who employed through the contractor. This employment trend reduces the cost of production and supervision, discourages the contractual labour from forming unions, and reduces its collective bargaining capacity. So, the primary intention of this paper is to highlight the increasing discriminatory employment scope for contractual labour engaged by Indian Railways. This paper critically analyses the diminishing perspective of anti-employment opportunity practiced by Indian Railways towards contractual labour and demands an urgent outlook on the probable scope of anti-employment discrimination against contractual labour engaged by Indian Railways. The researcher used doctrinal methodology where primary materials (Railways Act, Contract Labour Act and Occupational, health and Safety Code, 2020) and secondary data (CAG Report 2018, Railways Employment Regulation Rules, ILO Report etc.) are used for the paper.

Keywords: anti-employment, CAG Report, contractual labour, discrimination, Indian Railway, principal employer

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Authors: Fatemeh Parhizkar, Mojtaba Yamani, Abdolla Behboodi, Masoomeh Hashemi

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The mangrove forests are natural and valuable gifts that exist in some parts of the world, including Iran. Regarding the threats faced by these forests and the declining area of them all over the world, as well as in Iran, it is very necessary to manage and monitor them. The current study aimed to investigate the changes in mangrove forests and the relationship between these changes and the marine hydrodynamics and coastal morphology in the area between qeshm island and the west coast of the Hormozgan province (i.e. the coastline between Mehran river and Bandar-e Pol port) in the 49-year period. After preprocessing and classifying satellite images using the SVM, MLC, and ANN classifiers and evaluating the accuracy of the maps, the SVM approach with the highest accuracy (the Kappa coefficient of 0.97 and overall accuracy of 98) was selected for preparing the classification map of all images. The results indicate that from 1972 to 1987, the area of these forests have had experienced a declining trend, and in the next years, their expansion was initiated. These forests include the mangrove forests of Khurkhuran wetland, Muriz Deraz Estuary, Haft Baram Estuary, the mangrove forest in the south of the Laft Port, and the mangrove forests between the Tabl Pier, Maleki Village, and Gevarzin Village. The marine hydrodynamic and geomorphological characteristics of the region, such as average intertidal zone, sediment data, the freshwater inlet of Mehran river, wave stability and calmness, topography and slope, as well as mangrove conservation projects make the further expansion of mangrove forests in this area possible. By providing significant and up-to-date information on the development and decline of mangrove forests in different parts of the coast, this study can significantly contribute to taking measures for the conservation and restoration of mangrove forests.

Keywords: mangrove forests, marine hydrodynamics, coastal morphology, west of strait of Hormuz, Iran

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Authors: Sakinah Idris, Gabrine Jagersma, Bjorn Jaime Van Pelt, Kirstin Greaves-Lord

