Search results for: text classification

Authors: R. Gwynn, P. Panwalkar, K. Veravalli , M. Tofighi, R. Clement, A. Mofidi

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The aim of this study was to access the impact of Covid-19 and lockdown on the incidence, injury pattern, and treatment of tibial plateau fractures in a combined rural and urban population in wales. Methods: Retrospective study was performed to identify tibial plateau fractures in 15-month period of Covid-19 lockdown 15-month period immediately before lockdown. Patient demographics, injury mechanism, injury severity (based on Schatzker classification), and associated injuries, treatment methods, and outcome of fractures in the Covid-19 period was studied. Results: The incidence oftibial plateau fracture was 9 per 100000 during Covid-19, and 8.5 per 100000, and both were similar to previous studies. The average age was 52, and female to male ratio was 1:1 in both control and study group. High energy injury was seen in only 20% of the patients and 35% in the control groups (2=12, p<0025). 14% of the covid-19 population sustained other injuries as opposed 16% in the control group(2=0.09, p>0.95). Lower severity isolated lateral condyle fracturesinjury (Schatzker 1-3) were seen in 40% of fractures this was 60% in the control populations. Higher bicondylar and shaft fractures (Schatzker 5-6) were seen in 60% of the Covid-19 group and 35% in the control groups(2=7.8, p<0.02). Treatment mode was not impacted by Covid-19. The complication rate was low in spite of higher number of complex fractures and the impact of covid-19 pandemic. Conclusion: The associated injuries were similar in spite of a significantly lower mechanism of injury. There were unexpectedly worst tibial plateau fracture based Schatzker classification in the Covid-19 period as compared to the control groups. This was especially relevant for medial condyle and shaft fractures. This was postulated to be caused by reduction in bone density caused by lack of vitamin D and reduction in activity. The treatment mode and outcome was not impacted by the impact of Covid-19 on care for tibial plateau fractures.

Keywords: Covid-19, knee, tibial plateau fracture, trauma

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Authors: I. Gusti Agung Aditya Surya Wibawa, Andri Syafriya, Beiruny Syam

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Gumai Formation which acts as regional seal for Talang Akar Formation becomes one of the most prolific reservoir in South Sumatra Basin and the primary exploration target in this area. Marine conditions were eventually established during the continuation of transgression sequence leads an open marine facies deposition in Early Miocene. Marine clastic deposits where calcareous shales, claystone and siltstones interbedded with fine-grained calcareous and glauconitic sandstones are the domination of lithology which targeted as the hydrocarbon reservoir. All this time, the main objective of PetroChina’s exploration and production in Betara area is only from Lower Talang Akar Formation. Successful testing in some exploration wells which flowed gas & condensate from Gumai Formation, opened the opportunity to optimize new reservoir objective in Betara area. Limitation of conventional wireline logs data in Gumai interval is generating technical challenge in term of geological approach. A utilization of Gas While Drilling indicator initiated with the objective to determine the next Gumai reservoir candidate which capable to increase Jabung hydrocarbon discoveries. This paper describes how Gas While Drilling indicator is processed to generate potential and non-potential zone by cut-off analysis. Validation which performed by correlation and comparison with well logs, Drill Stem Test (DST), and Reservoir Performance Monitor (RPM) data succeed to observe Gumai reservoir in Betara Complex. After we integrated all of data, we are able to generate a Betara Complex potential map and overlaid with reservoir characterization distribution as a part of risk assessment in term of potential zone presence. Mud log utilization and geophysical data information successfully covered the geological challenges in this study.

Keywords: Gumai, gas while drilling, classification, reservoir, potential

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Authors: Lidija R. Jevrić, Sanja O. Podunavac-Kuzmanović, Strahinja Z. Kovačević

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Quantitative structure-retention relationship (QSRR) analysis was used to predict the chromatographic behavior of s-triazine derivatives by using theoretical descriptors computed from the chemical structure. Fundamental basis of the reported investigation is to relate molecular topological descriptors with chromatographic behavior of s-triazine derivatives obtained by reversed-phase (RP) thin layer chromatography (TLC) on silica gel impregnated with paraffin oil and applied ethanol-water (φ = 0.5-0.8; v/v). Retention parameter (RM0) of 14 investigated s-triazine derivatives was used as dependent variable while simple connectivity index different orders were used as independent variables. The best QSRR model for predicting RM0 value was obtained with simple third order connectivity index (3χ) in the second-degree polynomial equation. Numerical values of the correlation coefficient (r=0.915), Fisher's value (F=28.34) and root mean square error (RMSE = 0.36) indicate that model is statistically significant. In order to test the predictive power of the QSRR model leave-one-out cross-validation technique has been applied. The parameters of the internal cross-validation analysis (r2CV=0.79, r2adj=0.81, PRESS=1.89) reflect the high predictive ability of the generated model and it confirms that can be used to predict RM0 value. Multivariate classification technique, hierarchical cluster analysis (HCA), has been applied in order to group molecules according to their molecular connectivity indices. HCA is a descriptive statistical method and it is the most frequently used for important area of data processing such is classification. The HCA performed on simple molecular connectivity indices obtained from the 2D structure of investigated s-triazine compounds resulted in two main clusters in which compounds molecules were grouped according to the number of atoms in the molecule. This is in agreement with the fact that these descriptors were calculated on the basis of the number of atoms in the molecule of the investigated s-triazine derivatives.

