Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process\u27s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users\u27 interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making

Kalkulator garis panduan kemudahan awam untuk pembangunan perumahan mapan / Norainah Abdul Rahman … [et al.]

Garis panduan perancangan adalah sangat penting dalam mengawal pembangunan perumahan di bandar dan di luar bandar. Ini untuk memastikan pembangunan yang mampan di mana pembangunan ini menepati kehendak dan kemahuan semasa tanpa menggadaikan keperluan untuk masa hadapan. Garis panduan perancangan digunakan oleh Pihak Berkuasa Perancangan Tempatan (PBPT) untuk memastikan aktiviti guna tanah yang seragam, selesa dan selamat supaya projek pembangunan perumahan yang dijalankan memenuhi keperluan masyarakat dan mewujudkan persekitaran kehidupan yang selesa. PBPT telah menghasilkan garis panduan perancangan yang lengkap supaya dipatuhi oleh setiap projek pembangunan di Malaysia. Walau bagaimanapun, garis panduan perancangan masa kini telah disediakan dalam bentuk laporan dan tidak mesra pengguna. Ekoran daripada masalah ini, Kalkulator Garis Panduan Kemudahan Awam untuk Pembangunan Perumahan Mampan versi digital telah direka cipta. Objektifutama reka cipta ini adalah untuk membangunkan garis panduan perancangan versi digital yang lebih mesra pengguna bagi memudahkan proses pencarian maklumat serta dapat membantu pengguna mengakses maklumat yang (tepat\ 'mudah'dan 'cepat' (Exact, Easy dan Express - 3E s). Kalkulator ini disediakan untuk membantu perancang bandar, juru runding, pemaju tanah danpelajar' dalam penyediaan susun atur pembangunan yang memenuhi keperluan pembangunan bagi tujuan permohonan kebenaran merancang. Di samping, itu kalkulator ihi dijangka dapat memudahkan kerja PBPT dalam menyemak susun atur cadangan pembangunan yang dikemukakan agar ia selari dengan garis panduan perancangan supaya kebenaran perancangan dapat diluluskan

Global Retinoblastoma Presentation and Analysis by National Income Level.

Importance: Early diagnosis of retinoblastoma, the most common intraocular cancer, can save both a child's life and vision. However, anecdotal evidence suggests that many children across the world are diagnosed late. To our knowledge, the clinical presentation of retinoblastoma has never been assessed on a global scale. Objectives: To report the retinoblastoma stage at diagnosis in patients across the world during a single year, to investigate associations between clinical variables and national income level, and to investigate risk factors for advanced disease at diagnosis. Design, Setting, and Participants: A total of 278 retinoblastoma treatment centers were recruited from June 2017 through December 2018 to participate in a cross-sectional analysis of treatment-naive patients with retinoblastoma who were diagnosed in 2017. Main Outcomes and Measures: Age at presentation, proportion of familial history of retinoblastoma, and tumor stage and metastasis. Results: The cohort included 4351 new patients from 153 countries; the median age at diagnosis was 30.5 (interquartile range, 18.3-45.9) months, and 1976 patients (45.4%) were female. Most patients (n = 3685 [84.7%]) were from low- and middle-income countries (LMICs). Globally, the most common indication for referral was leukocoria (n = 2638 [62.8%]), followed by strabismus (n = 429 [10.2%]) and proptosis (n = 309 [7.4%]). Patients from high-income countries (HICs) were diagnosed at a median age of 14.1 months, with 656 of 666 (98.5%) patients having intraocular retinoblastoma and 2 (0.3%) having metastasis. Patients from low-income countries were diagnosed at a median age of 30.5 months, with 256 of 521 (49.1%) having extraocular retinoblastoma and 94 of 498 (18.9%) having metastasis. Lower national income level was associated with older presentation age, higher proportion of locally advanced disease and distant metastasis, and smaller proportion of familial history of retinoblastoma. Advanced disease at diagnosis was more common in LMICs even after adjusting for age (odds ratio for low-income countries vs upper-middle-income countries and HICs, 17.92 [95% CI, 12.94-24.80], and for lower-middle-income countries vs upper-middle-income countries and HICs, 5.74 [95% CI, 4.30-7.68]). Conclusions and Relevance: This study is estimated to have included more than half of all new retinoblastoma cases worldwide in 2017. Children from LMICs, where the main global retinoblastoma burden lies, presented at an older age with more advanced disease and demonstrated a smaller proportion of familial history of retinoblastoma, likely because many do not reach a childbearing age. Given that retinoblastoma is curable, these data are concerning and mandate intervention at national and international levels. Further studies are needed to investigate factors, other than age at presentation, that may be associated with advanced disease in LMICs

