Search

Posting personalized comments on an upcoming hot topic in time is very meaningful, which not only likely to attract more users to participate, but also affects other users' point of view. LSTM-SNP is a variant of long short-term memory (LSTM) inspired by the nonlinear spiking mechanism in nonlinear spiking neural systems. In order to improve the efficiency and diversity of user-edited comments, we design a novel assistant based on the LSTM-SNP model. The assistant consists of two modules, one for predicting topic hotness and the other for generating comments with personalized expression features based on blog post and user information. Experimental results show that, this novel assistant not only predicts the upcoming hot topics accurately, but also outperforms the baseline model in terms of automatic evaluation and human discernment of comment generation. More importantly, the generated comments excel in terms of timeliness and personalization. [ABSTRACT FROM AUTHOR]

Controllable Image Captioning (CIC)—generating natural language descriptions about images under the guidance of given control signals—is one of the most promising directions toward next-generation captioning systems. Till now, various kinds of control signals for CIC have been proposed, ranging from content-related control to structure-related control. However, due to the format and target gaps of different control signals, all existing CIC works (or architectures) only focus on one certain control signal, and overlook the human-like combinatorial ability. By "combinatorial", we mean that our humans can easily meet multiple needs (or constraints) simultaneously when generating descriptions. To this end, we propose a novel prompt-based framework for CIC by learning Combinatorial Prompts, dubbed as ComPro. Specifically, we directly utilize a pretrained language model GPT-2 Radford et al. (OpenAI blog 1:9, 2019) as our language model, which can help to bridge the gap between different signal-specific CIC architectures. Then, we reformulate the CIC as a prompt-guide sentence generation problem, and propose a new lightweight prompt generation network to generate the combinatorial prompts for different kinds of control signals. For different control signals, we further design a new mask attention mechanism to realize the prompt-based CIC. Due to its simplicity, our ComPro can be further extended to more kinds of combined control signals by concatenating these prompts. Extensive experiments on two prevalent CIC benchmarks have verified the effectiveness and efficiency of our ComPro on both single and combined control signals. [ABSTRACT FROM AUTHOR]

Detecting texts that contain semantic-level new information is not straightforward. The problem becomes more challenging for research articles. Over the years, many datasets and techniques have been developed to attempt automatic novelty detection. However, the majority of the existing textual novelty detection investigations are targeted toward general domains like newswire. A comprehensive dataset for scientific novelty detection is not available in the literature. In this paper, we present a new triplet-based corpus (SciND) for scientific novelty detection from research articles via knowledge graphs. The proposed dataset consists of three types of triples (i) triplet for the knowledge graph, (ii) novel triplets, and (iii) non-novel triplets. We build a scientific knowledge graph for research articles using triplets across several natural language processing (NLP) domains and extract novel triplets from the paper published in the year 2021. For the non-novel articles, we use blog post summaries of the research articles. Our knowledge graph is domain-specific. We build the knowledge graph for seven NLP domains. We further use a feature-based novelty detection scheme from the research articles as a baseline. Moreover, we show the applicability of our proposed dataset using our baseline novelty detection algorithm. Our algorithm yields a baseline F1 score of 72%. We show analysis and discuss the future scope using our proposed dataset. To the best of our knowledge, this is the very first dataset for scientific novelty detection via a knowledge graph. We make our codes and dataset publicly available at https://github.com/92Komal/Scientific_Novelty_Detection. [ABSTRACT FROM AUTHOR]

People on the autism spectrum can learn about autism from various sources, likely differing in the information, portrayal, and discussion they offer. The present study investigates where autistic people learn about autism, and whether their information source is associated with their level of autism knowledge, perceptions of stigma, and development and expression of an autism identity. A survey of 198 Australian adults with an autism diagnosis showed that learning about autism from conventional sources (e.g., professionals, parents) was associated with more internalised stigma, lower endorsement of special abilities and autism identity, whereas online blogs and social media showed the opposite pattern as well as more accurate knowledge of autism. The findings raise questions about how authoritative sources of information discuss autism. [ABSTRACT FROM AUTHOR]

With the emerging new technologies based on Artificial Intelligence (AI) for the generation of new and paraphrasing of existing text, the identification of genuinely written text has become an important research undertaking. Past approaches to address this issue, need a significant volume of human-labeled data. Most of the approaches used in literature are either for noisy text or for clean text. Conversations in chats, text in blogs, text messages on cell phones, text exchange on Messengers, etc., are examples of noisy text that may contain misspelled words or incomplete words. The second approach focuses on clean text, which is free from the mentioned characteristics in the noisy text. As research articles do not contain noisy data, we propose a model that focuses on clean text for the identification of paraphrases in research articles. To address the problem of paraphrase detection, this work presents a novel Bidirectional Encoder Representation from Transformers (BERT) based model with fine-tuning. For word representation, Global Vectors (Glove) embeddings and contextualized Embeddings From Language Models (ELMo) are employed in this work. Initially, the model is evaluated without performing preprocessing. Later, the preprocessing step is performed before evaluating the model. Extensive experimentations are performed to evaluate the proposed model utilizing two benchmark datasets, namely, Microsoft Research Paraphrase (MSRP) and Quora Question Pairs (Quora). A comparison of the proposed model is done with four closely related state-of-the-art works. The obtained results show that Fine-tuned BERT using ELMo embeddings with preprocessing produces promising outcomes. Paraphrase identification rates achieved on MSRP and Quora datasets are 86.51% and 94.32%, respectively, which are better than the other competing methods. The proposed solution enables the identification of paraphrased text with a higher accuracy having its application in multiple domains requiring genuinely written documents. [ABSTRACT FROM AUTHOR]

The significance of the web and the crucial role of web archives in its preservation highlight the necessity of understanding how users, both human and robot, access web archive content, and how best to satisfy this disparate needs of both types of users. To identify robots and humans in web archives and analyze their respective access patterns, we used the Internet Archive's (IA) Wayback Machine access logs from 2012, 2015, and 2019, as well as Arquivo.pt's (Portuguese Web Archive) access logs from 2019. We identified user sessions in the access logs and classified those sessions as human or robot based on their browsing behavior. To better understand how users navigate through the web archives, we evaluated these sessions to discover user access patterns. Based on the two archives and between the three years of IA access logs (2012 vs. 2015 vs. 2019), we present a comparison of detected robots vs. humans and their user access patterns and temporal preferences. The total number of robots detected in IA 2012 (91% of requests) and IA 2015 (88% of requests) is greater than in IA 2019 (70% of requests). Robots account for 98% of requests in Arquivo.pt (2019). We found that the robots are almost entirely limited to "Dip" and "Skim" access patterns in IA 2012 and 2015, but exhibit all the patterns and their combinations in IA 2019. Both humans and robots show a preference for web pages archived in the near past. [ABSTRACT FROM AUTHOR]

