The 88-year-old began the role four years ago after his wife died.

Former Shrewsbury School boy Sandy Irvine went missing climbing Everest 100 years ago.

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.

Sisters Alice, Chloe and Lucy will be part of the Children in Need Choir on Friday.

A mum says she felt like a nuisance because she had to repeatedly try to find out what was wrong.

Potential industrial action over the plans have now been called off by a doctors union.

‘Don’t play Wordle’ is the plea from striking New York Times workers who are complaining about what they say are unfair labour practices. The New York Times Tech Guild, which […]

The post Boycott Wordle – New York Times workers plead, DNA firm disappears without trace appeared first on Tech Digest.

When Microsoft announced it was rebranding its phone hardware as “Microsoft Lumia”, it hinted that there might be a new phone on the way, with the Nokia Conversations blog post quoting Senior VP Tuula Rytilä as saying “we are looking …

Plans are put In the freezer, alongside the lamb chops.

The Chinese electric vehicle giant BYD has seen its quarterly revenues soar, beating Tesla’s for the first time. It posted more than 200bn yuan ($28.2bn, £21.8bn) in revenues between July […]

The post BYD revenues overtake Tesla’s, Reddit makes profit for first time appeared first on ShinyShiny.

McLaren could be set to win their first constructors' title for 25 years this season. Here is how close they've come over that time.

F1 did something it has never done before last weekend and moved a race start time earlier. But did that affect your ability to watch?

I didn’t watch this in the cinema, and I had a bit more to say about it than my usual one-line movie review format, so it didn’t fit into my monthly round-up, but I do want to wholeheartedly recommend you...

This paper presents a system that simulates, in real time, the volumetric deformation of muscles during human locomotion. We propose a two-layered motion model. The requirements of realism and real time computation lead to a hybrid locomotion system that uses a skeleton as first layer. The muscles, represented by an anatomical surface model, constitute the second layer, whose deformations are simulated with a finite element method (FEM). The FEM subsystem is fed by the torques and forces got from the locomotion system, through a line of action model, and takes into account the geometry and material properties of the muscles. High level parameters (like height, weight, physical constitution, step frequency, step length or speed) allow to customize the individuals and the locomotion and therefore, the deformation of the persons' muscles.

Real-time systems like pervasive computing have to complete executing a task within the predetermined time while ensuring that the execution results are logically correct. Such systems require intelligent scheduling methods that can adequately promptly distribute the given tasks to a processor(s). In this paper, we propose LSTR (Least Slack Time Rate first), a new and simple scheduling algorithm, for a multi-processor environment, and demonstrate its efficient performance through various tests.

Service composition is an effective approach for large-scale multimedia delivery. In previous works, user requirement is represented as one fixed functional path which is composed of several functional components in a certain order. Actually, there may be several functional paths (deliver different quality level multimedia data, e.g., image pixel, frame rate) that can meet one request. And due to the diversity of devices and connections in pervasive environment, system should choose a suitable media quality delivery path in accordance with context, instead of one fixed functional path. This paper presents a deep study of multimedia delivery problem and proposes an on-line algorithm LDPath and an off-line centralized algorithm LD/RPath respectively. LDPath aims at delivering multimedia data to end user with lowest delay by choosing services to build delivery paths hop-by-hop, which is adapted to the unstable open environment. And LD/RPath is developed for a relatively stable environment, which generates delivery paths according to the trade-off between delay and reliability metrics, because the service reliability is also an important fact in such scenario. Experimental results show that both algorithms have good performance with low overhead to the system.