Keywords: s-triazines, QSRR, chemometrics, chromatography, molecular descriptors

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Authors: L. Bruckert, A. Mansourzadeh

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The voice has a major influence on interpersonal communication in everyday life via the perception of pleasantness. The evolutionary perspective postulates that the mechanisms underlying the pleasantness judgments are universal adaptations that have evolved in the service of choosing a mate (through the process of sexual selection). From this point of view, the favorite voices would be those with more marked sexually dimorphic characteristics; for example, in men with lower voice pitch, pitch is the main criterion. On the other hand, one can postulate that the mechanisms involved are gradually established since childhood through exposure to the environment, and thus the prosodic elements could take precedence in everyday life communication as it conveys information about the speaker's attitude (willingness to communicate, interest toward the interlocutors). Our study focuses on voice pleasantness and its relationship with social impression formation, exploring both the spectral aspects (pitch, timbre) and the prosodic ones. In our study, we recorded the voices through two vocal corpus (five vowels and a reading text) of 25 French males speaking French and 25 Iranian males speaking Farsi. French listeners (40 male/40 female) listened to the French voices and made a judgment either on the voice's pleasantness or on the speaker (judgment about his intelligence, honesty, sociability). The regression analyses from our acoustic measures showed that the prosodic elements (for example, the intonation and the speech rate) are the most important criteria concerning pleasantness, whatever the corpus or the listener's gender. Moreover, the correlation analyses showed that the speakers with the voices judged as the most pleasant are considered the most intelligent, sociable, and honest. The voices in Farsi have been judged by 80 other French listeners (40 male/40 female), and we found the same effect of intonation concerning the judgment of pleasantness with the corpus «vowel» whereas with the corpus «text» the pitch is more important than the prosody. It may suggest that voice perception contains some elements invariant across culture/language, whereas others are influenced by the cultural/linguistic background of the listener. Shortly in the future, Iranian people will be asked to listen either to the French voices for half of them or to the Farsi voices for the other half and produce the same judgments as the French listeners. This experimental design could potentially make it possible to distinguish what is linked to culture and what is linked to language in the case of differences in voice perception.

Keywords: cross-cultural psychology, impression formation, pleasantness, voice perception

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Authors: Pranita Shivankar, Arun Suryawanshi, Prabodhachandra Deshmukh, S. V. C. Kameswara Rao

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Many undesirable significant changes in landscapes and the regions in the vicinity of historically important structures occur as impacts due to anthropogenic activities over a period of time. A better understanding of such influences using recently developed satellite remote sensing techniques helps in planning the strategies for minimizing the negative impacts on the existing environment. In 1982, a fossilized hominid skull cap was discovered at a site located along the northern bank of the east-west flowing river Narmada in the village Hathnora. Close to the same site, the presence of Late Acheulian and Middle Palaeolithic tools have been discovered in the immediately overlying pebbly gravel, suggesting that the ‘Narmada skull’ may be from the Middle Pleistocene age. The reviews of recently carried out research studies relevant to hominid remains all over the world from Late Acheulian and Middle Palaeolithic sites suggest succession and contemporaneity of cultures there, enhancing the importance of Hathnora as a rare precious site. In this context, the maximum likelihood classification using digital interpretation techniques was carried out for this study area using the satellite imagery from Landsat ETM+ for the year 2006 and Landsat TM (OLI and TIRS) for the year 2016. The overall accuracy of Land Use Land Cover (LULC) classification of 2016 imagery was around 77.27% based on ground truth data. The significant reduction in the main river course and agricultural activities and increase in the built-up area observed in remote sensing data analysis are undoubtedly the outcome of human encroachments in the vicinity of the eminent heritage site.

