Because social proof has become such a mainstay in marketing, it's easy for marketers to grow numb to it. You forget that social proof is one of Cialdini's most persuasive principles. So here are 5 new studies to remind you about the persuasive power of social proof.

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Have you ever felt pressured to be the perfect wife, mom, and Christian? We don't have to be Martha Stewart, but we can give God what we have.

First, let me tell you a little story involving butterflies and bladders. I've spent the past few days at MicroConf. Just a phenomenal conference, full of like-minded folks, loaded with actionable content. Not to get too carried away, but it...

Larne defender Tomas Cosgrove gives his predictions for this weekend's Irish Premiership games.

A review of the front page stories from the daily newspapers in Scotland.

The Highland club had previously said the energy project could raise funds for community projects.

The BBC's longest-serving gardening presenter was a favourite with audiences for 41 years before retiring in 2019.

Registered childcare providers in Wales won't have to pay business rates, says the Welsh government.

A series of sessions are being held to explain how a Business Improvement District (BID) could work.

Residents say the two-storey home would cause parking issues.

Residents raise concerns about the plans in an east Suffolk village.

Councillors water down and approve plans to expand operating times of a bus lane in the Devon city.

The plans to bring in charges led to protests in some areas and outside council meetings.

Watch highlights as a Kyle Wootton goal in extra time gives League One Stockport County a narrow victory over National League high-fliers Forest Green Rovers in the first round of the FA Cup.

Women's safety and religious groups speak out against the club, which is popular during race meets.

Indigenous Australians says the book is damaging and stereotypes first nation people.

A watchdog says a fire service is taking "significant steps" to address concerns raised a year ago.

Three mainstream schools in Herefordshire will benefit from the grant and a SEND school will be built.

New road markings are being installed on the slip road following a fatal crash in September.

All about the Bibby Stockholm barge, which is moored in Portland, Dorset.

Bristol Rovers boss Matt Taylor says it will be a moment of "togetherness" as fans plan support for players who have suffered racist abuse.

Formula 1 drivers urged the operators of the Interlagos circuit to improve the new surface they laid ahead of this year's event.

The controversy which surrounded Red Bull team principal Christian Horner earlier this year "had an impact" on their staff, according to a long-serving ex-F1 engineer.

This post discusses an update to Search Console's user and permissions to improve the accuracy and reflect the actual state of unused ownership tokens.

Ponencia presentada por Julia Benseñor en el III CONGRESO LATINOAMERICANO DE TRADUCCIÓN E INTERPRETACIÓN, CTPBA, Buenos Aires, abril de 2001

Recently, IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX for short) has provided us with low-cost, high efficiency and high bandwidth network services. However, as with the WiFi, the radio wave transmission also makes the WiMAX face the wireless transmission security problem. To solve this problem, the IEEE802.16Std during its development stage defines the Privacy Key Management (PKM for short) authentication process which offers a one-way authentication. However, using a one-way authentication, an SS may connect to a fake BS. Mutual authentication, like that developed for PKMv2, can avoid this problem. Therefore, in this paper, we propose an authentication key management approach, called Diffie-Hellman-PKDS-based authentication method (DiHam for short), which employs a secret door asymmetric one-way function, Public Key Distribution System (PKDS for short), to improve current security level of facility authentication between WiMAX's BS and SS. We further integrate the PKMv1 and the DiHam into a system, called PKM-DiHam (P-DiHam for short), in which the PKMv1 acts as the authentication process, and the DiHam is responsible for key management and delivery. By transmitting securely protected and well-defined parameters for SS and BS, the two stations can mutually authenticate each other. Messages including those conveying user data and authentication parameters can be then more securely delivered.