The global retinoblastoma outcome study : a prospective, cluster-based analysis of 4064 patients from 149 countries

DATA SHARING : The study data will become available online once all analyses are complete.BACKGROUND : Retinoblastoma is the most common intraocular cancer worldwide. There is some evidence to suggest that major differences exist in treatment outcomes for children with retinoblastoma from different regions, but these differences have not been assessed on a global scale. We aimed to report 3-year outcomes for children with retinoblastoma globally and to investigate factors associated with survival. METHODS : We did a prospective cluster-based analysis of treatment-naive patients with retinoblastoma who were diagnosed between Jan 1, 2017, and Dec 31, 2017, then treated and followed up for 3 years. Patients were recruited from 260 specialised treatment centres worldwide. Data were obtained from participating centres on primary and additional treatments, duration of follow-up, metastasis, eye globe salvage, and survival outcome. We analysed time to death and time to enucleation with Cox regression models. FINDINGS : The cohort included 4064 children from 149 countries. The median age at diagnosis was 23·2 months (IQR 11·0–36·5). Extraocular tumour spread (cT4 of the cTNMH classification) at diagnosis was reported in five (0·8%) of 636 children from high-income countries, 55 (5·4%) of 1027 children from upper-middle-income countries, 342 (19·7%) of 1738 children from lower-middle-income countries, and 196 (42·9%) of 457 children from low-income countries. Enucleation surgery was available for all children and intravenous chemotherapy was available for 4014 (98·8%) of 4064 children. The 3-year survival rate was 99·5% (95% CI 98·8–100·0) for children from high-income countries, 91·2% (89·5–93·0) for children from upper-middle-income countries, 80·3% (78·3–82·3) for children from lower-middle-income countries, and 57·3% (52·1-63·0) for children from low-income countries. On analysis, independent factors for worse survival were residence in low-income countries compared to high-income countries (hazard ratio 16·67; 95% CI 4·76–50·00), cT4 advanced tumour compared to cT1 (8·98; 4·44–18·18), and older age at diagnosis in children up to 3 years (1·38 per year; 1·23–1·56). For children aged 3–7 years, the mortality risk decreased slightly (p=0·0104 for the change in slope). INTERPRETATION : This study, estimated to include approximately half of all new retinoblastoma cases worldwide in 2017, shows profound inequity in survival of children depending on the national income level of their country of residence. In high-income countries, death from retinoblastoma is rare, whereas in low-income countries estimated 3-year survival is just over 50%. Although essential treatments are available in nearly all countries, early diagnosis and treatment in low-income countries are key to improving survival outcomes.The Queen Elizabeth Diamond Jubilee Trust and the Wellcome Trust.https://www.thelancet.com/journals/langlo/homeam2023Paediatrics and Child Healt

Resistance to Moisture Damage of Recycled Asphalt Concrete Pavement

Recycled asphalt concrete mixture are prepared, artificially aged and processed in the laboratory to maintain the homogeneity of recycled asphalt concrete mixture gradation, and bitumen content. The loose asphalt concrete mix was subjected to cycle of accelerated aging, (short –term aging) and the compacted mix was subjected to (long -term aging) as per Super-pave procedure. Twenty four Specimens were constructed at optimum asphalt content according to Marshall Method. Recycled mixture was prepared from aged asphalt concrete using recycling agent (soft asphalt cement blended with silica fumes) by (1.5%) weight of mixture as recycling agent content. The effect of recycling agent on aging after recycling process behavior of asphalt concrete was determine. Aged specimens after recycling process were prepared by subjecting the recycled asphalt concrete to accelerated aging and tested for resistance to moisture damage. The improvement in the resistance to moisture damage of aged mixture after recycling with (soft asphalt cement blended with silica fumes) was 76.17% as compared to the corresponding aged mixture before recycling process. The ITS for unconditioned specimens for aged after recycling process mixture was less than reference by 67.1%, and less than that of aged before recycling process mixtures by 64.1%