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Background: The PEERS (Program for the Education and Enrichment of Relational Skills) intervention can be considered a well-established, evidence-based intervention in the USA. However, testing the efficacy of cultural adaptations of PEERS is still ongoing. More and more, the involvement of all stakeholders in the development and evaluation of interventions is acknowledged as crucial for the longer term implementation of interventions across settings. Therefore, in the current project, teens with ASD (Autism Spectrum Disorder), their neurotypical peers, parents, teachers, as well as clinicians were involved in the development and evaluation of the Dutch version of PEERS. Objectives: The current presentation covers (1) the formative phase and (2) the preliminary adaptation test phase of the cultural adaptation of evidence-based interventions. In the formative phase, we aim to describe the process of adaptation of the PEERS program to the Dutch culture and care system. In the preliminary adaptation phase, we will present results from the preliminary adaptation test among 32 adolescents with ASD. Methods: In phase 1, a group discussion on common vocabulary was conducted among 70 teenagers (and their teachers) from special and regular education aged 12-18 years old. This inventory concerned 14 key constructs from PEERS, e.g., areas of interests, locations for making friends, common peer groups and crowds inside and outside of school, activities with friends, commonly used ways for electronic communication, ways for handling disagreements, and common teasing comebacks. Also, 15 clinicians were involved in the translation and cultural adaptation process. The translation and cultural adaptation process were guided by the research team, and who included input and feedback from all stakeholders through an iterative feedback incorporation procedure. In phase 2, The parent-reported Social Responsiveness Scale (SRS), the Test of Adolescent Social Skills Knowledge (TASSK), and the Quality of Socialization Questionnaire (QSQ) were assessed pre- and post-intervention to evaluate potential treatment outcome. Results: The most striking cultural adaptation - reflecting the standpoints of all stakeholders - concerned the strategies for handling rumors and gossip, which were suggested to be taught using a similar approach as the teasing comebacks, more in line with ‘down-to-earth’ Dutch standards. The preliminary testing of this adapted version indicated that the adolescents with ASD significantly improved their social knowledge (TASSK; t₃₁ = -10.9, p < .01), social experience (QSQ-Parent; t₃₁ = -4.2, p < .01 and QSQ-Adolescent; t₃₂ = -3.8, p < .01), and in parent-reported social responsiveness (SRS; t₃₃ = 3.9, p < .01). In addition, subjective evaluations of teens with ASD, their parents and clinicians were positive. Conclusions: In order to further scrutinize the effectiveness of the Dutch version of the PEERS intervention, we recommended performing a larger scale randomized control trial (RCT) design, for which we provide several methodological considerations.

Keywords: cultural adaptation, PEERS, preliminary testing, translation

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Authors: Hasan Frookh Jamal

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This is a case of a 36-year-old Bahraini gentleman diagnosed to have Cushing's Syndrome with a large anterior mediastinal mass. He was sent abroad to the Speciality hospital in Jordan, where he underwent diagnostic video-assisted thoracoscopy, partial thymectomy and pericardial fat excision. Histopathology of the mass was reported to be an Atypical carcinoid tumor with a low Ki67 proliferation index of 5%, the mitotic activity of 4 MF/10HPF and pathological stage classification(pTNM): pT1aN1. MRI of the pituitary gland showed an ill-defined non-enhancing focus of about 3mm on the Rt side of the pituitary on coronal images, with a similar but smaller one on the left side, which could be due to enhancing pattern rather than a real lesion as reported. The patient underwent Ga68 Dotate PET/CT scan post-operatively, which showed multiple somatostatin receptor-positive lesions seen within the tail, body and head of the pancreas and positive somatostatin receptor lymph nodes located between the pancreatic head and IVC. There was no uptake detected at the anterior mediastinum nor at the site of thymic mass resection. There was no evidence of any positive somatostatin uptake at the soft tissue or lymph nodes. The patient underwent IPSS, which proved that the source is, in fact, an ectopic source of ACTH secretion. Unfortunately, the patient's serum cortisol remained elevated after surgery and failed to be suppressed by 1 mg ODST and by 2 days LLDST with a high ACTH value. The patient was started on Osilodrostat for treatment of hypercortisolism for the time being and his future treatment plan with Lutetium-177 Dotate therapy vs. bilateral adrenalectomy is to be considered in an MDT meeting.

Keywords: cushing syndrome, neuroendocrine tumur, carcinoid tumor, Thymoma

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Authors: Chun-Min Lin, Yuh-Jen Wu, Ching-Ting Chung

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The formulation of tourism policies requires objective academic research and evidence as support, especially research from local academia. Taiwan is a small island, and its economic growth relies heavily on tourism revenue. Taiwanese government has been devoting to the promotion of the tourism industry over the past few decades. Scientific research outcomes by Taiwanese scholars may and will help lay the foundations for drafting future tourism policy by the government. In this study, a total of 120 full journal articles published between 2008 and 2016 from the Journal of Tourism and Leisure Studies (JTSL) were examined to explore the scientific research trend of tourism study in Taiwan. JTSL is one of the most important Taiwanese journals in the tourism discipline which focuses on tourism-related issues and uses traditional Chinese as the study language. The method of co-word analysis from bibliometrics approaches was employed for semantic analysis in this study. When analyzing Chinese words and phrases, word segmentation analysis is a crucial step. It must be carried out initially and precisely in order to obtain meaningful word or word chunks for further frequency calculation. A word segmentation system basing on N-gram algorithm was developed in this study to conduct semantic analysis, and 100 groups of meaningful phrases with the highest recurrent rates were located. Subsequently, co-word analysis was employed for semantic classification. The results showed that the themes of tourism research in Taiwan in recent years cover the scope of tourism education, environmental protection, hotel management, information technology, and senior tourism. The results can give insight on the related issues and serve as a reference for tourism-related policy making and follow-up research.