Language is a vehicle for telling the truth and for lies. While linguistic profiles have been used to analyze the language used when telling lies, the linguistic profile of pathological liars has yet to be examined. The purpose of our study was to examine the linguistic profile of pathological liars, specifically from entries made in blogs and forums. We used the Linguistic Inquiry and Word Count software to analyze the narratives of 22 self-reported pathological liars and compared those narratives to normative blog data. We found that pathological liars used more affective processes, negative emotion, risk, and family words. Additionally, pathological liars' narratives contained fewer words, were less analytic, contained less clout, consisted of less tone, and were more authentic. Lastly, pathological liars tended to use more first-person singular pronouns. Implications for research and practice are discussed. [ABSTRACT FROM AUTHOR]

Using multimedia elements to present poetry can help make it more accessible and understandable to a broader audience. In the context of China, where poetry is important in culture and art, interpreting the poems of Chinese poets in contemporary music can help popularize their work among young audiences. The study aimed to explore the possibilities of using various multimedia tools to promote poetry in online educational blogs and to study the effectiveness of various multimedia tools in attracting audience attention to poetry. The survey results reveal the popularity of contemporary songs in China among the youth, who draw inspiration from poetry. "Green Light" (绿光) received the highest percentage of votes, at 38%. This song centres on the theme of love and expresses the desire to be reunited with a loved one. "Raindrops on Banana Leaves" (雨打芭蕉) garnered 26% of the votes and conveys the beauty of nature after rain, emphasizing the appreciation of the world's beauty. "Nebula" (天空) was mentioned by 9% of the students, and this song includes lines from the poem "Love in the Sky" by Jiang Zhiqun. It explores themes of love and self-reflection, reflecting the desire to be with a loved one. "Crazy Heartbeats" (疯狂的心跳) received 18% of the votes, and the song uses phrases from the poem "Strong Feelings" by Junjian Lu. It portrays intense emotions, highlighting love and passion. "I Am by Your Side" (我在你左右), a song containing lines from the eponymous poem by Zhongsan Lu, was chosen by 9% of the students. It revolves around the theme of love and the longing to be with someone special. These findings illustrate how contemporary music in China often intertwines with classical poetry, bridging the gap between traditional literary heritage and modern forms of cultural self-expression. The results of the statistical analysis indicate a statistically significant difference between the control and experimental groups regarding the impact of multimedia technologies on learning outcomes.. The experiment has shown that these technologies contribute to better comprehension of the material and the attainment of higher scores compared to students who employed traditional teaching methods. Based on the study, conclusions can be drawn about the prospects of other processes related to this topic, namely the importance of using multimedia technologies to present poetry in online educational blogs, especially for interpreting poems by Chinese poets in modern Chinese music, such technologies will help to attract a wider audience and develop a person's creativity, linguistic culture, and sensual and aesthetic spheres. For further research, it will be essential to study the impact of multimedia technologies on the understanding and appreciation of Chinese poetry by students of different age groups and cultural backgrounds and to conduct a comparative analysis of various methods of interpreting Chinese poetry in multimedia format. The present findings can also be used to develop recommendations and practices for using multimedia technologies in education and cultural activities related to Chinese poetry and methods for applying multimedia technologies as the presentation of poetry in online blogs. [ABSTRACT FROM AUTHOR]

This research explores using blogs as an innovative assessment tool to enhance authentic learning and professional skill development in students. Unlike traditional methods, blogs foster active knowledge building and peer interaction, making learning more engaging and aligned with industry contexts. The study involved third-year planning students, in the course Governance and Planning, and utilised questionnaires, focus groups, and blog post analyses. Results indicated that blogs promoted reflective practice, facilitated peer review, and improved learning efficiencies. Despite some initial resistance and concerns about academic rigor, students found that blogging enhanced their understanding of course content and professional practices. Teacher support and peer feedback played a crucial role in this process. The literature supports blogs' effectiveness in motivating students and aligning learning activities with real-world applications. However, assumptions about students' familiarity with blogging were challenged, highlighting the need for thorough induction and support. Overall, blog-based assessments proved beneficial in creating authentic learning experiences and preparing students for their future careers. Future research should consider long-term studies on graduate outcomes and further explore peer review mechanisms. [ABSTRACT FROM AUTHOR]

Classifying user browsing behavior is an essential task to put suitable information on the web. Also, the browsing behavior is stored in the web server logs, which are used for identifying and classifying commonly assessed patterns of web users. This work designs a Butterfly-based Recurrent Neural Scheme (BbRNS) for accurate classification of the web user browsing behavior based on the URLs. It involves preprocessing, feature extraction, and classification. Generally, preprocessing removes the error, and feature extraction is employed to extract the web user browsing URLs. Then, updating the fitness function in the classification layer for accurate prediction of the browsing behavior of web users also enhances the performance of user behavior detection. Consequently, the developed framework is implemented using a Python tool, and the parameters of the current research work are evaluated with prevailing assignments. The designed model gained 98.8 accuracies, 97.5% recall, and 98% precision for predicting and classifying web user behavior. The experimental result shows improved accuracy for classifying web user browsing behavior with less error rate. [ABSTRACT FROM AUTHOR]

As a digital social medium, blogs have transformed into arenas where individuals can express their perspectives, concepts, and feelings. This research delves into the spread of information in blog posts related to the Uyghur discourse, utilizing topic modeling to classify and prioritize pertinent content. The analysis reveals a significant emphasis on the Chinese Government's suppression of Uyghur Muslims, as demonstrated by the hyperlinks embedded within the blog network. We mapped the discovered topics onto a network of hyperlinks, forming a topic-induced hyperlink network. Additionally, we conducted a toxicity analysis, which revealed a high level of toxicity in blog posts during that period. A morality assessment was also performed, revealing exceedingly high scores for all vices in the blog posts. Furthermore, we conducted an emotion analysis on the text and image data, which underscored a dominant sentiment of sadness pervading the blog posts and corresponding images. This reflects the severity of the Uyghur persecution. The research highlights the effectiveness of topic modeling, toxicity evaluation, and hyperlink analysis in shedding light on intricate social issues. Future studies could augment these results by expanding keyword scope and refining image recognition models within the context of Uyghur discourse. [ABSTRACT FROM AUTHOR]

