UrduAI: Writeprints for Urdu Authorship Identification

The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. However, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces and when the number of candidate authors increases. Consequently, these solutions are inapplicable to real-world cases. Moreover, due to the unavailability of reliable POS taggers or sentence segmenters, all existing authorship identification studies on Urdu text are limited to the word n-grams features only. To overcome these limitations, we formulate a stylometric feature space, which is not limited to the word n-grams feature only. Based on this feature space, we use an authorship identification solution that transforms each text sample into a point set, retrieves candidate text samples, and relies on the nearest neighbors classifier to predict the original author of the anonymous text sample. To evaluate our solution, we create a significantly larger corpus than existing studies and conduct several experimental studies that show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works.

Factors Affecting Customer Intention to Adopt a Mobile Chronic Disease Management Service

The Mobile Chronic Disease Management Service (MCDMS) is an emerging medical service for chronic disease prevention and treatment, but limited attention has been paid to the factors that affect users’ intention to adopt the service. Based on the unified theory of acceptance and use of technology 2 and the protection motivation theory, the authors built an MCDMS adoption model. The authors also verified the differentiating age effect on the service adoption intention from experiential distance perspective of the construal level theory. Empirical results showed that the young group focused more on the impact of effort expectancy, whereas the elderly group focused more on performance expectancy, imitating others, and perceived severity. Furthermore, the young group, however, focused more on the impact of perceived vulnerability, and offline medical habits showed no significant influence on either group’s intention to adopt, which were not consistent with the original hypotheses. The findings can aid MCDMS providers in selecting marketing strategies targeted toward different age groups.

Do Micronutrient and Omega-3 Fatty Acid Supplements Affect Human Maternal Immunity during Pregnancy? A Scoping Review

Maternal dietary micronutrients and omega-3 fatty acids support development of the fetal and neonatal immune system. Whether supplementation is similarly beneficial for the mother during gestation has received limited attention. A scoping review of human trials was conducted looking for evidence of biochemical, genomic, and clinical effects of supplementation on the maternal immune system. The authors explored the literature on PubMed, Cochrane Library, and Web of Science databases from 2010 to the present day using PRISMA-ScR methodology. Full-length human trials in English were searched for using general terms and vitamin A, B12, C, D, and E; choline; iodine; iron; selenium; zinc; and docosahexaenoic/eicosapentaenoic acid. Of 1391 unique articles, 36 were eligible for inclusion. Diverse biochemical and epigenomic effects of supplementation were identified that may influence innate and adaptive immunity. Possible clinical benefits were encountered in malaria, HIV infections, anemia, Type 1 diabetes mellitus, and preventing preterm delivery. Only limited publications were identified that directly explored maternal immunity in pregnancy and the effects of micronutrients. None provided a holistic perspective. It is concluded that supplementation may influence biochemical aspects of the maternal immune response and some clinical outcomes, but the evidence from this review is not sufficient to justify changes to current guidelines.

Observed correlations from cross-sectional individual differences research reflect both between-person and within-person correlations

The study of individual differences is often conducted using cross-sectional experiments. The cross-sectional nature of such data means that results obtained from these experiments are a mix of both between- and within-person associations. While concerns about this within-between confounding effect have been surfaced, they are often not addressed when reporting results from individual differences studies. This obfuscation threatens the interpretability and validity of cross-sectional individual differences research. We speculate that a lack of a clear and accessible description of the confounding effect may have contributed to the limited attention to this issue. In this paper, we develop a tractable description of the confounding effect and how it manifests. In doing so, we suggest a need for greater transparency in the reporting of the goals and results of cross-sectional studies and how they contribute to knowledge of individual differences.

Pensions and the Extending Working Lives Agenda in the UK: The Impact on Women

Extending working lives (EWLs) has been a key policy response to the challenges presented by an ageing population in the United Kingdom (UK). This includes the use of pension policies to encourage working longer. However, opportunities and experiences of EWLs are not equal. While much has been written about EWLs more broadly, limited attention has been paid to connecting those EWLs policies associated with pensions and their potentially unequal impact on women. This article aims to address this gap, taking a feminist political-economy perspective to explore the structural constraints that shape EWLs and pensions. Initially it briefly introduces the EWLs agenda, before focussing on pension developments and their implications for EWLs, considering the gendered nature of these policies. Finally, it touches upon potential policy measures to mitigate the impact of these developments on women. It demonstrates how women’s existing labour market and pension disadvantages have been largely overlooked in the development of EWLs policy, perpetuating or expanded many women’s financial inequalities in later life. It highlights the need for a greater focus on gendered pension differences in developing EWLs policy to ensure women’s circumstances are not adversely impacted on.

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

GVC-Oriented Policies and Urban Manufacturing: The Role of Cities in Global Value Chains

Studies on policies oriented to Global Value Chains (GVC) focus much attention on developing countries and upgrading opportunities. Recent trends related to digitalization, market requests, and new consideration for value linked to manufacturing challenges GVC-oriented policies in developed countries. Such policies may refer to the attractiveness of foreign investments or increase the value captured through upgrading. At the city level, explicit policies promoted by municipalities are oriented to attract and support manufacturing activities to increase employment, entrepreneurship, and urban specializations while leveraging the new technological scenario. However, despite their interests in policies for economic growth at the national and cluster levels, research on the Global Value Chain has paid limited attention to cities and their role as production contexts within value chains. Linking to research on urban manufacturing and based on an empirical study on six cities (Barcelona, Detroit, London, Milan, New York, and Paris), the paper advances the theoretical debate on urban-related policies in the GVC framework by proposing three different policy directions related to (a) enhancing value related to urban production; (b) sustaining new urban entrepreneurship (digital craftsmanship); and (c) shortening GVC (Urban Value Chains).

Social Media's Influence on Destination Image

Social media have become key tools for promoting and spreading the image of a tourist destination. In particular, these communication channels are critical for heritage destinations looking to boost awareness and attract a greater number of visitors. However, the tourism marketing literature has devoted limited attention to how these destinations project their image on social media. In order to cover this gap, this chapter focuses on analyzing the image projected by a specific heritage destination—Cuenca, a World Heritage City—on Facebook and Instagram. To this end, the chapter analyzes the posts, comments, and hashtags that have been published on the different tourism pages about Cuenca. The results of the analysis underscore the growing importance of the image projected by destinations on social networks. That said, there is a need to deepen our knowledge about the communication potential of these channels.

Assessing the forecasting model ability in measuring the prevention transmission of COVID-19 pandemic: An application of visibility analysis using Inductive logic

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

Applying a Developmental Lens to Educational Game Designs for Preschoolers

Preschool-aged learners process information differently from older individuals, making it critical to design digital educational games that are tailored to capitalize on young children's learning capabilities. This in-depth literature synthesis connects features of digital educational game design - including visuals, feedback, scaffolding challenge, rewards, and physical interactions to how young children learn. Preschoolers' interests and abilities (e.g., limited attention-span, early reading skills, etc.) are different than older users. As such, developmental science should be used to guide the design of educational games from aesthetic decisions that capture preschoolers' initial interest (e.g., meaningful characters) to carefully select end-of-game rewards (e.g., leveling up). This article connects learning and developmental science research to the design of digital educational games, offering insights into how best to design games for young users and how to select developmentally appropriate games for children.