Project monitoring plays a crucial role in project management, which is a part of every stage of a project's life-cycle. Nevertheless, along with the increasing ratio of outsourcing in many companies' strategic plans, project monitoring has been challenged by geographically dispersed project teams and culturally diverse team members. Furthermore, because of the lack of a uniform standard, data exchange between various project monitoring software becomes an impossible mission. These factors together lead to the issue of ambiguity in project monitoring processes. Ontology is a form of knowledge representation with the purpose of disambiguation. Consequently, in this paper, we propose the framework of an ontology-based real-time project monitoring system (ORPSM), in order to, by means of ontologies, solve the ambiguity issue in project monitoring processes caused by multiple factors. The framework incorporates a series of ontologies for knowledge capture, storage, sharing and term disambiguation in project monitoring processes, and a series of metrics for assisting management of project organizations to better monitor projects. We propose to configure the ORPMS framework in a cloud environment, aiming at providing the project monitoring service to geographically distributed and dynamic project members with great flexibility, scalability and security. A case study is conducted on a prototype of the ORPMS in order to evaluate the framework.

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

1. Long ago, Voyager 2 might have caught Uranus at a bad time  Space.com
2. The anomalous state of Uranus’s magnetosphere during the Voyager 2 flyby  Nature.com
3. Potential For Life Found On Uranus' Moons, Scientists Reveal  NDTV
4. New Uranus research suggests what’s known about the planet could be wrong  CNN
5. Uranus Might Have Experienced a Freak Event When Voyager 2 Visited  The New York Times

By Lucas Miotto Lopes and Jiahong Chen The future of real-time appeal Knowing when to say or do something is often just as important as knowing what to say or do. The right advice at the wrong time is not

In order to further improve the teaching quality of multimedia teaching in school daily teaching, a classroom facial expression emotion recognition model is proposed based on convolutional neural network. VGGNet and CliqueNet are used as the basic expression emotion recognition methods, and the two recognition models are fused while the attention module CBAM is added. Simulation results show that the designed classroom face expression emotion recognition model based on V-CNet has high recognition accuracy, and the recognition accuracy on the test set reaches 93.11%, which can be applied to actual teaching scenarios and improve the quality of classroom teaching.

Since its birth, the stock market has received widespread attention from many scholars and investors. However, there are many factors that affect stock prices, including the company's own internal factors and the impact of external policies. The extent and manner of fundamental impacts also vary, making stock price predictions very difficult. Based on this, this article first introduces the research significance of the stock market prediction framework, and then conducts academic research and analysis on two key sentences of stock market prediction and artificial intelligence in stock market prediction. Then this article proposes a constructive algorithm theory, and finally conducts a simulation comparison experiment and summarises and discusses the experiment. Research results show that the neural network prediction method is more effective in stock market prediction; the minimum training rate is generally 0.9; the agency's expected dilution rate and the published stock market dilution rate are both around 6%.

Aim/Purpose: The aim of this article is to develop a tool to detect plagiarism in real time amongst students being evaluated for learning in a computer-based assessment setting. Background: Cheating or copying all or part of source code of a program is a serious concern to academic institutions. Many academic institutions apply a combination of policy driven and plagiarism detection approaches. These mechanisms are either proactive or reactive and focus on identifying, catching, and punishing those found to have cheated or plagiarized. To be more effective against plagiarism, mechanisms that detect cheating or colluding in real-time are desirable. Methodology: In the development of a tool for real-time plagiarism prevention, literature review and prototyping was used. The prototype was implemented in Delphi programming language using Indy components. Contribution: A real-time plagiarism detection tool suitable for use in a computer-based assessment setting is developed. This tool can be used to complement other existing mechanisms. Findings: The developed tool was tested in an environment with 55 personal computers and found to be effective in detecting unauthorized access to internet, intranet, and USB ports on the personal computers. Recommendations for Practitioners: The developed tool is suitable for use in any environment where computer-based evaluation may be conducted. Recommendation for Researchers: This work provides a set of criteria for developing a real-time plagiarism prevention tool for use in a computer-based assessment. Impact on Society: The developed tool prevents academic dishonesty during an assessment process, consequently, inculcating confidence in the assessment processes and respectability of the education system in the society. Future Research: As future work, we propose a comparison between our tool and other such tools for its performance and its features. In addition, we want to extend our work to include testing for scalability of the tool to larger settings.