Free Open-Access Medical Education (FOAMed) has transformed medical education in the past decade by complementing and substituting for traditional medical education when needed. The attractiveness of FOAMed resources is due to their inexpensive nature, wide availability, and user ability to access on demand across a variety of devices, making it easy to create, share, and participate. The subject of nephrology is complex, fascinating, and challenging. Traditional didactic lectures can be passive and ineffective in uncovering these difficult concepts and may need frequent revisions. Active teaching methods like flipped classrooms have shown some benefits, and these benefits can only be multifold with current social media tools. Social media will inspire the involvement of students and allow them to create and share educational content in a "trendy way," encouraging the participation of their peers and thus building an educational environment more conducive to them while promoting revision and retainment. FOAMed also promotes asynchronous learning, spaced learning, microlearning, and multimodal presentation with a meaningful variation. This article discusses the evolution of digital education, social media platforms, tools for creating and developing FOAMed resources, and digital scholarship. [ABSTRACT FROM AUTHOR]

Parents of children with cancer, who face significant financial and psychological burdens, often employ metaphors to convey their distinctive experiences and inner realms. However, these metaphors have received scant attention in prior research. Therefore, this study seeks to investigate the utilization of metaphors in blogs authored by parents of children with cancer, aiming to shed light on this understudied area. This study collected data from blogs authored by 33 parents of children with cancer spanning the period from 2019 to 2022, yielding a total of 181 metaphors. Drawing upon Conceptual Metaphor Theory, the method of qualitative content analysis was employed to analyze how parents of children with cancer utilize metaphors. This study found that "Game", "War", "Journey" and "Plant" metaphors were the most commonly used by parents of children with cancer. Game and war metaphors occupy a dominant position, followed by journey and plant metaphors. Additionally, these parents frequently employ mixed metaphors. The use of metaphorical language enables these parents to effectively articulate their distinct experiences and emotions. The use of metaphors may serve as a means for parents to articulate their feelings and motivate their children to actively fight cancer. By recognizing the metaphors embraced by these parents, we can gain a deeper understanding of their internal world and potentially offer support through both online and offline channels to mitigate their stress levels. [ABSTRACT FROM AUTHOR]

Diaries have been generally understood as "windows" on sense-making processes when studying life ruptures. In this article, we draw on Michel Foucault's conceptualization of self-writing as a "technology of the self" and on sociocultural psychology to propose that diaries are not "windows" but technologies that aid in the sense-making. Concretely, we analyzed three non-exhaustive and non-exclusive uses of diary writing in times of vulnerability: (1) imagination of the future and preparation to encounter difficulties; (2) distancing from one's own experience; and (3) creating personal commitments. Our longitudinal data comprised three public online diaries written over more than twenty years, belonging to three anonymous individuals selected from a database of more than 400 diaries. We analyzed these three diaries by iterating between qualitative and quantitative analysis. We conclude that: (1) beyond their expressive dimension, diaries are technologies that support the sense-making process, but not without difficulties; (2) diaries form a self-generated space for dialogue with oneself in which the diarist also becomes aware of the social nature of her life story; (3) diaries are not only technologies for the Socratic "know thyself" but also technologies to work on oneself, especially in terms of the personal perspective on the past or the future; and (4) the practice of diary writing goes beyond sense-making towards personal development and the desire to transform one's life trajectory. [ABSTRACT FROM AUTHOR]

In spite of the massive body of work reconceptualizing school mathematics in keeping with progressive approaches, research has shown that many school mathematics teachers still opt for more traditional methods. The present study sheds light on the mechanisms that shape mathematics teachers' knowledge, beliefs, and instructional practices and thereby sheds light on those factors that support or impede the adoption of more reform-based teaching methods in mathematics. By adopting a sociological perspective, the study explores the school mathematics culture based on reflective blogs published by high-school mathematics interns during the year of their professional and cultural socialization. A qualitative analysis of the blogs yielded 10 cultural codes, suggesting that these could be hegemonically reproduced and preserved by the schools' more experienced mathematics teachers, who also emerged as primary socialization agents during the interns' induction into the teaching profession. The findings are salient for teacher educators and mentors who aim to promote changes in the pedagogical practices in teaching mathematics in school. [ABSTRACT FROM AUTHOR]

Life-long exercise is essential in axial spondyloarthritis (axSpA) management; however, long-term adherence is challenging. Online exercise programmes are an alternative to face-to-face physiotherapy. (1) To measure adherence to a 12-month, individualised, online physiotherapy programme for people with axSpA, and investigate the effects on disease activity, spinal mobility, work ability, quality of life and function. (2) To investigate associations between programme adherence and outcomes. (3) To explore participants' views of the programme and factors affecting adherence. Participants were 'non-exercisers' recruited from rheumatology outpatient services. Adherence was measured using online diary entries. Outcomes included the BATH indices, health status (EQ5D), Ankylosing Spondylitis Quality of Life (ASQOL), exercise capacity (6MWT), Work, Productivity and Activity Impairment in AS (WPAI), Exercise Attitude Questionnaire (EAQ) and Exercise Motivations Inventory-2 (EMI-2) at baseline, 6 and 12 months. Interviews determined views on the intervention and factors affecting adherence. Fifty participants were recruited. Over the 52-week intervention, adherence (five times/week) ranged from 19% (± 30%) to 44% (± 35%). Significant improvements were found in disease activity (BASDAI), spinal mobility (BASMI), 6MWT, AsQoL and EQ5D-VAS at 6 and 12 months. There were no associations between adherence and baseline variables or demographics. Interviews suggested support from others, routine, and feeling the benefit positively affected adherence. Conversely, lack of motivation, life events and symptoms negatively affected adherence. A 12-month online physiotherapy programme significantly improved symptoms in people with axSpA who were not regular exercisers. Adherence reduced over the intervention period. Online exercise programmes may benefit people with axSpA; however, strategies to improve adherence are required. [ABSTRACT FROM AUTHOR]