1. ‘Been a long time since I felt that way’: Sexually transmitted infection numbers provide new hope  POLITICO
2. There are millions of sexually transmitted infections in the US every year, but new data shows the epidemic may be slowing  CNN
3. America's STD explosion laid bare and the shocking number of people catching one every minute  Daily Mail
4. At Long Last, the Surge in S.T.I.s May Be Leveling Off  The New York Times

Physical Layer Security (PLS) and relay technology have emerged as viable methods for enhancing the security of wireless networks. Relay technology adoption enhances the extent of coverage and enhances dependability. Moreover, it can improve the PLS. Choosing relay and jammer nodes from the group of intermediate nodes effectively mitigates the presence of powerful eavesdroppers. Current methods for Joint Relay and Jammer Selection (JRJS) address the optimisation problem of achieving near-optimal secrecy. However, most of these techniques are not scalable for large networks due to their computational cost. Secrecy will decrease if eavesdroppers are aware of the relay and jammer intermediary nodes because beamforming can be used to counter the jammer. Consequently, this study introduces a multi-agent Q-learning-based PLS-enhanced secured joint relay and jammer in dual-hop wireless cooperative networks, considering the existence of several eavesdroppers. The performance of the suggested algorithm is evaluated in comparison to the current algorithms for secure node selection. The simulation results verified the superiority of the proposed algorithm.

Increasingly, numerous universities have identified benefits of flipped learning environments and have been encouraging instructors to adapt such methodologies in their respective classrooms, at a time when departments are facing significant budget constraints. This article proposes an instructional design framework utilized to strategically enhance traditional flipped methodologies in a first-year engineering course, by using low-cost technology aids and proven pedagogical techniques to enhance student learning. Implemented in a first-year engineering course, this modified flipped model demonstrated an improved student awareness of essential engineering concepts and improved academic performance through collaborative and active learning activities, including flipped learning methodologies, without the need for expensive, formal active learning spaces. These findings have been validated through two studies and have shown similar results confirming that student learning is improved by the implementation of multi-pedagogical strategies in-formed by the use of an instructional design in a traditional classroom setting.

Aim/Purpose: Improving public schools is a focus of federal legislation in the United States with much of the burden placed on principals. However, preparing principals for this task has proven elusive despite many changes in programming by institutions of higher learning. Emerging technologies that rely on augmented and virtual realities are posited to be powerful pedagogical tools for closing this gap. Background: This study investigated the effects of immersive simulation technologies on principals’ self-efficacy after treatment and the perceived significance of the design of the immersive simulation experience as an effective tool for adult learners. Methodology: The investigator employed a multiple-methods study that relied on a purposive sample of graduate students enrolled in educational leadership programs at two small universities in the southeastern United States. Participants completed a two-hour module of immersive simulation designed to facilitate transfer of knowledge to skills thereby increasing their self-efficacy. Contribution: This paper contributes to a small body of literature that examines the use of immersive simulation to prepare aspiring principals. Findings: The findings indicate moderate effect sizes in changes in self-efficacy, positive attitudes toward immersive simulation as a pedagogical tool, and significance in the design of immersive simulation modules. This suggests that immersive simulation, when properly designed, aids principals in taking action to improve schools. Recommendations for Practitioners: Educational leadership programs might consider the use of immersive simulations to enhance principals’ ability to meet the complex demands of leading in the 21st century. Impact on Society: Principals may be more adept at improving schools if preparation programs provided consistent opportunities to engage in immersive simulations. Future Research: Future research should be conducted with larger sample sizes and longitudinally to determine the effectiveness of this treatment.

Aim/Purpose: The purpose of this study is to develop an efficient methodology that can assist the evaluators in assessing a variable number of individuals that are working in groups and guarantee that the assessment is dependent on the group members’ performance and contribution to the work developed. Background: Collaborative work has been gaining more popularity in academic settings. However, group assessment needs to be performed according to each individual’s performance. The problem rests on the need to distinguish each member of the group in order to provide fair and unbiased assessments. Methodology: Design Science Research methodology supported the design of a framework able to provide the evaluator with the means to distinguish individuals in a workgroup and deliver fair results. Hevner’s DSR guidelines were fulfilled in order to describe WebAVALIA. To evaluate the framework, a quantitative study was performed and the first results are presented. Contribution: This paper provides a methodological solution regarding a fair evaluation of collaborative work through a tool that allows its users to perform their own assessment and peer assessment. These are made accordingly to the user’s perspectives on the performance of each group member throughout the work development. Findings: The first analysis of the results indicates that the developed method provides fairness in the assessment of group members, delivering a distinction amongst individuals. Therefore, each group member obtains a mark that corresponds to their specific contribution to the workgroup. Recommendations for Practitioners: For those who intend to apply this workgroup assessment method, it is relevant to raise student awareness about the methodology that is going to be used. That is, all the functionalities and steps in WebAVALIA have to be thoroughly explained before beginning of the project. Then, the evaluators have to decide about the students’ intermediate voting, namely if the evaluator chooses or not to publish student results throughout the project’s development. If there is the decision to display these intermediate results, the evaluator must try to encourage collaboration among workgroup members, instead of competition. Recommendation for Researchers: This study explores the design and development of an e-assessment tool – WebAVALIA. In order to assess its feasibility, its use in other institutions or contexts is recommended. The gathering of user opinions is suggested as well. It would then be interesting to compare the findings of this study with the results from other experimentations Impact on Society: Sometimes, people develop a rejection of collaborative work because they feel exploited due to the biased evaluation results. However, the group members assessment distinction, according to each one’s performance, may give each individual a sense of fairness and reward, leading to an openness/willingness towards collaborative work. Future Research: As future work, there are plans to implement the method in other group assessment contexts – such as sports and business environments, other higher education institutions, technical training students – in other cultures and countries. From this myriad of contexts, satisfaction results would be compared. Other future plans are to further explore the mathematical formulations and the respective WebAVALIA supporting algorithms.