Keywords: cultural succession, digital interpretation, Hathnora, Homo Sapiens, Late Acheulian, Middle Palaeolithic

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Authors: M. Baratieri, M. Beccali, S. Corradino, B. Di Pietra, C. La Grassa, F. Monteleone, G. Morosinotto, G. Puglisi

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The energy renovation of existing buildings represents an important opportunity to increase the decarbonization and the sustainability of urban environments. In this context, the work carried out has the objective of demonstrating the technical and economic feasibility of an energy renovate of a public building destined for offices located on the island of Lampedusa in the Mediterranean Sea. By applying the Italian transpositions of European Directives 2010/31/EU and 2009/28/EC, the building has been renovated from the current energy requirements of 111.7 kWh/m² to 16.4 kWh/m². The result achieved classifies the building as nZEB (nearly Zero Energy Building) according to the Italian national definition. The analysis was carried out using in parallel a quasi-stationary software, normally used in the professional field, and a dynamic simulation model often used in the academic world. The proposed interventions cover the components of the building’s envelope, the heating-cooling system and the supply of energy from renewable sources. In these latter points, the analysis has focused more on assessing two aspects that affect the supply of renewable energy. The first concerns the use of advanced logic control systems for air conditioning units in order to increase photovoltaic self-consumption. With these adjustments, a considerable increase in photovoltaic self-consumption and a decrease in the electricity exported to the Island's electricity grid have been obtained. The second point concerned the evaluation of the building's energy classification considering the real efficiency of the heating-cooling plant. Normally the energy plants have lower operational efficiency than the designed one due to multiple reasons; the decrease in the energy classification of the building for this factor has been quantified. This study represents an important example for the evaluation of the best interventions for the energy renovation of buildings in the Mediterranean Climate and a good description of the correct methodology to evaluate the resulting improvements.

Keywords: heat pumps, HVAC systems, nZEB renovation, renewable energy sources

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Authors: Mballa Abanda Luc Aurelien Serge, Henda Gnakate Biba, Kuate Guemo Romaric, Akono Rufine Nicole, Zabotom Yaya Fadel Biba, Petfiang Sidonie, Bella Suzane Jenifer

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The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original, and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological, and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation, and the question of modeling in the human sciences: mathematics, computer science, translation automation, and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal, and random music. The experimentation confirming the theorization, I designed a semi-digital, semi-analog application that translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music, and deterministic and random music). To test this application, I use music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). The translation is done (from writing to writing, from writing to speech, and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz, and world music or variety, etc. The software runs, giving you the option to choose harmonies, and then you select your melody.

Keywords: language, music, sciences, quantum entenglement

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Sahand Golmohammadi, Sana Hosseini Shirazi

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Because the status and mechanical parameters of discontinuities in the rock mass are included in the calculations, various methods of rock engineering classification are often used as a starting point for the design of different types of structures. The Q-system is one of the most frequently used methods for stability analysis and determination of support systems of underground structures in rock, including tunnel. In this method, six main parameters of the rock mass, namely, the rock quality designation (RQD), joint set number (Jn), joint roughness number (Jr), joint alteration number (Ja), joint water parameter (Jw) and stress reduction factor (SRF) are required. In this regard, in order to achieve a reasonable and optimal design, identifying the effective parameters for the stability of the mentioned structures is one of the most important goals and the most necessary actions in rock engineering. Therefore, it is necessary to study the relationships between the parameters of a system and how they interact with each other and, ultimately, the whole system. In this research, it has attempted to determine the most effective parameters (key parameters) from the six parameters of rock mass in the Q-system using the rock engineering system (RES) method to improve the relationships between the parameters in the calculation of the Q value. The RES system is, in fact, a method by which one can determine the degree of cause and effect of a system's parameters by making an interaction matrix. In this research, the geomechanical data collected from the water conveyor tunnel of Azad Dam were used to make the interaction matrix of the Q-system. For this purpose, instead of using the conventional methods that are always accompanied by defects such as uncertainty, the Q-system interaction matrix is coded using a technique that is actually a statistical analysis of the data and determining the correlation coefficient between them. So, the effect of each parameter on the system is evaluated with greater certainty. The results of this study show that the formed interaction matrix provides a reasonable estimate of the effective parameters in the Q-system. Among the six parameters of the Q-system, the SRF and Jr parameters have the maximum and minimum impact on the system, respectively, and also the RQD and Jw parameters have the maximum and minimum impact on the system, respectively. Therefore, by developing this method, we can obtain a more accurate relation to the rock mass classification by weighting the required parameters in the Q-system.