Cybersecurity event detection aims to detect and classify the occurrence of cybersecurity events from a large amount of data. Previous approaches to event detection have used trigger word detection as the entry point for the task. However, constructing trigger words requires selecting words that describe the type of event occurrence from a large number of event sentences, which makes annotation of the training corpus time-consuming and expensive. To solve this issue, based on the latest achievements of contrastive learning in sentence embedding, we propose a novel method called Trigger-free Cybersecurity Event Detection Based on Contrastive Learning (TCEDCL), which incorporates semantics into the representation model to detect events without triggers. To demonstrate the feasibility of our proposed TCEDCL method, we collected and constructed a dataset from numerous cybersecurity news and blog posts. Extensive experiments have shown that our proposed method performs exceptionally well in detecting cybersecurity events and enables the extension of new types of event detection. [ABSTRACT FROM AUTHOR]

During the recent Covid-19 pandemic SMEs in the hospitality sector had to develop new ways of increasing consumer engagement and maintaining business activity. This study examines the effect of using blogs to counter the detrimental effect of pandemic lockdowns. A survey method was deployed with 449 respondents. Analysis used SEM PLS. The findings show trustworthiness and reputation positively affects credibility, but promotional incentives and expertise do not affect credibility. The findings also suggest that unverified information sharing mediates the relationship between credibility and loyalty. Information System (IS) researchers can systematically develop approach using big data to identify false information. This research contributes to knowledge of both IS researchers and SMEs in hospitality sector. SMEs in hospitality sector can partner IS and use this research as an example of method for recovering from crisis by the adoption of blog posts, as well as working remotely with IS researchers to explore data sources and research techniques to investigate false information. [ABSTRACT FROM AUTHOR]

In recent years, researchers from academic and industrial fields have become increasingly interested in social network data to extract meaningful information. This information is used in applications such as link prediction between people groups, community detection, protein module identification, etc. Therefore, the clustering technique has emerged as a solution to finding similarities between social network members. Recently, in most graph clustering solutions, the structural similarity of nodes is combined with their attribute similarity. The results of these solutions indicate that the graph's topological structure is more important. Since most social networks are sparse, these solutions often suffer from insufficient use of node features. This paper proposes a hybrid clustering approach as an application for link prediction in heterogeneous information networks (HINs). In our approach, an adjacency vector is determined for each node until, in this vector, the weight of the direct edge or the weight of the shortest communication path among every pair of nodes is considered. A similarity metric is presented that calculates similarity using the direct edge weight between two nodes and the correlation between their adjacency vectors. Finally, we evaluated the effectiveness of our proposed method using DBLP, Political blogs, and Citeseer datasets under entropy, density, purity, and execution time metrics. The simulation results demonstrate that while maintaining the cluster density significantly reduces the entropy and the execution time compared with the other methods. [ABSTRACT FROM AUTHOR]

When the city of Wuhan was severely locked down on January 23, 2020 for 76 days due to the coronavirus outbreak, many residents started writing "lockdown diaries." This article argues these diaries constitute a kind of performance art for their authors, specifically, an 'art of endurance' as described by Shalson (2018). Keeping a diary requires a plan, but the following through of the plan is a contingent process requiring efforts and endurance. The challenges become particularly daunting for authors of online diaries in pandemic times. The article analyzes multiple types of endurance associated with the Wuhan lockdown diarists, showing that in digitally-driven environments, where potential collective responses are a key context, the lockdown diaries of Wuhan, like works of endurance art, engage with meanings that reach far beyond their original experience and context. Their stories of endurance are an allegory of the endurance of the entire city of Wuhan. [ABSTRACT FROM AUTHOR]

Background: Objective: This study investigated the efficacy of a counselling video blog (vlog) from YouTube on depression reduction among primary school teachers in Nigeria. Methods: A 10-week group-randomized trial (GRT) was employed for this clinical investigation. Ninety-seven primary school teachers who had severe depression participated in the study. The authors used Beck's Depression Inventory-II (BDI-II) to collect data for the study. The authors used ANOVA with repeated measures to analyse the data collected in the study. Results: From the results the authors obtained, treatment and no-treatment group participants had severe depression at the initial assessment. However, exposure to a 10-week counselling vlog on YouTube reduced depression among participants in the treatment group, while those in the no-treatment control group still maintained severe depression. There was a significant reduction in depression among participants in the treatment group at the follow-up assessment, while no significant reduction was observed in the no-treatment control group at the follow-up assessment. Conclusion: From the findings of this study, YouTube channels should be utilized for different kinds of counselling. Specifically, counsellors should adopt vlog counselling in depression treatment. [ABSTRACT FROM AUTHOR]

Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the accuracy and efficiency of existing methods depend highly on the labeled web log data, countless web requests are generated daily with the help of web robots. Exhaustive and accurate manual labeling of reconstructed sessions is time-consuming and challenging. Further, effective detection of web robots is more challenging with unlabeled or partially labeled data. To address the aforementioned issues, we reformulated web robot detection as a semi-supervised learning problem. In this paper, we propose a deep learning-based Semi-Supervised Stacked Sparse AutoEncoder (Web-S4AE) for web robot detection. The proposed model uses content-based features and features extracted from web access log data to effectively classify web robots. The experiments were conducted on publicly available web log data from a library and information portal to assess the performance of Web-S4AE. The Web-S4AE model was trained in two phases. The first phase; comprises training the model with unlabeled data to extract the hidden information, and in the second phase, the model is fine-tuned using labeled data. The results suggest that incorporating more unlabeled data can significantly improve the classifier's performance. The Web-S4AE model's performance was also compared with other models such as the Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). [ABSTRACT FROM AUTHOR]