Aim/Purpose: Business analytics is a cross-functional field that is important to implement for a college and has emerged as a critically important core component of the business curriculum. It is a difficult task due to scheduling concerns and limits to faculty and student resources. This paper describes the process of creating a central video repository to serve as a platform for just in time teaching and the impact on student learning outcomes. Background: Industry demand for employees with analytical knowledge, skills, and abilities requires additional analytical content throughout the college of business curriculum. This demand needs other content to be added to ensure that students have the prerequisite skills to complete assignments. Two pedagogical approaches to address this issue are Just-in-Time Teaching (JiTT) and scaffolding, grounded in the Vygoskian concept of “Zone of Proximal Development. Methodology: This paper presents a case study that applies scaffolding and JiTT teaching to create a video repository to add business analytics instruction to a curriculum. The California Critical Thinking Skills Test (CCTST) and Major Field Test (MFT) scores were analyzed to assess learning outcomes. Student and faculty comments were considered to inform the results of the review. Contribution: This paper demonstrates a practical application of scaffolding and JiTT theory by outlining the process of using a video library to provide valuable instructional resources that support meaningful learning, promote student academic achievement, and improve program flexibility. Findings: A centrally created library is a simple and inexpensive way to provide business analytics course content, augmenting standard content delivery. Assessment of learning scores showed an improvement, and a summary of lessons learned is provided to guide implications. Recommendations for Practitioners: Pedagogical implications of this research include the observation that producing a central library of instructor created videos and assignments can help address knowledge and skills gaps, augment the learning of business analytics content, and provide a valuable educational resource throughout the college of business curriculum. Recommendation for Researchers: This paper examines the use of scaffolding and JiTT theories. Additional examination of these theories may improve the understanding and limits of these concepts as higher education evolves due to the combination of market forces changing the execution of course delivery. Impact on Society: Universities are tasked with providing new and increasing skills to students while controlling the costs. A centrally created library of instructional videos provides a means of delivering meaningful content while controlling costs. Future Research: Future research may examine student success, including the immediate impact of videos and longitudinally using video repositories throughout the curriculum. Studies examining the approach across multiple institutions may help to evaluate the success of video repositories. Faculty acceptance of centrally created video libraries and assignments should be considered for the value of faculty recruiting and use in the classroom. The economic impact on both the university and students should be evaluated.

Aim/Purpose: The study focused on learner sentiments and experiences after using the Moodle assessment module and trained a machine learning classifier for future sentiment predictions. Background: Learner assessment is one of the standard methods instructors use to measure students’ performance and ascertain successful teaching objectives. In pedagogical design, assessment planning is vital in lesson content planning to the extent that curriculum designers and instructors primarily think like assessors. Assessment aids students in redefining their understanding of a subject and serves as the basis for more profound research in that particular subject. Positive results from an evaluation also motivate learners and provide employment directions to the students. Assessment results guide not just the students but also the instructor. Methodology: A modified methodology was used for carrying out the study. The revised methodology is divided into two major parts: the text-processing phase and the classification model phase. The text-processing phase consists of stages including cleaning, tokenization, and stop words removal, while the classification model phase consists of dataset training using a sentiment analyser, a polarity classification model and a prediction validation model. The text-processing phase of the referenced methodology did not utilise tokenization and stop words. In addition, the classification model did not include a sentiment analyser. Contribution: The reviewed literature reveals two major omissions: sentiment responses on using the Moodle for online assessment, particularly in developing countries with unstable internet connectivity, have not been investigated, and variations of the k-fold cross-validation technique in detecting overfitting and developing a reliable classifier have been largely neglected. In this study we built a Sentiment Analyser for Learner Emotion Management using the Moodle for assessment with data collected from a Ghanaian tertiary institution and developed a classification model for future sentiment predictions by evaluating the 10-fold and the 5-fold techniques on prediction accuracy. Findings: After training and testing, the RF algorithm emerged as the best classifier using the 5-fold cross-validation technique with an accuracy of 64.9%. Recommendations for Practitioners: Instead of a closed-ended questionnaire for learner feedback assessment, the open-ended mechanism should be utilised since learners can freely express their emotions devoid of restrictions. Recommendation for Researchers: Feature selection for sentiment analysis does not always improve the overall accuracy for the classification model. The traditional machine learning algorithms should always be compared to either the ensemble or the deep learning algorithms Impact on Society: Understanding learners’ emotions without restriction is important in the educational process. The pedagogical implementation of lessons and assessment should focus on machine learning integration Future Research: To compare ensemble and deep learning algorithms