Keywords: Q-system, rock engineering system, statistical analysis, rock mass, tunnel

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Authors: Erin Hea-Jin Kim

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This study aims to investigate scholarly communication patterns in Korean studies, which focuses on all aspects of Korea, including history, culture, literature, politics, society, economics, religion, and so on. It is called ‘national study or home study’ as the subject of the study is itself, whereas it is called ‘area study’ as the subject of the study is others, i.e., outside of Korea. Understanding of the structure of scholarly communication in Korean studies is important since the motivations, procedures, results, or outcomes of individual studies may be affected by the cooperative relationships that appear in the communication structure. To this end, we collected 1,798 articles with the (author or index) keyword ‘Korean’ published in 2018 from the Scopus database and extracted the institution and country of the authors using a text mining technique. A total of 96 countries, including South Korea, was identified. Then we constructed a co-authorship network based on the countries identified. The indicators of social network analysis (SNA), co-occurrences, and cluster analysis were used to measure the activity and connectivity of participation in collaboration in Korean studies. As a result, the highest frequency of collaboration appears in the following order: S. Korea with the United States (603), S. Korea with Japan (146), S. Korea with China (131), S. Korea with the United Kingdom (83), and China with the United States (65). This means that the most active participants are S. Korea as well as the USA. The highest rank in the role of mediator measured by betweenness centrality appears in the following order: United States (0.165), United Kingdom (0.045), China (0.043), Japan (0.037), Australia (0.026), and South Africa (0.023). These results show that these countries contribute to connecting in Korean studies. We found two major communities among the co-authorship network. Asian countries and America belong to the same community, and the United Kingdom and European countries belong to the other community. Korean studies have a long history, and the study has emerged since Japanese colonization. However, Korean studies have never been investigated by digital content analysis. The contributions of this study are an analysis of co-authorship in Korean studies with a global perspective based on digital content, which has not attempted so far to our knowledge, and to suggest ideas on how to analyze the humanities disciplines such as history, literature, or Korean studies by text mining. The limitation of this study is that the scholarly data we collected did not cover all domestic journals because we only gathered scholarly data from Scopus. There are thousands of domestic journals not indexed in Scopus that we can consider in terms of national studies, but are not possible to collect.

Keywords: co-authorship network, Korean studies, Koreanology, scholarly communication

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Authors: Vistorina Amputu, Katja Tielboerger, Nichola Knox

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Primary productivity in dry savannahs is constraint by moisture availability and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, methods for assessing the status of such rangelands should be easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labor-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software provide an opportunity to transcend these limitations. Yet, they have not been extensively calibrated in natural systems to encompass their complexities if they are to be integrated for long-term monitoring. Limited research using drone technology has been conducted in arid savannas, for example to assess the health status of this dynamic two-layer vegetation ecosystem. In our study, we fill this gap by testing the relationship between UAV-estimated cover of rangeland functional attributes and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classification performed on the ultra-high resolution multispectral imagery to distinguish between rangeland functional attributes (bare, non-woody, and woody), with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.

Keywords: arid savannah, degradation gradient, field observations, narrow-band sensor, supervised classification

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Authors: Saeid Gharechelou, Ryutaro Tateishi

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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.

Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid damage monitoring, 2015-Nepal earthquake

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Authors: Eudjen Cinc

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Common Byzantine origins of church music of Serbs and Romanians are certainly not the only reason for great similarities between the ways of singing of the two nations, especially in the region of Banat. If it was so, the differences between the interpretation of church music in this part of Orthodox religion and the one specific for other parts where Serbs or Romanians live could not be explained. What is it that connects church signing of two nations in this peaceful part of Europe to such an extent that it could be considered a comprehensive corpus, different from other 'Serbian' or 'Romanian' regions? This is the main issue dealt with in the text according to examples and comparative processing of material. The main aim of the paper is representation of the new and interesting, while its value lies in its potential to encourage the reader or a future researcher to investigate and search further.

Keywords: folk influences, melody, melodic models, ethnomusicology

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Authors: Jeffrey Chase

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Historically the profession of Occupational Therapy was closely tied to the treatment of those suffering from mental illness; more recently, and especially in the U.S., the percentage of OTs identifying as working in the mental health area has declined significantly despite the estimate that by 2020 behavioral health disorders will surpass physical illnesses as the major cause of disability worldwide. In the U.S. less than 10% of OTs identify themselves as working with the mentally ill and/or practicing in mental health settings. Such a decline has implications for both those suffering from mental illness and the profession of Occupational Therapy. One reason cited for the decline of OT in mental health has been the limited research in the discipline addressing mental health practice. Despite significant advances in technology and growth in the field of neuroscience, major institutions and funding sources such as the National Institute of Mental Health (NIMH) have noted that research into the etiology and treatment of mental illness have met with limited success over the past 25 years. One major reason posited by NIMH is that research has been limited by how we classify individuals, that being mostly on what is observable. A new classification system being developed by NIMH, the Research Domain Criteria (RDoc), has the goal to look beyond just descriptors of disorders for common neural, genetic, and physiological characteristics that cut across multiple supposedly separate disorders. The hope is that by classifying individuals along RDoC measures that both reliability and validity will improve resulting in greater advances in the field. As a result of this change NIH and NIMH will prioritize research funding to those projects using the RDoC model. Multiple disciplines across many different setting will be required for RDoC or similar classification systems to be developed. During this shift in research methodology OT has an opportunity to reassert itself into the research and treatment of mental illness, both in developing new ways to more validly classify individuals, and to document the legitimacy of previously ill-defined and validated disorders such as sensory integration.