Cybersecurity Risk Analysis in the IoT: A Systematic Review

The Internet of Things (IoT) is increasingly becoming a part of our daily lives, raising significant concerns about future cybersecurity risks and the need for reliable solutions. This study conducts a comprehensive systematic literature review to examine the various challenges and attacks threatening IoT cybersecurity, as well as the proposed frameworks and solutions. Furthermore, it explores emerging trends and identifies existing gaps in this domain. The study’s novelty lies in its extensive exploration of machine learning techniques for detecting and countering IoT threats. It also contributes by highlighting research gaps in economic impact assessment and industrial IoT security. The systematic review analyzes 40 articles, providing valuable insights and guiding future research directions. Results show that privacy issues and cybercrimes are the primary concerns in IoT security, and artificial intelligence holds promise for future cybersecurity. However, some attacks remain inadequately addressed by existing solutions, such as confidentiality, security authentication, and data server connection attacks, necessitating further research and real-life testing of proposed remedies

Solar and sand: Dust deposit mitigation in the desert for PV arrays

Solar photovoltaic installations have now become a common sight across the globe. However, in places with a high level of dust, the panels have not performed as expected. The dust deposition acts to reduce the effective light that the solar cells receive thus reducing the output. As such, cleaning regimes are instituted to improve the performance of the panels. The more often the cleaning, the better the performance, but this could likely imply more energy is used for cleaning and the improvement may not be sufficient. In this paper, we develop a model to forecast the output power with respect to variables of cleaning, wind, humidity and temperature. The result shows that it is possible to develop a model to optimise cleaning regimes taking into account meteorological factors. One has to consider the impact of wind, which was found to be positively correlated with photovoltaic power output

Actual Use of Mobile Learning Technologies during Social Distancing Circumstances: Case Study of King Faisal University Students

The most current highly infectious disease, which has become a global health challenge permeating entire sectors of society, is COVID-19. In the education sector, the transmission of COVID-19 has been curbed through the closure of institutions and the facilitation of online learning. The main objective of this study was to propose an integrated model of the unified theory of acceptance and use of technology combined with the DeLone and McLean model, to examine the influence of quality features, namely, performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI), on the intentions and satisfaction of users toward mobile learning (m-learning) use in the context of Saudi learning institutions. The study obtained m-learning user data using an online questionnaire, after which the data were exposed to partial least squares structural equation modeling to test the proposed research model. The findings supported the influence of PE, EE, and FC on intention toward m-learning use but did not support the significant influence of SI. Moreover, system, intention, and user satisfaction were found to positively and significantly influence m-learning-system usage, with system, information, and service quality being top drivers of such user intention and satisfaction. The results reflect the required information concerning the strategies of higher institutions to enhance m-learning-system acceptance among students, with general implications for learning acceptance and usage

Temporal behavioural analysis of extremists on social media: A machine learning based approach

Public opinion is of critical importance to businesses and governments. It represents the collective opinion and prevalent views about a certain topic, policy, or issue. Extreme public opinion consists of extreme views held by individuals that advocate and spread radical ideas for the purpose of radicalizing others. while the proliferation of social media gives unprecedented reach and visibility and a platform for freely expressing public opinion, social media fora can also be used for spreading extreme views, manipulating public opinions, and radicalizing others. In this work, we leverage data mining and analytics techniques to study extreme public opinion expressed using social medial. A dataset of 259, 904 tweets posted between 21/02/2016 and 01/05/2021 was collected in relation to extreme nationalism, hate speech, and supremacy. The collected data was analyzed using a variety to techniques, including sentiment analysis, named entity recognition, social circle analysis, and opinion leaders\u27 identification, and results related to an American politician and an American right-wing activist were presented. The results obtained are very promising and open the door to the ability to monitor the evolution of extreme views and public opinion online

Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process\u27s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users\u27 interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making