Keywords: bibliometrics, co-word analysis, word segmentation, tourism research, policy

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Authors: Shubham Arora

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This paper illustrates a relationship between the Dvaitādvaita (dualistic non-dualistic) doctrine of Nimbārka school of Vedānta and philosophy of Indian classical dance, through the Rasa theory. There would be a separate focus on the philosophies of both the disciplines and then analyzing Rasa theory as a connexion between them. The paper presents ideas regarding the similarity between the Brahman and the dancer, manifestation of enacting character and the Jīva (soul), the existence of the phenomenal world and the imaginary world classification of rasa on the basis of three modes of nature, and the feelings and expressions depicting the Dvaita and Advaita. The reason behind choosing such a topic is an intention to explore the relativity of the Vedantic philosophy of this school in real manner. It is really important to study the practical implications and relevance of the doctrine with other disciplines for perceiving it cogently. In our daily lives, we use various forms of facial expressions and bodily gestures in order to communicate, along with the oral and written means of communication. What if, when gestures and expressions mingle with the music beats, in order to present an idea? Indian Classical dance is highly rich in expressing the emotions using extraordinary expressions, unconventional bodily gestures and mesmerizing music beats. Ancient scriptures like Nāṭyaśāstra of Bharata Muni and Abhinava Bhārati by Abhinavaguptā recount aesthetics in a well-defined and structured way of acting and dancing and also reveal the grammar of rasa theory. Indian Classical dance is not only for entertainment but it is deeply in contact with divinity. During the period of Bhakti movement in India, this art form was used as a means to narrate the vignettes from epics like Rāmāyana and Mahābhārata and Purānas. Even in present era, this art has a deep rooted philosophy within.

Keywords: Advaita, Brahman, Dvaita, Jiva, Nimbarka, Rasa, Vedanta

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Authors: Maimuna Akram, Khadija Zia, Sohaib Naseer

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Pedagogical objectives and the nature of the course content may lead instructors to take varied approaches to selecting a student for the cold call, specifically in a studio setup where students work on different projects independently and show progress work time to time at scheduled critiques. Cold-calling often proves to be an effective tool in eliciting a response without enforcing judgment onto the recipients. While there is a mixed range of behavior exhibited by students who are cold-called, a classification of responses from anxiety-provoking to inspiring may be elicited; there is a need for a greater understanding of utilizing the exchanges in bringing about fruitful and engaging outcomes of studio discussions. This study aims to unravel the dimensions of utilizing the cold-call approach in a didactic exchange within studio pedagogy. A questionnaire survey was conducted in an undergraduate class at Arts and Design School. The impact of cold calling on students’ participation was determined through various parameters, including course choice, participation frequency, students’ comfortability, and teaching methodology. After analyzing the surveys, specific classroom teachers were interviewed to provide a qualitative perspective of the faculty. It was concluded that cold-calling increases students’ participation frequency and also increases preparation for class. Around 67% of students responded that teaching methods play an important role in learning activities and students’ participation during class discussions. 84% of participants agreed that cold calling is an effective way of learning. According to research, cold-calling can be done in large numbers without making students uncomfortable. As a result, the findings of this study support the use of this instructional method to encourage more students to participate in class discussions.