Keywords: global mental health and neuroscience, research opportunities for ot, greater integration of ot in mental health research, research and funding opportunities, research domain criteria (rdoc)

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Authors: Srushti Patil, Preethi Baligar, Gopalkrishna Joshi, Gururaj N. Bhadri

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In designing learning environments, the instructional strategies can be tailored to suit the learning style of an individual to ensure effective learning. In this study, the information shared on social media like Facebook is being used to predict learning style of a learner. Previous research studies have shown that Facebook data can be used to predict user personality. Users with a particular personality exhibit an inherent pattern in their digital footprint on Facebook. The proposed work aims to correlate the user's’ personality, predicted from Facebook data to the learning styles, predicted through questionnaires. For Millennial learners, Facebook has become a primary means for information sharing and interaction with peers. Thus, it can serve as a rich bed for research and direct the design of learning environments. The authors have conducted this study in an undergraduate freshman engineering course. Data from 320 freshmen Facebook users was collected. The same users also participated in the learning style and personality prediction survey. The Kolb’s Learning style questionnaires and Big 5 personality Inventory were adopted for the survey. The users have agreed to participate in this research and have signed individual consent forms. A specific page was created on Facebook to collect user data like personal details, status updates, comments, demographic characteristics and egocentric network parameters. This data was captured by an application created using Python program. The data captured from Facebook was subjected to text analysis process using the Linguistic Inquiry and Word Count dictionary. An analysis of the data collected from the questionnaires performed reveals individual student personality and learning style. The results obtained from analysis of Facebook, learning style and personality data were then fed into an automatic classifier that was trained by using the data mining techniques like Rule-based classifiers and Decision trees. This helps to predict the user personality and learning styles by analysing the common patterns. Rule-based classifiers applied for text analysis helps to categorize Facebook data into positive, negative and neutral. There were totally two models trained, one to predict the personality from Facebook data; another one to predict the learning styles from the personalities. The results show that the classifier model has high accuracy which makes the proposed method to be a reliable one for predicting the user personality and learning styles.

Keywords: educational data mining, Facebook, learning styles, personality traits

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Ahmed Elhussein, Hossam El-Zomor, Adel Alieldin, Mahmoud A. Afifi, Abdullah Elhusseiny, Hala Taha, Amal Refaat, Soha Ahmed, Mohamed S. Zagloul

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Background: A majority of children with retinoblastoma (60%) have a disease in one eye only (unilateral disease). This is a retrospective study to evaluate two different treatment modalities in those patients for saving their lives and vision. Methods: Four hundred and four patients were diagnosed with unilateral intraocular retinoblastoma at Children’s Cancer, Hospital Egypt (CCHE) through the period of July/2007 until December/2017. Management strategies included primary enucleation versus ocular salvage treatment. Results: Patients presented with mean age 24.5 months with range (1.2-154.3 months). According to the international retinoblastoma classification, Group D (n=172, 42%) was the most common, followed by group E (n=142, 35%), group C (n=63, 16%), and group B (n=27, 7%). All patients were alive at the end of the study except four patients who died, with 5-years overall survival 98.3% [CI, (96.5-100%)]. Patients presented with advanced disease and poor visual prognosis (n=241, 59.6%) underwent primary enucleation with 6 cycles adjuvant chemotherapy if they had high-risk features in the enucleated eye; only four patients out of 241 ended-up either with extraocular metastasis (n=3) or death (n=1). While systemic chemotherapy and focal therapy were the primary treatment for those who presented with favorable disease status and good visual prognosis (n=163, 40.4%); seventy-seven patients of them (47%) ended up with a pre-defined event (enucleation, EBRT, off protocol chemotherapy or 2ry malignancy). Ocular survival for patients received primary chemotherapy + focal therapy was [50.9% (CI, 43.5-59.6%)] at 3 years and [46.9% (CI,39.3-56%)] at 5 years. Comparison between upfront enucleation and primary chemotherapy for occurrence of extraocular metastasis revealed that there was no statistical difference between them except in group D (p value). While for occurrence of death, no statistical difference in all classification groups. Conclusion: In retinoblastoma, primary chemotherapy is a reasonable option and has a good probability for ocular salvage without increasing the risk of metastasis in comparison to upfront enucleation except in group D.