Aim/Purpose: The aim of the study is to test whether the perception of self-regulated learning during text reading in online teacher education is improved by using a digital tool for the use of metacognitive strategies for planning, monitoring, and self-assessment. Background: The use of self-regulated learning is important in reading skills, and for students to develop self-regulated learning, their teachers must master it. Therefore, teaching strategies for self-regulated learning in teacher education is essential. Methodology: The sample size was 252 participants with the tool used by 42% or the participants. A quasi-experimental design was used in a pre-post study. ARATEX-R, a text-based scale, was used to evaluate self-regulated learning. The 5-point Likert scale includes the evaluation of five dimensions: planning strategies, cognition management, motivation management, comprehension assessment and context management. A Generalized Linear Model was used to analyse the results. Contribution: Using the tool to self-regulate learning has led to an improvement during text reading, especially in the dimensions of motivation management, planning management and comprehension assessment, key dimensions for text comprehension and learning. Findings: Participants who use the app perceive greater improvement, especially in the dimensions of motivation management (22,3%), planning management (19.9%) and comprehension assessment (24,6%), which are fundamental dimensions for self-regulation in text reading. Recommendations for Practitioners: This tool should be included in teacher training to enable reflection during the reading of texts, because it helps to improve three key types of strategies in self-regulation: (1) planning through planning management, (2) monitoring through motivation management and comprehension assessment, and (3) self-assessment through comprehension assessment. Recommendation for Researchers: The success of the tool suggests further study for its application in other use cases: other student profiles in higher education, other teaching modalities, and other educational stages. These studies will help to identify adaptations that will extend the tool’s use in education. Impact on Society: The use of Metadig facilitates reflection during the reading of texts in order to improve comprehension and thus self-regulate the learning of content. This reflection is crucial for students’ knowledge construction. Future Research: Future research will focus on enhancing the digital tool by adding features to support the development of cognition and context management. It will also focus on how on adapting the tool to help other types of learners.

Badminton, as a fast and highly technical sport, requires high accuracy in identifying athletes' swing movements. Accurately identifying different swing movements is of great significance for technical analysis, coach guidance, and game evaluation. To improve the recognition accuracy of badminton players' swing movements, this text is based on an improved dense trajectory algorithm to improve the accuracy of recognising badminton players' swing movements. The features are efficiently extracted and encoded. The results on the KTH, UCF Sports, and Hollywood2 datasets demonstrated that the improved algorithm achieved recognition accuracy of 94.2%, 88.2%, and 58.3%, respectively. Compared to traditional methods, the innovation of research lies in optimised feature extraction methods, efficient algorithm design, and accurate action recognition. These results provide new ideas for the research and application of badminton swing motion recognition.

In this paper, we study and provide algorithms for source-provenance of answers to extended SPARQL queries. Extended SPARQL queries are an extension of SPARQL 1.1 queries which support not only a single dataset but multiple datasets, each in a particular context. For example, normal subqueries, aggregate subqueries, (NOT) EXISTS filter subqueries may (optionally) have their own dataset. Additionally, GRAPH patterns can query multiple RDF graphs from the local FROM NAMED dataset and not just one. For monotonic queries, the source why provenance sets that we derive for an answer mapping are each the minimal set of sources appearing in the query that if we consider as they are while the rest of the sources are considered empty, we derive the same answer mapping. We show that this property does not hold for non-monotonic queries. Among others, knowing source why provenance is of critical importance for judging confidence on the answer, allow information quality assessment, accountability, as well as understanding the temporal and spatial status of information.