Keywords: active learning, class discussion, class participation, cold calling, pedagogical methods, student engagement

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Authors: V. Veeraprathap, G. S. Harish, G. Narendra Kumar

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Uncontrolled growth of abnormal cells in the lung in the form of tumor can be either benign (non-cancerous) or malignant (cancerous). Patients with Lung Cancer (LC) have an average of five years life span expectancy provided diagnosis, detection and prediction, which reduces many treatment options to risk of invasive surgery increasing survival rate. Computed Tomography (CT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI) for earlier detection of cancer are common. Gaussian filter along with median filter used for smoothing and noise removal, Histogram Equalization (HE) for image enhancement gives the best results without inviting further opinions. Lung cavities are extracted and the background portion other than two lung cavities is completely removed with right and left lungs segmented separately. Region properties measurements area, perimeter, diameter, centroid and eccentricity measured for the tumor segmented image, while texture is characterized by Gray-Level Co-occurrence Matrix (GLCM) functions, feature extraction provides Region of Interest (ROI) given as input to classifier. Two levels of classifications, K-Nearest Neighbor (KNN) is used for determining patient condition as normal or abnormal, while Artificial Neural Networks (ANN) is used for identifying the cancer stage is employed. Discrete Wavelet Transform (DWT) algorithm is used for the main feature extraction leading to best efficiency. The developed technology finds encouraging results for real time information and on line detection for future research.

Keywords: artificial neural networks, ANN, discrete wavelet transform, DWT, gray-level co-occurrence matrix, GLCM, k-nearest neighbor, KNN, region of interest, ROI

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Authors: Marc Toutain, Pascale Leconte, Antoine Gauthier

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Introduction: Eating Disorders (ED), such as anorexia, bulimia, and binge eating, are psychiatric illnesses that mostly affect young people. The main symptoms concern eating (restriction, excessive food intake) and weight control behaviors (laxatives, vomiting). Psychological comorbidities (depression, executive function disorders, etc.) and problematic behaviors toward physical activity (PA) are commonly associated with ED. Acquaintances on ED risk factors are still lacking, and more community sample studies are needed to improve prevention and early detection. To our knowledge, studies are needed to specifically investigate the link between ED risk level, PA, and psychological risk factors in a community sample of adolescents. The aim of this study is to assess the relation between ED risk level, exercise (type, frequency, and motivations for engaging in exercise), and psychological factors based on the Jacobi risk factors model. We suppose that a high risk of ED will be associated with the practice of high caloric cost PA, motivations oriented to weight and shape control, and psychological disturbances. Method: An online survey destined for students has been sent to several middle schools and colleges in northwest France. This survey combined several questionnaires, the Eating Attitude Test-26 assessing ED risk; the Exercise Motivation Inventory–2 assessing motivations toward PA; the Hospital Anxiety and Depression Scale assessing anxiety and depression, the Contour Drawing Rating Scale; and the Body Esteem Scale assessing body dissatisfaction, Rosenberg Self-esteem Scale assessing self-esteem, the Exercise Dependence Scale-Revised assessing PA dependence, the Multidimensional Assessment of Interoceptive Awareness assessing interoceptive awareness and the Frost Multidimensional Perfectionism Scale assessing perfectionism. Machine learning analysis will be performed in order to constitute groups with a tree-based model clustering method, extract risk profile(s) with a bootstrap method comparison, and predict ED risk with a prediction method based on a decision tree-based model. Expected results: 1044 complete records have already been collected, and the survey will be closed at the end of May 2022. Records will be analyzed with a clustering method and a bootstrap method in order to reveal risk profile(s). Furthermore, a predictive tree decision method will be done to extract an accurate predictive model of ED risk. This analysis will confirm typical main risk factors and will give more data on presumed strong risk factors such as exercise motivations and interoceptive deficit. Furthermore, it will enlighten particular risk profiles with a strong level of proof and greatly contribute to improving the early detection of ED and contribute to a better understanding of ED risk factors.