Keywords: CCHE, chemotherapy, enucleation, retinoblastoma

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Authors: Bianca W. O. Silva, Adriana C. M. Andrade, Gustavo C. M. Lucena, Virna M. S. Lima

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Introduction: Basic life support (BLS) involves the immediate recognition of cardiopulmonary arrest. Each year, 359.400 and 275.000 individuals with cardiac arrest are attended in emergency departments in USA and Europe. Brazilian data shows that 200.000 cardiac arrests occur every year, and half of them out of the hospital. Medical schools around the world teach BLS in the first years of the course, but studies show that there is a decline of the knowledge as the years go by, affecting the chain of survival. The objective was to analyze the knowledge of medical students about BLS and the retention of this learning throughout the course. Methods: This study included 150 students who were at the second and third year of a medical school in Salvador, Bahia, Brazil. The instrument of data collection was a structured questionnaire composed of 20 questions based on the 2015 American Heart Association guideline. The Pearson Chi-square test was used in order to study the association between previous training, sex and semester with the degree of knowledge of the students. The Kruskal-Wallis test was used to evaluate the different yields obtained between the various semesters. The number of correct answers was described by average and quartiles. Results: Regarding the degree of knowledge, 19.6% of the female students reached the optimal classification, a better outcome than the achieved by the male participants. Of those with previous training, 33.33% were classified as good and optimal, none of the students reached the optimal classification and only 2.2% of them were classified as bad (those who did not have 52.6% of correct answers). The analysis of the degree of knowledge related to each semester revealed that the 5th semester had the highest outcome: 30.5%. However, the acquaintance presented by the semesters was generally unsatisfactory, since 50% of the students, or more, demonstrated knowledge levels classified as bad or regular. When confronting the different semesters and the achieved scores, the value of p was 0.831. Conclusion: It is important to focus on the training of medical professionals that are capable of facing emergency situations, improving the systematization of care, and thereby increasing the victims' possibility of survival.

Keywords: basic life support, cardiopulmonary ressucitacion, education, medical students

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Authors: Oscar E. Cariceo, Claudia V. Casal

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Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice.

Keywords: cyberbullying, evidence based practice, machine learning, social work research

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Authors: Vadim Kulikov

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The classical Stroop effect is the phenomenon that it takes more time to name the ink color of a printed word if the word denotes a conflicting color than if it denotes the same color. Over the last 80 years, there have been many variations of the experiment revealing various mechanisms behind semantic, attentional, behavioral and perceptual processing. The Stroop task is known to exhibit asymmetry. Reading the words out loud is hardly dependent on the ink color, but naming the ink color is significantly influenced by the incongruent words. This asymmetry is reversed, if instead of naming the color, one has to point at a corresponding color patch. Another debated aspects are the notions of automaticity and how much of the effect is due to semantic and how much due to response stage interference. Is automaticity a continuous or an all-or-none phenomenon? There are many models and theories in the literature tackling these questions which will be discussed in the presentation. None of them, however, seems to capture all the findings at once. A computational model is proposed which is based on the philosophical idea developed by the author that the mind operates as a collection of different information processing modalities such as different sensory and descriptive modalities, which produce emergent phenomena through mutual interaction and coherence. This is the framework theory where ‘framework’ attempts to generalize the concepts of modality, perspective and ‘point of view’. The architecture of this computational model consists of blocks of neurons, each block corresponding to one framework. In the simplest case there are four: visual color processing, text reading, speech production and attention selection modalities. In experiments where button pressing or pointing is required, a corresponding block is added. In the beginning, the weights of the neural connections are mostly set to zero. The network is trained using Hebbian learning to establish connections (corresponding to ‘coherence’ in framework theory) between these different modalities. The amount of data fed into the network is supposed to mimic the amount of practice a human encounters, in particular it is assumed that converting written text into spoken words is a more practiced skill than converting visually perceived colors to spoken color-names. After the training, the network performs the Stroop task. The RT’s are measured in a canonical way, as these are continuous time recurrent neural networks (CTRNN). The above-described aspects of the Stroop phenomenon along with many others are replicated. The model is similar to some existing connectionist models but as will be discussed in the presentation, has many advantages: it predicts more data, the architecture is simpler and biologically more plausible.

Keywords: connectionism, Hebbian learning, artificial neural networks, philosophy of mind, Stroop

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Authors: Alisher Ikramov, Gayrat Juraev

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The production of logic devices faces the occurrence of faults during manufacturing. This work analyses the complexity of testing a special type of logic device on inverse, adhesion, and constant input faults. The focus of this work is on devices that implement cryptographic functions. The complexity values for the general case faults and for some frequently occurring subsets were determined and proved in this work. For a special case, when the length of the text block is equal to the length of the key block, the complexity of testing is proven to be asymptotically half the complexity of testing all logic devices on the same types of input faults.