Aiming at the problems of long evaluation time and poor evaluation accuracy of existing evaluation methods, an improved decision tree-based evaluation method for the effectiveness of college online course teaching reform is proposed. Firstly, the teaching mode of college online course is analysed, and an evaluation system is constructed to ensure the applicability of the evaluation method. Secondly, AHP entropy weight method is used to calculate the weights of evaluation indicators to ensure the accuracy and authority of evaluation results. Finally, the evaluation model based on decision tree algorithm is constructed and improved by fuzzy neural network to further optimise the evaluation results. The parameters of fuzzy neural network are adjusted and gradient descent method is used to optimise the evaluation results, so as to effectively evaluate the effect of college online course teaching reform. Through experiments, the evaluation time of the method is less than 5 ms, and the evaluation accuracy is more than 92.5%, which shows that the method is efficient and accurate, and provides an effective evaluation means for the teaching reform of online courses in colleges and universities.

In order to improve recommendation coverage, a personalised recommendation method for English online learning resources based on improved collaborative filtering algorithm is studied to enhance the comprehensiveness of personalised recommendation for learning resources. Use matrix decomposition to decompose the user English online learning resource rating matrix. Cluster low dimensional English online learning resources by improving the K-means clustering algorithm. Based on the clustering results, calculate the backfill value of English online learning resources and backfill the information matrix of low dimensional English online learning resources. Using an improved collaborative filtering algorithm to calculate the predicted score of learning resources, personalised recommendation of English online learning resources for users based on the predicted score. Experimental results have shown that this method can effectively backfill English online learning resources, and the resource backfilling effect is excellent, and it has a high recommendation coverage rate.

Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.

This paper describes an environment for the “sheer curation” of the experimental data of a group of researchers in the fields of biophysics and structural biology. The approach involves embedding data capture and interpretation within researchers' working practices, so that it is automatic and invisible to the researcher. The environment does not capture just the individual datasets generated by an experiment, but the entire workflow that represent the “story” of the experiment, including intermediate files and provenance metadata, so as to support the verification and reproduction of published results. As the curation environment is decoupled from the researchers’ processing environment, the provenance is inferred from a variety of domain-specific contextual information, using software that implements the knowledge and expertise of the researchers. We also present an approach to publishing the data files and their provenance according to linked data principles by using OAI-ORE (Open Archives Initiative Object Reuse and Exchange) and OPMV.

In 2008 the University of Hull, Stanford University and University of Virginia decided to collaborate with Fedora Commons (now DuraSpace) on the Hydra project. This project has sought to define and develop repository-enabled solutions for the management of multiple digital content management needs that are multi-purpose and multi-functional in such a way as to allow their use across multiple institutions. This article describes the evolution of Hydra as a project, but most importantly as a community that can sustain the outcomes from Hydra and develop them further. The data modelling and technical implementation are touched on in this context, and examples of the Hydra heads in development or production are highlighted. Finally, the benefits of working together, and having worked together, are explored as a key element in establishing a sustainable open source solution.

Multi-criteria inventory classification (MCIC) is popularly used to aid managers in categorising the inventory. Researchers have used numerous mathematical models and approaches, but few resorted to unsupervised machine-learning techniques to address MCIC. This study uses the expectation-maximisation (EM) algorithm to estimate the parameters of the Gaussian mixture model (GMM), a popular unsupervised machine learning algorithm, for ABC inventory classification. The EM-GMM algorithm is sensitive to initialisation, which in turn affects the results. To address this issue, two different initialisation procedures have been proposed for the EM-GMM algorithm. Inventory classification outcomes from 14 existing MCIC models have been given as inputs to study the significance of the two proposed initialisation procedures of the EM-GMM algorithm. The effectiveness of these initialisation procedures corresponding to various inputs has been analysed toward inventory management performance measures, i.e., fill rate, total relevant cost, and inventory turnover ratio.