Keywords: eating disorders, risk factors, physical activity, machine learning

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Authors: Chittana Phompila

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The current increased population pushes increasing demands for natural resources and living space. In Laos, urban areas have been expanding rapidly in recent years. The rapid urbanization can have negative impacts on landscapes, including forest and agriculture lands. The primary objective of this research were to map current urban areas in a large city in Savannakhet province, in Laos, 2) to compare changes in urbanization between 1990 and 2018, and 3) to estimate forest and agriculture areas lost due to expansions of urban areas during the last over twenty years within study area. Landsat 8 data was used and existing GIS data was collected including spatial data on rivers, lakes, roads, vegetated areas and other land use/land covers). GIS data was obtained from the government sectors. Object based classification (OBC) approach was applied in ECognition for image processing and analysis of urban area using. Historical data from other Landsat instruments (Landsat 5 and 7) were used to allow us comparing changes in urbanization in 1990, 2000, 2010 and 2018 in this study area. Only three main land cover classes were focused and classified, namely forest, agriculture and urban areas. Change detection approach was applied to illustrate changes in built-up areas in these periods. Our study shows that the overall accuracy of map was 95% assessed, kappa~ 0.8. It is found that that there is an ineffective control over forest and land-use conversions from forests and agriculture to urban areas in many main cities across the province. A large area of agriculture and forest has been decreased due to this conversion. Uncontrolled urban expansion and inappropriate land use planning can lead to creating a pressure in our resource utilisation. As consequence, it can lead to food insecurity and national economic downturn in a long term.

Keywords: urbanisation, forest cover, agriculture areas, Landsat 8 imagery

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Authors: Ali Alsawayeh, Ibrahim Eldanfour

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This paper examines the extent of disclosure of oil and gas reserve quantum in annual reports of international oil and gas exploration and production companies, particularly companies in untested international markets, such as Canada, the UK and the US, and seeks to determine the underlying factors that affect the level of disclosure on oil reserve quantum. The study is concerned with the usefulness of disclosure of oil and gas reserves quantum to investors and other users. Given the primacy of the annual report (10-k) as a source of supplemental reserves data about the company and as the channel through which companies disseminate information about their performance, the annual reports for one year (2009) were the central focus of the study. This comparative study seeks to establish whether differences exist between the sample companies, based on new disclosure requirements by the Securities and Exchange Commission (SEC) in respect of reserves classification and definition. The extent of disclosure of reserve is provided and compared among the selected companies. Statistical analysis is performed to determine whether any differences exist in the extent of disclosure of reserve under the determinant variables. This study shows that some factors would affect the extent of disclosure of reserve quantum in the above-mentioned countries, namely: company’s size, leverage and quality of auditor. Companies that provide reserves quantum in detail appear to display higher size. The findings also show that the level of leverage has affected companies’ reserves quantum disclosure. Indeed, companies that provide detailed reserves quantum disclosure tend to employ a ‘high-quality auditor’. In addition, the study found significant independent variable such as Profit Sharing Contracts (PSC). This factor could explain variations in the level of disclosure of oil reserve quantum between the contractor and host governments. The implementation of SEC oil and gas reporting requirements do not enhance companies’ valuation because the new rules are based only on past and present reserves information (proven reserves); hence, future valuation of oil and gas companies is missing for the market.

Keywords: comparison, company characteristics, disclosure, reserve quantum, regulation

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Authors: David de Oliveira Costa, Miguel Ângelo Lellis Moreira, Carlos Francisco Simões Gomes, Daniel Augusto de Moura Pereira, Marcos dos Santos