Background: Pharmacists have been co-located in general practice teams to support the quality use of medicines and optimise patient health outcomes. Evidence of the impact of pharmacist-led activities in Australian general practices is sparse. Aim: This study aimed to evaluate the potential outcomes of pharmacist-led activities in Australian general practices. Method: A prospective observational study was conducted in eight general practices in the Australian Capital Territory, where each general practice employed a pharmacist on a part-time basis for 18 months. A recommended, but flexible, list of activities was provided for pharmacists. Descriptive information on general practice pharmacist-led activities, collected with an online diary, was analysed. The potential clinical, economic, and organisational impact of pharmacist-led clinical activities was evaluated using the CLinical Economic Organisational (CLEO) tool, with a modified economic dimension. Results: Nine pharmacists reported 4290 activities over 3918.5 work hours in general practice. Medication management services were the primary clinical activity of pharmacists. In medication reviews, 75% of the pharmacists' recommendations were fully accepted by general practitioners. Conducting clinical audits, updating patients' medical records, and providing information to patients and staff were other major activities of pharmacists. Of 2419 clinical activities, around 50% had the potential for a moderate or major positive clinical impact on patients. Sixty-three per cent of activities had the potential to decrease healthcare costs. Almost all the pharmacist-led clinical activities had a positive organisational impact. Conclusion: Most pharmacist-led clinical activities in general practice had the potential for a positive impact on patients and reduction in healthcare costs, supporting the expansion of this model in Australia. [ABSTRACT FROM AUTHOR]

Detecting automated users' agent activities at any web application through users' web access logs is a challenging issue. Many machines learning based automated solutions exist to address this issue. However, the existing supervised learning methods are heavily dependent on fully labeled data. But the scarcity of labeled log access data and the cost of labeling still make the issue challenging. Some unsupervised learning-based solutions are also proposed, but their performance accuracy is questionable. The semi-supervised based self-training method works with a small set of partially labeled data but lacks a suitable selection metric for a set of predictions with a high degree of confidence and a reliable base learner. In this paper, we propose a new semi-supervised learning based self-training method using probability-based selection criteria with Mahalanobis distance, named DIstance-based SElf-Training (DISET) for detecting automated users' agent activities. The DISET used probability-based selection criteria with Mahalanobis distance to achieve high-confidence subset selection. The DISET framework works in four steps. First, it performs the data cleaning, session identification, feature extraction, and session labeling during the data preprocessing step. The second step segments the data into labeled and unlabeled datasets. The third step of model self-training performs the subset selection using six different supervised base learners independently. Lastly, the fourth step tests the performance of the used model. The performance of DISET is evaluated on NASA95 and E-commerce weblog datasets using three-fold cross-validation training and testing. The used datasets are also divided into different ratios of labeled and unlabeled instances for experiments. The performance is recorded on the accuracy, precision, recall, and the f-1 score, and the Matthews Correlation Coefficient (MCC) measures and compares the model's performance with six different base classifiers. We also plotted the ROC and PR curves to confirm and compare the performance of different base learners with the DISET method. Out of the six-base learners, XGBoost outperformed both datasets in the 30:70 data segmentation ratio. The results show that DISET achieves a minimum percentage improvement of 1.91% in accuracy, 2.70% in precision, 3.65% in sensitivity, and 1.00% in F-1 score with large unlabeled datasets. [ABSTRACT FROM AUTHOR]

Sugar relationships are described most commonly as negotiated relationships, usually between an older (sugar parent) partner and younger (sugar baby) partner and in which the younger partner is provided monetary or material gifts for their participation in the relationship (e.g., companionship, friendship, consensual sexual behavior). Sugar dating crosses the boundaries of not only what is considered a traditional relationship. Previous scholarship considers the legality of sugar relationships, negotiations in sugar relationships, power dynamics associated with sugar relationships, and if a transactional nature often seen as wedded to sugar dating can be seen as a form of sex work. The current study focused on advice sharing within Tumblr communities of sugar babies, including sugar-dating specific language. Tumblr, a popular blogging website, houses a prolific subculture for sugar dating, wherein blogs provide information/advice to potential or novice sugar babies about how to navigate the sugar experience. Using conventional content analysis, we report nine themes from blog posts of sugar babies. These themes include sugar dating advice, relationship maintenance once a sugar relationship begins, and what to expect in a sugar dating relationship. We discuss how the open and transactional components of sugar dating are not wholly dissimilar from other dyadic relationships. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-024-74427-z.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-74427-z, published online 15 October 2024In the original version of this Article, Tiantian Li was incorrectly listed as a corresponding author. The correct corresponding author for this Article is Fang Liu. Correspondence and request for materials should be addressed to 0200116@bbmc.edu.cn.The original Article has been corrected.By Tiantian Li; Fang Liu; Xiaobin Chen and Chao MaReported by Author; Author; Author; Author [Extracted from the article]

Objective: To examine whether exposure to safe sleep recommendations using a blog format changed infant sleep practices. Methods: We conducted a pilot randomized controlled trial via Qualtrics, a web-based platform, with a national sample of parents of children < 1 year old. Survey questions about infant sleep practices included: bed-sharing, location, position and objects present. Safe sleep was defined as not bed-sharing, in a crib, bassinet or playard, back positioning, and no other objects present except pacifiers. Participants were randomized to read one of the following: (1) pediatrician blog post, (2) parent blog post, or (3) no blog post. The blog posts contained the same content about infant sleep but varied by identified authorship. All participants received links to online content about safe sleep. Participants received a follow-up survey 2–4 weeks later with the same questions about infant sleep practices. We compared responses in pre- and post-surveys by type of blog post exposure using multivariable logistic regression models. Results: The average infant age (n = 1500) was 6.6 months (Standard Deviation 3.3). Most participants (74%) were female; 77% were married; 65% identified as white Non-Hispanic, 12% were black and 17% were Hispanic. 47% (n = 711) completed both surveys. We identified no differences in the odds of any of the four safe sleep practices after exposure to safe sleep recommendations in blog post format. Conclusion: Although in-person advice has been associated with improved safe sleep practices, we did not identify changes in infant sleep practices after exposure to safe sleep advice using blog posts. [ABSTRACT FROM AUTHOR]

This is the first study to investigate the impact of increased access to ovulation tests in "lowest-low" fertility populations. We use data from a randomized controlled trial involving 657 heterosexual married women in Singapore, one of the world's lowest fertility countries. Half of the participants were randomly selected to receive ovulation test kits, which provided personalized information on the timing of their fertile window. 97.72% of participants completed online diaries recording dates of intercourse, menstrual cycles and test results over the next 14 weeks. Random effects regression model results provide evidence that married couples who have not yet achieved their fertility ideals at the time of intervention respond by being significantly more likely to have sex on days with positive test results. Moreover, they only do so on the exact day of positive test results, not on the day before or after. Increasing public access to ovulation tests may allow couples to more accurately time intercourse within the fertile window, potentially helping couples in lowest-low fertility settings to achieve their fertility ideals. However, when wives' and husbands' fertility ideals are perceived to be non-aligned, wives strategically use the information to maximize the likelihood of realizing their own ideals. Given the importance of both parents in childbearing, policymakers should carefully consider the implications of such interventions. [ABSTRACT FROM AUTHOR]