Keywords: complexity, cryptographic devices, input faults, testing

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Authors: M. P. Tripathi, Priti Tiwari

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Soil and Water Assessment Tool (SWAT) is a distributed parameter continuous time model and was tested on daily and fortnightly basis for a small agricultural watershed (Dhangaon) of Chhattisgarh state in India. The SWAT model recently interfaced with ArcGIS and called as ArcSWAT. The watershed and sub-watershed boundaries, drainage networks, slope and texture maps were generated in the environment of ArcGIS of ArcSWAT. Supervised classification method was used for land use/cover classification from satellite imageries of the years 2009 and 2012. Manning's roughness coefficient 'n' for overland flow and channel flow and Fraction of Field Capacity (FFC) were calibrated for monsoon season of the years 2009 and 2010. The model was validated on a daily basis for the years 2011 and 2012 by using the observed daily rainfall and temperature data. Calibration and validation results revealed that the model was predicting the daily surface runoff and sediment yield satisfactorily. Sensitivity analysis showed that the annual sediment yield was inversely proportional to the overland and channel 'n' values whereas; annual runoff and sediment yields were directly proportional to the FFC. The model was also tested (calibrated and validated) for the fortnightly runoff and sediment yield for the year 2009-10 and 2011-12, respectively. Simulated values of fortnightly runoff and sediment yield for the calibration and validation years compared well with their observed counterparts. The calibration and validation results revealed that the ArcSWAT model could be used for identification of critical sub-watershed and for developing management scenarios for the Dhangaon watershed. Further, the model should be tested for simulating the surface runoff and sediment yield using generated rainfall and temperature before applying it for developing the management scenario for the critical or priority sub-watersheds.

Keywords: watershed, hydrologic and water quality, ArcSWAT model, remote sensing, GIS, runoff and sediment yield

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Authors: Nathenal Thomas

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In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: Davoud Ansari Kejal, Meysam Sabour

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For long, there has been a belief that a reader can easily comprehend a text if he is strong enough in vocabulary and grammatical knowledge but there was no account for the ability of understanding different subjects based on readers’ understanding of the surrounding world which is called world background knowledge. This paper attempts to investigate the reading comprehension process applying the schema theory as an influential factor in comprehending texts, in order to prove the important role of background knowledge in reading comprehension. Based on the discussion, some teaching methods are suggested for employing world background knowledge for an elaborated teaching of reading comprehension in an active learning environment in EFL classes.

Keywords: background knowledge, reading comprehension, schema theory, ELT classes

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Tomiwa, Akinyemi Clement

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Tropical regions experience diverse and complex precipitation patterns, posing significant challenges for accurate rainfall estimation and forecasting. This study addresses the problem of effectively classifying tropical rain types and refining the Z-R (Reflectivity-Rain Rate) relationship to enhance rainfall estimation accuracy. Through a combination of remote sensing, meteorological analysis, and machine learning, the research aims to develop an advanced classification framework capable of distinguishing between different types of tropical rain based on their unique characteristics. This involves utilizing high-resolution satellite imagery, radar data, and atmospheric parameters to categorize precipitation events into distinct classes, providing a comprehensive understanding of tropical rain systems. Additionally, the study seeks to improve the Z-R relationship, a crucial aspect of rainfall estimation. One year of rainfall data was analyzed using a Micro Rain Radar (MRR) located at The Federal University of Technology Akure, Nigeria, measuring rainfall parameters from ground level to a height of 4.8 km with a vertical resolution of 0.16 km. Rain rates were classified into low (stratiform) and high (convective) based on various microstructural attributes such as rain rates, liquid water content, Drop Size Distribution (DSD), average fall speed of the drops, and radar reflectivity. By integrating diverse datasets and employing advanced statistical techniques, the study aims to enhance the precision of Z-R models, offering a more reliable means of estimating rainfall rates from radar reflectivity data. This refined Z-R relationship holds significant potential for improving our understanding of tropical rain systems and enhancing forecasting accuracy in regions prone to heavy precipitation.

Keywords: remote sensing, precipitation, drop size distribution, micro rain radar

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Semire Yekta

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Many online retailers set up manual review teams to overcome the limitations of automated online fraud detection systems. This study critically examines the strategies they adapt in their decision-making process to set apart fraudulent individuals from non-fraudulent online shoppers. The study uses a mix method research approach. 32 in-depth interviews have been conducted alongside with participant observation and auto-ethnography. The study found out that all steps of the decision-making process are significantly affected by a level of subjectivity, personal understandings of online fraud, preferences and judgments and not necessarily by objectively identifiable facts. Rather clearly knowing who the fraudulent individuals are, the team members have to predict whether they think the customer might be a fraudster. Common strategies used are relying on the classification and fraud scorings in the automated fraud detection systems, weighing up arguments for and against the customer and making a decision, using cancellation to test customers’ reaction and making use of personal experiences and “the sixth sense”. The interaction in the team also plays a significant role given that some decisions turn into a group discussion. While customer data represent the basis for the decision-making, fraud management teams frequently make use of Google search and Google Maps to find out additional information about the customer and verify whether the customer is the person they claim to be. While this, on the one hand, raises ethical concerns, on the other hand, Google Street View on the address and area of the customer puts customers living in less privileged housing and areas at a higher risk of being classified as fraudsters. Phone validation is used as a final measurement to make decisions for or against the customer when previous strategies and Google Search do not suffice. However, phone validation is also characterized by individuals’ subjectivity, personal views and judgment on customer’s reaction on the phone that results in a final classification as genuine or fraudulent.