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Strategic management is a fundamental process for global companies that intend to remain competitive in an increasingly dynamic and complex market. To do so, it is necessary to maintain alignment with their principles and values. The Balanced Scorecard (BSC) proposes to ensure that the overall business performance is based on different perspectives (financial, customer, internal processes, and learning and growth). However, relying solely on the BSC may not be enough to ensure the success of strategic management. It is essential that companies also evaluate and prioritize strategic projects that need to be implemented to ensure they are aligned with the business vision and contribute to achieving established goals and objectives. In this context, the proposition involves the incorporation of the SAPEVO-M multicriteria method to indicate the degree of relevance between different perspectives. Thus, the strategic objectives linked to these perspectives have greater weight in the classification of structural projects. Additionally, it is proposed to apply the concept of the Impact & Probability Matrix (I&PM) to structure and ensure that strategic projects are evaluated according to their relevance and impact on the business. By structuring the business's strategic management in this way, alignment and prioritization of projects and actions related to strategic planning are ensured. This ensures that resources are directed towards the most relevant and impactful initiatives. Therefore, the objective of this article is to present the proposal for integrating the BSC methodology, the SAPEVO-M multicriteria method, and the prioritization matrix to establish a concrete weighting of strategic planning and obtain coherence in defining strategic projects aligned with the business vision. This ensures a robust decision-making support process.

Keywords: MCDA process, prioritization problematic, corporate strategy, multicriteria method

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Authors: Kehabtimer Shiferaw Kotiso, Getachew Hailemariam, Abiy Seifu Estifanos

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Introduction: Despite the highest share of low birth weight (LBW) for neonatal mortality and morbidity, predicting births with LBW for better intervention preparation is challenging. This study aims to predict LBW using a dataset encompassing 53,637 birth cohorts collected from 36 primary hospitals across seven regions in Ethiopia from February 2022 to June 2024. Methods: We identified ten explanatory variables related to maternal and neonatal characteristics, including maternal education, age, residence, history of miscarriage or abortion, history of preterm birth, type of pregnancy, number of livebirths, number of stillbirths, antenatal care frequency, and sex of the fetus to predict LBW. Using WEKA 3.8.2, we developed and compared seven machine learning algorithms. Data preprocessing included handling missing values, outlier detection, and ensuring data integrity in birth weight records. Model performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic curve (ROC AUC) using 10-fold cross-validation. Results: The results demonstrated that the decision tree, J48, logistic regression, and gradient boosted trees model achieved the highest accuracy (94.5% to 94.6%) with a precision of 93.1% to 93.3%, F1-score of 92.7% to 93.1%, and ROC AUC of 71.8% to 76.6%. Conclusion: This study demonstrates the effectiveness of machine learning models in predicting LBW. The high accuracy and recall rates achieved indicate that these models can serve as valuable tools for healthcare policymakers and providers in identifying at-risk newborns and implementing timely interventions to achieve the sustainable developmental goal (SDG) related to neonatal mortality.

Keywords: low birth weight, machine learning, classification, neonatal mortality, Ethiopia

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: Deniz Ünal Özpalamutcu, Burcu Altman

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Actuaria uses mathematics, statistic and financial information when analyzing the financial impacts of uncertainties, risks, insurance and pension related issues. In other words, it deals with the likelihood of potential risks, their financial impacts and especially the financial measures. Handling these measures require some long-term payment and investments. So, it is obvious it is inevitable to plan the periodic payments with equal time intervals considering also the changing value of money over time. These series of payment made specific intervals of time is called annuity or rant. In literature, rants are classified based on start and end dates, start times, payments times, payments amount or frequency. Classification of rants based on payment amounts changes based on the constant, descending or ascending payment methods. The literature about handling the annuity is very limited. Yet in a daily life, especially in today’s world where the economic issues gained a prominence, it is very crucial to use the variable annuity method in line with the demands of the customers. In this study, the types of annuities with flexible payment are discussed. In other words, we focus on calculating payment amount of a period by adding a certain percentage of previous period payment was studied. While studying this problem, formulas were created considering both start and end period payments for cash value and accumulated. Also increase of each period payment by r interest rate each period payments calculated with previous periods increases. And the problem of annuities (rants) of which each period payment increased with previous periods’ increase by r interest rate has been analyzed. Cash value and accumulated value calculation of this problem were studied separately based on the period start/end and their relations were expressed by formulas.