Provided are empirical answers to questions about what conscious emotions underlie union intolerance and what elements of these emotions relate to willingness to join a union. We draw from the racial intolerance literature, with attention to emotions listed by religious institutions as intolerable vices. We also draw from personal websites and blogs authored by employees who espouse union hatred to identify vices seen as encouraged by unions. With this source material in hand, union intolerance is defined as sustained hatred evoked when unions are perceived by nonunion employees to encourage intolerable vices, with union members envisioned as first-line recipients of such encouragement. Turning to the natural sciences, we evoked inductive processes to identify representative vices and to construct a union intolerance scale. With samples of nonunion employees (Ns = 262, 267), a scalable dimension is revealed. Our deductive work draws from: (a) a heuristic framework, in which conscious feelings are thought to evoke an emotional readiness to act; and (b) psychodynamics, in which unacceptable emotions are thought to be projected onto targeted others. With these conceptualizations, a prediction model is advanced, with willingness to join positioned as an outcome of readiness to act stemming from intolerance. Using the sample data, a hypothesized negative relationship is shown, in which diminished willingness to join is associated with more intolerance. Contextualized in reference to the union history of sampled employees, the negative relationship is also shown as intensified in relation to claims of union discrimination. Directions for future research and implications for union recruitment are discussed. [ABSTRACT FROM AUTHOR]

In a previous study we detected that a number of inquiry stages (data collection, analysis and conclusions) went unnoticed by the students of an in situ joint online/onsite master's degree via online teaching. In this paper we analyse the effect of improved instruction, in which students fully experienced and became aware of all the stages that comprise the inquiry-based teaching approach. In the article we show the differences between the initial and improved instruction. The comparison of student comments as exhibited in the online class diary forum between the initial and improved instruction has allowed us to analyse the influence of this improvement in the level of depth of the students' discourse. Two codings have been employed to analyse the forums: the first (deductive) detected which stages of inquiry appeared in the comments. The second (inductive) involved the recoding of each of the previously classified comments based on five levels of communicative quality that emerged. Our main finding was that as well as being more aware of the different stages of inquiry, the students of the improved investigation were able to explain and identify them with specific examples. In other words, the investment of time in developing each of the stages in question helped them to define, afford reality to, and increase the explicative quality of their comments. [ABSTRACT FROM AUTHOR]

This opinion piece emerged from research for the book, Story Machines: How Computers Have Become Creative Writers, by Mike Sharples and Rafael Pérez y Pérez, published by Routledge. While thinking of ways to promote the book, I realised that students could employ AI story generators to write essays. That led me to research automated essay writing, write a Twitter thread that has garnered 43,000 engagements, and author a blog article (Sharples, 2022). The current piece is a revised and expanded version of that article. [ABSTRACT FROM AUTHOR]

Due to the innovation in technology, various techniques and tools are used to generate, collect, and store the data arriving at high speed and in huge volume. Stream data is the well-ordered sequence of items that arrive in a timely manner. The various sources of stream data are mobile devices, sensor nodes, network monitoring, traffic management, weblogs, etc. This paper aims at mining data stream with and without concept drift using the supervised machine learning technique in massive online analysis (MOA) frame work. The existing challenges in handling the stream data are improving the efficiency of the learners, effective memory management, reducing time taken and the way of handling the drifted data. The intrusion datasets are streamed using MOA framework and classification is performed using Naive Bayes, hoeffding tree, and ensemble algorithms, with and without drifted data. It is found that ensemble algorithm outperforms the other similar algorithms. [ABSTRACT FROM AUTHOR]

Current era is witnessing data explosion being generated from a wide range of resources including RFID (Radio-frequency identification), sensors, web logs, social media, IoT (Internet of Things) devices and many more. Pace at which data is being generated routinely in all the task performed by us has overwhelmed the proficiency and working of present infrastructure and analytical solutions available. Data has become the driving force of economy and has been treated as an asset for an organization. It contains truth or facts that can be interpreted and manipulated to gain insight for knowledge discovery. To excel out in competition enterprises are escalating their big data projects for knowledge discovery to gain valuable insights. These projects require scalable architectures for storage and data processing. Data-centric technologies are gaining impetus which can be provisioned as service to the organizations. Cloud computing is an effective and promising solution for refined analytical application. Cloud computing model supports resources to be provisioned as service. Herein paper we examine the requirements for provisioning Big Data Knowledge Discovery as a service. In addition, we explore the prevalent big data frameworks accessible and provisioned as a service via cloud. We also explore the state-of-the- art progress in this arena with open challenges and research prospects. [ABSTRACT FROM AUTHOR]

Dynamic text documents, including news articles, user reviews, and blogs, are now commonly encountered in many fields. Accordingly, the topics underlying text streams also change over time. To grasp the topic changes in the increasing accumulation of text documents, there is a great need to develop automatic text analysis models to find the key changes in topics. To this end, this study proposes a topic change point detection (Topic-CD) model. Different from previous studies, we define the change point of topics from the perspective of hyperparameters associated with topic-word distributions. This allows the model to detect change points underlying the whole topic set. Under this definition, the topic modeling and change point detection are combined in a unified framework and then performed simultaneously using a Markov chain Monte Carlo algorithm. In addition, the Topic-CD model is free from setting the number of change points in advance, which makes it more convenient for practical use. We investigate the performance of the Topic-CD model numerically using synthetic data and three real datasets. The results show that the Topic-CD model can well identify the change points in topics when compared with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Narrative analysis, creative writing, and interactive reflective writing have been identified as valuable for professional identity formation and resilience among medical and premedical students alike. This study proposes that medical student blogs are novel pedagogical tools for fostering peer-to-peer learning in academic medicine and are currently underutilized as a near-peer resource for premedical students to learn about the medical profession. To evaluate the pedagogical utility of medical student blogs for introducing core themes in the medical humanities, the authors conducted qualitative analysis of one hundred seventy-six reflective essays by baccalaureate premedical students written in response to medical student-authored narrative blog posts. Using an iterative thematic approach, the authors identified common patterns in the reflective essays, distilled major themes, coded the essays, and conducted narrative analysis through close reading. Qualitative analysis identified three core themes (empathic conflict, bias in healthcare, and the humanity of medicine) and one overarching theme (near-peer affinities). The premedical students' essays demonstrated significant self-reflection in response to near-peer works, discussed their perceptions of medical professionalism, and expressed concerns about their future progress through the medical education system. The essays consistently attributed the impact of the medical student narratives to the authors' status as near-peers. The authors conclude that reading and engaging in reflective writing about near-peer blog posts encourages premedical students to develop an understanding of core concepts in the medical humanities and promotes their reflection on the profession of medicine. Thus, incorporating online blogs written by medical trainees as narrative works in medical humanities classrooms is a novel pedagogical method for fostering peer-to-peer learning in academic medicine. [ABSTRACT FROM AUTHOR]

