The present work titled QRGB consists of the development of a Python application for generating QR codes using the additive color generation method (RGB). This innovative method increases the information density stored in QR codes by using three color layers (red, green, and blue), each representing a different data set. QRGB offers an efficient and secure solution for storing and transmitting large amounts of information in limited spaces, significantly improving the capabilities of traditional black-and-white QR codes.By using three color layers (red, green, and blue), QRGB codes can store up to three times more information in the same space. This technique not only increases storage capacity but also enhances information security, making it more difficult to counterfeit or manipulate the code. The superposition of multiple data layers allows for redundancy implementation, increasing the code's robustness against damage or reading errors.QRGB codes are especially useful in applications that require the transmission of large amounts of data in limited spaces, such as in the packaging industry, digital business cards, and interactive advertising. Additionally, they have great potential in areas such as document and ticket security, where the authenticity and integrity of information are crucial.These points provide a solid foundation for understanding the innovation and advantages of colored QR codes (QRGB) compared to traditional QR codes, highlighting their applicability and potential in various sectors.The Python application developed for QRGB utilizes advanced color image processing techniques, applying models and methods to generate and decode QR codes using the RGB color system, thus enhancing information density and security through the innovative use of additive color layers.

Class attendance in institutions of higher learning plays a major role in the success of students and as such putting in measures to curb absenteeism of students would go a long way to help improve their grades. This research sought to design a ‘Bring Your Own Device’ System which will be very suitable for the post-COVID era as well as the fast-growing university community. The research results in a mobile-based attendance infrastructure used by students to prove their presence for class sessions. Flutter, a Software Development Kit (SDK) was used to program the mobile platform and Firebase was used for both the authentication and database. This system also comes in as an improvement on most existing systems as it functions on both Android and iOS. After the testing phase, the models achieved high accuracy and the proposed design has proven to have great potential. This solution stands beyond a research model as a ready-to-use platform.

This article presents a conceptual program model that integrates three main methods: object recognition, color classification, and QR decoding of information. These methods are well-established in the fields of Robotics and Artificial Intelligence, yet they remain subjects of ongoing research interest. The program model organizes the execution of the methods in a sequence directed toward recognized objects. In this way, color classification and QR decoding are performed only on already recognized objects. Colors and QR codes that do not belong to recognized objects are not analyzed, which can provide intelligent robots with reliable and accurate information about the objects they can recognize. For research purposes, image sensors (cameras), images saved to disk, clipboard, or shared memory between applications are used. The article shows that the program model can be augmented with various sensors and algorithms for the comprehensive construction of program control of autonomous robots. Future research that builds on current studies will focus on measuring the distance to recognized objects and structuring the collected information about these objects, which will create opportunities for perceptual anchoring and logical reasoning in the program control of intelligent robots.

This paper examines dynamic identity, as it pertains to the IoT; and explores the practical implementation of a mitigation to some of the key weaknesses of a conventional dynamic identity model. This paper explores human-centric and machine-based observer approaches for confirming device identity, permitting automated identity confirmation for deployed systems. It also assesses the advantages of dynamic identity in the context of identity revocation permitting secure change of ownership for IoT devices. The paper explores use-cases for human and machine-based observation for authentication of device identity when devices join a C2 network, and considers the relative merits for these two approaches for different types of system.

The aim of this study was to evaluate the acceptability and feasibility of a theory-derived sedentary workplace intervention (single arm, pre-post design) for police office staff. Twenty-four staff participated in an 8-week intervention incorporating an education session, team competition with quick response (QR) codes, team trophy, and weekly leaderboard newsletters, a self-monitoring phone app, and electronic prompt tools. The intervention supported participants to reduce and break up their sitting time with three minutes of incidental movement every 30 minutes at work. Feasibility and acceptability were assessed using mixed methods via the RE-AIM QuEST and PRECIS-2 frameworks. The intervention was highly pragmatic in terms of eligibility, organisation, adherence, outcome, and analysis. It was slightly less pragmatic on recruitment and setting. Delivery and follow-up were more explanatory. Reach and adoption indicators demonstrated feasibility among police staff, across a range of departments, who were demographically similar to participants in previous office-based multi-component interventions. The intervention was delivered mostly as planned with minor deviations from protocol (Implementation fidelity). Participants perceived the intervention components as highly acceptable. Preliminary results showed improvements in workplace sitting and standing, as well as small improvements in weight and positive affect. Evaluation of the intervention in a fully powered randomised controlled trial to assess behaviour and health outcomes is recommended.

Guiding the visitor to appreciate the sights during an excursion is crucial to building a memorable experience. In the literature, we find many articles exploring the use of sophisticated technologies towards such a goal. The case of tourism proposals for inland areas exhibits a noteworthy aspect, since the presence of human artifacts and the signage may damage the experience, and introduce pollutants as well. This paper analyzes the signage for a natural trail putting into practice a holistic methodology that takes into account all aspects of the problem, starting from the touristic vocation of the area and the social fabric. The analysis indicates slow, community-involved tourism as the preferable target, with extreme requirements of sustainability in the social, economic, and environmental domains for the signage solution. Appropriate governance guidelines are given to implement the proposal. Footprint evaluations address the QR-code technology, which is used to design a real-scale solution finally deployed on the field.