In the construction sector, moulding machines are crucial in producing concrete products, yet changing their mould can pose challenges for some businesses. This paper presents a case study aimed at reducing the setup time of HESS RH 600 moulding machine. Four alternatives are proposed and evaluated to achieve this goal. The first alternative involves converting internal to external activities, while the subsequent alternatives aim to improve the basic solution. These include building a canopy near the machine (alternative 2), installing an air reservoir (alternative 3), and a comprehensive approach involving building the canopy, installing the air reservoir, and adding a new forklift to facilitate the machine setup process (alternative 4). The analytic hierarchy process (AHP) heuristic method is used to select the best alternative solution based on prespecified criteria. It is found that the application of the single-minute exchange of die (SMED) solution without any further improvement is the most favourable.

Voice Activity Detection (VAD) distinguishes speech segments from noise or silence areas. An efficient and noise-robust VAD system can be widely used for emerging speech technologies such as wireless communication and speech recognition. In this paper, we propose two versions of an unsupervised Arabic VAD method based on the combination of the Short-Time Energy (STE) and the Spectral Centroid (SC) features for formulating a typical threshold to detect speech areas. The first version compares only the STE feature to the threshold (STE-VAD). In contrast, the second compares the SC vector and the threshold (SC-VAD). The two versions of our VAD method were tested on 770 sentences of the Arabphone corpus, which were recorded in clean and noisy environments and evaluated under different values of Signal-to-Noise-Ratio. The experiments demonstrated the robustness of the STE-VAD in terms of accuracy and Mean Square Error.

Aim/Purpose: The objective of this study is to understand how video lectures of the same length and content as the current face-to-face lectures can be designed and implemented to have a positive effect on student performance, particularly when there is a campus shutdown. Background: In a number of South African universities protests by the students are on the increase. Often, they lead to the cancellation of academic activities such as face-to-face classes and examinations. Methodology: A quasi-experimental design was used on two video lectures to (1) compare the performance of the students who did not watch the video lectures and those who watched the video lectures, (2) compare the performance of each student who watched the video lectures on the test topics covered in the videos and the test topics not covered in the videos, and (3) determine the factors that influence the effectiveness of the video lectures. Contribution: This study contributes to the literature by investigating the effectiveness of video lectures in improving student performance, the factors associated to the effectiveness of such lectures, and the complexity or simplicity of the two video lectures used, and by providing possible solutions to the challenges identified in relation to designing video lectures. Findings: In terms of student performance, there is no significant advantage arising from watching the video lectures for the students who watch the video lectures, as compared to those who did not watch the video lectures. It is also found that the student performance on the topics with video lectures is significantly associated to the students’ commitment, prior performance, the quality of the content, and the design of the videos. Recommendations for Practitioners: This study recommends how the accounting video lectures can be designed and highlights the environments in which the video lectures of the same length and content as the face-to-face lectures should not be used. Recommendation for Researchers: Researchers should replicate this study by using short length videos of better quality and appropriate length, which incorporate current issues, games, are interactive, and so forth. Impact on Society: This study examines the use of educational video lectures in order to minimise the impact of disruptions at university level. Future Research: Future studies may use randomly selecting treatment and control groups. They may consider a nationwide research or using qualitative interviews in examining the use of educational video lectures.

Knowledge management research applied to the development of real-time research capability, or capability to solve societal problems in hours and days instead of years and decades, is perhaps increasingly important, given persistent global problems such as the Zika virus and rapidly developing antibiotic resistance. Drawing on swarm intelligence theory, this paper presents an approach to real-time research problem-solving in the form of a framework for understanding the complexity of real-time research and the challenges associated with maximizing collaboration. The objective of this research is to make explicit certain theoretical, methodological, and practical implications deriving from new literature on emerging technologies and new forms of problem solving and to offer a model of real-time problem solving based on a synthesis of the literature. Drawing from ant colony, bee colony, and particle swarm optimization, as well as other population-based metaheuristics, swarm intelligence principles are derived in support of improved effectiveness and efficiency for multidisciplinary human swarm problem-solving. This synthesis seeks to offer useful insights into the research process, by offering a perspective of what maximized collaboration, as a system, implies for real-time problem solving.