Since its emergence, neuromarketing has generated multiple debates about its uses and misuses, the ethics of this highly sensitive data technique, and the lack of regulation for consumer protection. To explore how consumers perceive neuromarketing, a content analysis of user-generated content (UGC) on blogs and social media was conducted. The main purpose of this research was to identify consumers' opinions about neuromarketing, its perceived benefits and risks, and the ethical concerns and implications for those involved in the use of this method. Taking a social media mining approach, the study uncovers the most common neuromarketing concerns discussed by consumers, describes the main participants (or actors) of the neuromarketing narrative and their interrelationships, and evaluates the overall consumer sentiment of neuromarketing. Six main themes and eight actors emerged from the exploratory analysis. Furthermore, the sentiment analysis demonstrated a positive perception of neuromarketing among consumers, but it also revealed some concerns about the lack of regulation and consumer privacy. The study concludes with a discussion of implications for future research. [ABSTRACT FROM AUTHOR]

A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by (1) developing and validating a comprehensive taxonomy of climate contrarianism, (2) conducting the largest content analysis to date on contrarian claims, (3) developing a computational model to accurately classify specific claims, and (4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to construct a detailed history of claims over the past 20 years. Our study finds that the claims utilized by CTTs and contrarian blogs have focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims. [ABSTRACT FROM AUTHOR]

Fluctuation scaling (FS) and anomalous diffusion have been discussed in different contexts, even though both are often observed in complex systems. To clarify the relationship between these concepts, we investigated approximately three billion Japanese blog articles over a period of six years and analyzed the corresponding Poisson process driven by a random walk model with power-law forgetting, which reproduces both the anomalous diffusion and the FS. From the analysis of the model, we have identified the relationship between the time-scale dependence of FS and characteristics of anomalous diffusion and showed that the time-scale-independent FS corresponds to essentially a logarithmic diffusion (i.e., a kind of ultraslow diffusion). In addition, we confirmed that this relationship is also valid for the actual data. This finding may contribute to the discovery of actual examples of ultraslow diffusion, which have been nearly unobserved in spite of many mathematical theories, because we can detect the time-scale-independent FS more easily and more distinctly than through direct detection of the logarithmic diffusion based on the mean squared displacement. [ABSTRACT FROM AUTHOR]

Internet users heavily rely on web search engines for their intended information. The major revenue of search engines is advertisements (or ads). However, the search advertising suffers from fraud. Fraudsters generate fake traffic which does not reach the intended audience, and increases the cost of the advertisers. Therefore, it is critical to detect fraud in web search. Previous studies solve this problem through fraudster detection (especially bots) by leveraging fraudsters' unique behaviors. However, they may fail to detect new means of fraud, such as crowdsourcing fraud, since crowd workers behave in part like normal users. To this end, this paper proposes an approach to detecting fraud in web search from the perspective of fraudulent keywords. We begin by using a unique dataset of 150 million web search logs to examine the discriminating features of fraudulent keywords. Specifically, we model the temporal correlation of fraudulent keywords as a graph, which reveals a very well-connected community structure. Next, we design DFW (detection of fraudulent keywords) that mines the temporal correlations between candidate fraudulent keywords and a given list of seeds. In particular, DFW leverages several refinements to filter out non-fraudulent keywords that co-occur with seeds occasionally. The evaluation using the search logs shows that DFW achieves high fraud detection precision (99%) and accuracy (93%). A further analysis reveals several typical temporal evolution patterns of fraudulent keywords and the co-existence of both bots and crowd workers as fraudsters for web search fraud. [ABSTRACT FROM AUTHOR]

In the new era of big data, numerous information and technology systems can store huge amounts of streaming data in real time, for example, in server-access logs on web application servers. The importance of anomaly detection in voluminous quantities of streaming data from such systems is rapidly increasing. One of the biggest challenges in the detection task is to carry out real-time contextual anomaly detection in streaming data with varying patterns that are visually detectable but unsuitable for a parametric model. Most anomaly detection algorithms have weaknesses in dealing with streaming time-series data containing such patterns. In this paper, we propose a novel method for online contextual anomaly detection in streaming time-series data using generalized extreme studentized deviates (GESD) tests. The GESD test is relatively accurate and efficient because it performs statistical hypothesis testing but it is unable to handle streaming time-series data. Thus, focusing on streaming time-series data, we propose an online version of the test capable of detecting outliers under varying patterns. We perform extensive experiments with simulated data, syntactic data, and real online traffic data from Yahoo Webscope, showing a clear advantage of the proposed method, particularly for analyzing streaming data with varying patterns. [ABSTRACT FROM AUTHOR]

Background and Aims: Patient-directed information on celiac disease has been reported to be of variable quality. We assessed the quantity and quality of information on blogs and Web sites intended to inform the layperson of celiac disease information. Methods: We performed a cross-sectional study analyzing celiac disease blogs and Web sites intended for the layperson. We searched from 20 cities, resulting in 55 Web sites. These sites were analyzed for 38 criteria that considered relevant clinical information for people with celiac disease. Claims were classified as true, false, or not proven. The readability level of each Web site was determined. Results: The 55 Web sites were categorized as national organizations, personal blogs, recipe-based blogs, or commercial/marketing Web sites. Only 40% of Web sites contained more than 50% of criteria. Of 212 claims assessed, 97% were found to be accurate. National organizations included the most criteria, followed by recipe-based blogs, then personal blogs, and lastly commercial/marketing Web sites. Additionally, national organizations had the highest proportion of accurate claims, followed by personal blogs, then commercial/marketing Web sites, and recipe-based blogs with the most inaccurate information. The average readability level of overall was 9.7, above the recommended readability level for patient education materials. Conclusions: A significant number of online claims regarding celiac disease were true, but the majority of patient-facing Web sites are missing large amounts of relevant information. This warrants efforts to improve the quality of medical information published online. [ABSTRACT FROM AUTHOR]