Keywords: online fraud, data mining, manual review, social construction

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Authors: Hala M. El-hanbuli, Mohammed F. Darweesh

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The incidence rates of any tumor vary hugely with geographical location. Basal Cell Carcinoma (BCC) is one of the most common skin cancer that has many histopathologic subtypes. Objective: The aim was to study the histopathological features of BCC cases that were received in the Pathology Department, Kasr El-Aini hospital, Cairo University, Egypt during the period from Jan 2004 to Dec 2013 and to evaluate the clinical characters through the patient data available in the request sheets. Methods: Slides and data of BCC cases were collected from the archives of the pathology department, Kasr El-Aini hospital. Revision of all available slides and histological classification of BCC according to WHO (2006) was done. Results: A total number of 310 cases of BCC representing about 65% from the total number of malignant skin tumors examined during the 10-years duration in the department. The age ranged from 8 to 84 years, the mean age was (55.7 ± 15.5). Most of the patients (85%) were above the age of 40 years. There was a slight male predominance (55%). Ulcerated BCC was the most common gross picture (60%), followed by nodular lesion (30%) and finally the ulcerated nodule (10%). Most of the lesions situated in the high-risk sites (77%) where the nose was the most common site (35%) followed by the periocular area (22%), then periauricular (15%) and finally perioral (5%). No lesion was reported outside the head. The tumor size was less than 2 centimeters in 65% of cases, and from 2-5 centimeters in the lesions' greatest dimension in the rest of cases. Histopathological reclassification revealed that the nodular BCC was the most common (68%) followed by the pigmented nodular (18.75%). The histologic high-risk groups represented (7.5%) about half of them (3.75%) being basosquamous carcinoma. The total incidence for multiple BCC and 2nd primary was 12%. Recurrent BCC represented 8%. All of the recurrent lesions of BCC belonged to the histologic high-risk group. Conclusion: Basal Cell Carcinoma is the most common skin cancer in the 10-year survey. Histopathological diagnosis and classification of BCC cases are essential for the determination of the tumor type and its biological behavior.

Keywords: basal cell carcinoma, high risk, histopathological features, statistical analysis

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Authors: Gudrun Görlitz, Christian Schölzel, Alexander Vollmar

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Sites of Jewish Life in Berlin 1933-1945. Between Persecution and Self-assertion” was realized in a project funded by the European Regional Development Fund. A smartphone app, and a associated web site enable tourists and other participants of this educational offer to learn in a serious way more about the life of Jews in the German capital during the Nazi era. Texts, photos, video and audio recordings communicate the historical content. Interactive maps (both current and historical) make it possible to use predefined or self combined routes. One of the manifold challenges was to create a broad ranged guide, in which all detailed information are well linked with each other. This enables heterogeneous groups of potential users to find a wide range of specific information, corresponding with their particular wishes and interests. The multitude of potential ways to navigate through the diversified information causes (hopefully) the users to utilize app and web site for a second or third time and with a continued interest. Therefore 90 locations, a lot of them situated in Berlin’s city centre, have been chosen. For all of them text-, picture and/or audio/video material gives extensive information. Suggested combinations of several of these “site stories” are leading to the offer of detailed excursion routes. Events and biographies are also presented. A few of the implemented biographies are especially enriched with source material concerning the aspect of (forced) migration of these persons during the Nazi time. All this was done in a close and fruitful interdisciplinary cooperation of computer scientists and historians. The suggested conference paper aims to show the challenges shaping complex source material for practical use by different user-groups in a proper technical and didactic way. Based on the historical research in archives, museums, libraries and digital resources the quantitative dimension of the project can be sized as follows: The paper focuses on the following historiographical and technical aspects: - Shaping the text material didactically for the use in new media, especially a Smartphone-App running on differing platforms; - Geo-referencing of the sites on historical and current map material; - Overlay of old and new maps to present and find the sites; - Using Augmented Reality technologies to re-visualize destroyed buildings; - Visualization of black-/white-picture-material; - Presentation of historical footage and the resulting problems to need too much storage space; - Financial and juridical aspects in gaining copyrights to present archival material.

Keywords: smartphone app, history, tourists, German

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