Aim/Purpose: The objective of this research is to investigate the effect of review consistency between textual content and rating on review helpfulness. A measure of review consistency is introduced to determine the degree to which the review sentiment of textual content conforms with the review rating score. A theoretical model grounded in signaling theory is adopted to explore how different variables (review sentiment, review rating, review length, and review rating variance) affect review consistency and the relationship between review consistency and review helpfulness. Background: Online reviews vary in their characteristics and hence their different quality features and degrees of helpfulness. High-quality online reviews offer consumers the ability to make informed purchase decisions and improve trust in e-commerce websites. The helpfulness of online reviews continues to be a focal research issue regardless of the independent or joint effects of different factors. This research posits that the consistency between review content and review rating is an important quality indicator affecting the helpfulness of online reviews. The review consistency of online reviews is another important requirement for maintaining the significance and perceived value of online reviews. Incidentally, this parameter is inadequately discussed in the literature. A possible reason is that review consistency is not a review feature that can be readily monitored on e-commerce websites. Methodology: More than 100,000 product reviews were collected from Amazon.com and preprocessed using natural language processing tools. Then, the quality reviews were identified, and relevant features were extracted for model training. Machine learning and sentiment analysis techniques were implemented, and each review was assigned a consistency score between 0 (not consistent) and 1 (fully consistent). Finally, signaling theory was employed, and the derived data were analyzed to determine the effect of review consistency on review helpfulness, the effect of several factors on review consistency, and their relationship with review helpfulness. Contribution: This research contributes to the literature by introducing a mathematical measure to determine the consistency between the textual content of online reviews and their associated ratings. Furthermore, a theoretical model grounded in signaling theory was developed to investigate the effect on review helpfulness. This work can considerably extend the body of knowledge on the helpfulness of online reviews, with notable implications for research and practice. Findings: Empirical results have shown that review consistency significantly affects the perceived helpfulness of online reviews. The study similarly finds that review rating is an important factor affecting review consistency; it also confirms a moderating effect of review sentiment, review rating, review length, and review rating variance on the relationship between review consistency and review helpfulness. Overall, the findings reveal the following: (1) online reviews with textual content that correctly explains the associated rating tend to be more helpful; (2) reviews with extreme ratings are more likely to be consistent with their textual content; and (3) comparatively, review consistency more strongly affects the helpfulness of reviews with short textual content, positive polarity textual content, and lower rating scores and variance. Recommendations for Practitioners: E-commerce systems should incorporate a review consistency measure to rank consumer reviews and provide customers with quick and accurate access to the most helpful reviews. Impact on Society: Incorporating a score of review consistency for online reviews can help consumers access the best reviews and make better purchase decisions, and e-commerce systems improve their business, ultimately leading to more effective e-commerce. Future Research: Additional research should be conducted to test the impact of review consistency on helpfulness in different datasets, product types, and different moderating variables.

Epidemiologists recommended contact-tracing as an effective control measure for the global infection like COVID-19 pandemic. Despite its effectiveness in infection containment, it has many limitations such as labour-intensive process, prone to human errors and most importantly, user privacy concerns. To address these shortcomings, we proposed location-aware blockchain-based hierarchical contact-tracing framework for anonymised data collection and processing. This infectious disease control framework serves both the infected users with localised alerts as well as stakeholders such as city officials and health workers with health statistics. Our proposed solution uses hierarchical network design that offloads individual infection block data to create hospital and city-level 'chains' for generating macro-level infection statistics. Results demonstrate that our system can represent the dynamic complexities of contract tracing in highly infection situations. Overall, our design emphasises on data processing and verification mechanism for large volume of infection data over a significant period of time for active risk assessment.