Social media is undoubtedly a widely used communication platform that affords easier sharing and access to information. Even though social media is used for benevolent purposes, a few use this platform for deviant activities such as cyberbullying, cyber warfare or propaganda, disinformation and fake news dissemination to influence the masses. With the availability of inexpensive and ubiquitous mass communication tools like social media, disseminating false information and propaganda is both convenient and effective. Social media in general and blogs in particular act as virtual spaces where narratives are framed. In order to generate discourse, web traffic needs to be driven to these virtual spaces. Social media platforms such as Twitter, Facebook, and other websites are therefore used as vehicles to disseminate the content. This link between blogs and social media platforms is vital to examine disinformation campaigns. In this research, we examine the role of media orchestration strategies, more specifically, cross-media and mix-media strategies in conducting disinformation campaigns. The paper presents an in-depth examination of the information networks using social network analysis and cyber forensic-based methodology, to identify prominent information actors and leading coordinators of the disinformation campaigns. Using the developed research methodology, the study reveals a massive disinformation coordination campaign pertaining to the Baltic region conducted primarily on blogs but strategically linking to a variety of other social media platforms, e.g., Twitter, Facebook, YouTube, VKontakte, among others. [ABSTRACT FROM AUTHOR]

This study investigates how tablet users react when technology falls short of their expectations. We deploy a data/frame model to study this process and investigate resistance-related reactions and the deployment of accommodating practices at the individual level. Analyzing user blogs that provide narratives on user interaction with tablets, we identify triggers of episodes of disillusionment and illustrate five sensemaking paths that users follow, eventually leading to one of three practices: 1) users choose to defer tasks until the situation changes, or they abandon the platform altogether; 2) they develop workarounds at different levels of proficiency; or 3) they proceed by reframing their expectations of the platform. By revealing user decision-making process during episodes of disillusionment, the findings contribute to information systems post-adoption research. At a practical level, the findings inform IT artifact and application design by offering insights on how users process discrepancies between their expectations and actual use experience. [ABSTRACT FROM AUTHOR]

Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism. Online news websites around the world are using this medium to give their readers a minute by minute update on an event. Good summaries enhance the value of the live blogs for a reader, but are often not available. In this article, (a) we first define the task of summarizing a live blog, (b) study ways of automatically collecting corpora for live blog summarization, and (c) understand the complexity of the task by empirically evaluating well-known state-of-the-art unsupervised and supervised summarization systems on our new corpus. We show that live blog summarization poses new challenges in the field of news summarization, since frequency and positional signals cannot be used. We make our tools publicly available to reconstruct the corpus and to conduct our empirical experiments. This encourages the research community to build upon and replicate our results. [ABSTRACT FROM AUTHOR]

Collection of web behavior relies on self-report and web logs in daily life. The self-report is derived on memory and can seem ambiguous for analyzing web behavior. It is necessary to quantitatively measure these web behaviors to see the changes over time. Individuals tied by the same emotion become a group, and its members synchronize with each other. Therefore, this study is to propose a web behavior model and its collecting and analyzing methods. Additionally, this study is to create a group using the synchronization of behavior patterns and social networking to provide insight into enhanced marketing. [ABSTRACT FROM AUTHOR]

Predicting the browsing behavior of the user on the web has gained significant importance, as it improves the productivity of the website owners and also raises the interest of web users. The Markov model has been used immensely for user's web navigation prediction. To enhance the coverage and accuracy of the Markov model, higher order Markov models are integrated with lower order models. However, this integration results in large state-space complexity. To reduce the state-space complexity, this paper proposes a novel technique, namely Pruned all-Kth modified Markov model (PKM3). PKM3 eliminates the irrelevant states from a higher order model, which have a negligible contribution toward prediction. The proposed model is evaluated on four standard weblogs: BMS, MSWEB, CTI and MSNBC. PKM3 performance was optimal for the website in which pages were closely placed and share high interlinking. This pruning-based optimal model achieves a significant reduction in state-space complexity while maintaining comparable accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Publizistik: Vierteljahreshefte für Kommunikationsforschung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article proposes an implicit mood computing system. The implicit mood computing task is a part of affective computing. Previous works in affective computing mostly focus on twitters, blogs, movie interviews, and news corpus. These works detect sentiment polarity (positive/negative), emotion types (joy, sadness, anger, etc.), or mood types (boring, tired, happy, etc.) of the text. Different from previous studies, our work focuses on the literature texts and detects the implicit mood of them. The implicit mood is sometimes discussed as the tone or the atmosphere of the text. The implicit mood is an important affective feature in the literature such as poetry, prose, and drama. Our work regards the implicit mood as a semantic phenomenon. We capture the feature of implicit mood via a semantic mapping approach and the long short-term memory neural network. The proposed system is capable of identifying 12 kinds of implicit moods with a promising result. [ABSTRACT FROM AUTHOR]

Self-regulated learning (SRL) is associated with increased academic achievement and improved learning outcomes for students. Thus, it is import to find ways to improve SRL, such as through training. Face-to-face training, discipline-dependent training, and paper-and-pencil diaries are limited in the number of students they can reach. The current randomised control study implemented discipline-independent online training and novel mobile-app based diaries and tested SRL motivation and perceived strategy use in 73 University students from mixed disciplines and study mode. Results showed that participants in the combined condition (training with diaries) improved more than other conditions. Specifically, they improved on SRL knowledge, metacognitive strategies, cognitive strategies (elaboration, organisation and critical thinking), and resources management strategies (time-management and effort regulation). The present study extends previous findings, showing that positive effects can be found for SRL when a discipline-independent approach is used coupled with online training and a mobile-app based daily diary. [ABSTRACT FROM AUTHOR]

This review essay examines the emergence of the patient narrative or "pathography" in the late eighteenth and early nineteenth century in relation to the great cultural, epistemological, and ethical transformations that enabled the formation of modern medicine. John Wiltshire's book provides an historical overview of this complex process, as well as laying the basis for a contemporary critique of some of its key assumptions. [ABSTRACT FROM AUTHOR]