Keywords: actuaria, annuity, flexible payment, rant

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Authors: Ying Chuan Wang, Zhang Tao

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The aim of this article intends to explore the differences of well-being of employees in casino hotels before and after the Chinese government began to fight corruption. This researcher also attempted to find out the relationship between work pressure and well-being of employees in gambling workplaces before and after the Chinese government crackdowns the corruption. The category of well-being including life well-being, workplace well-being, and psychological well-being was included for analyzing well-being of employees in gaming workplaces. In addition, the psychological pressure classification was applied into this study and the Job Content Questionnaire (JCQ) would be adopted on investigating employees’ work pressure in terms of decision latitude, psychological demands, and workplace support. This study is a quantitative approach research and was conducted in March 2017. A purposive sampling was used in this study. A total of valid 339 responses were collected and the participants were casino hotel employees. The findings showed that decision latitude was significantly different prior to and after Chinese government crackdowns on corruption. Moreover, workplace support was strongly significantly related to employees’ well-being before Chinese government crackdowns. Decision latitude was strongly significantly related to employees’ well-being after Chinese government crackdowns. The findings suggest that employees’ work pressure affects their well being. In particular, because of workplace supports, it may alleviate employees’ work pressure and affect their perceptions of well-being but only prior to fighting the crackdowns. Importantly, decision latitude has become an essential factor affecting their well-being after the crackdown. It is finally hoped that the findings of this study provide suggestion to the managerial levels of hospitality industries. It is important to enhance employees’ decision latitude. Offering training courses to equip employees’ skills could be a possible way to reduce work pressure. In addition, establishing career path for the employees to pursuit is essential for their self-development and the improvement of well being. This would be crucial for casino hotels’ sustainable development and strengthening their competitiveness.

Keywords: well-being, work pressure, Casino hotels’ employees, gaming workplace

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Authors: R. Tamborrino, F. Rinaudo

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Digital technologies offer possibilities to effectively convert historical archives into instruments of knowledge able to provide a guide for the interpretation of historical phenomena. Digital conversion and management of those documents allow the possibility to add other sources in a unique and coherent model that permits the intersection of different data able to open new interpretations and understandings. Urban history uses, among other sources, the inventories that register human activities in a specific space (e.g. cadastres, censuses, etc.). The geographic localisation of that information inside cartographic supports allows for the comprehension and visualisation of specific relationships between different historical realities registering both the urban space and the peoples living there. These links that merge the different nature of data and documentation through a new organisation of the information can suggest a new interpretation of other related events. In all these kinds of analysis, the use of GIS platforms today represents the most appropriate answer. The design of the related databases is the key to realise the ad-hoc instrument to facilitate the analysis and the intersection of data of different origins. Moreover, GIS has become the digital platform where it is possible to add other kinds of data visualisation. This research deals with the industrial development of Turin at the beginning of the 20th century. A census of factories realized just prior to WWI provides the opportunity to test the potentialities of GIS platforms for the analysis of urban landscape modifications during the first industrial development of the town. The inventory includes data about location, activities, and people. GIS is shaped in a creative way linking different sources and digital systems aiming to create a new type of platform conceived as an interface integrating different kinds of data visualisation. The data processing allows linking this information to an urban space, and also visualising the growth of the city at that time. The sources, related to the urban landscape development in that period, are of a different nature. The emerging necessity to build, enlarge, modify and join different buildings to boost the industrial activities, according to their fast development, is recorded by different official permissions delivered by the municipality and now stored in the Historical Archive of the Municipality of Turin. Those documents, which are reports and drawings, contain numerous data on the buildings themselves, including the block where the plot is located, the district, and the people involved such as the owner, the investor, and the engineer or architect designing the industrial building. All these collected data offer the possibility to firstly re-build the process of change of the urban landscape by using GIS and 3D modelling technologies thanks to the access to the drawings (2D plans, sections and elevations) that show the previous and the planned situation. Furthermore, they access information for different queries of the linked dataset that could be useful for different research and targets such as economics, biographical, architectural, or demographical. By superimposing a layer of the present city, the past meets to the present-industrial heritage, and people meet urban history.

Keywords: digital urban history, census, digitalisation, GIS, modelling, digital humanities

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