19684 research outputs found
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A Robust Multiparty Authentication Testbed Architecture for the Industrial Internet of Things
No full textThe Industrial Internet of Things (IIoT) has emerged as an advancement and application of the Internet of things (IoT) to enhance manufacturing and industrial processes. IIoT devices are designed for use in industrial environments. The vast majority of IIoT devices are sensors that monitor various manufacturing processes. Monitoring applications receive data from numerous types of sensors, ensuring that vital functions run smoothly. However, due to the wide range of devices and providers, incorporating and actively engaging collaborative users and services from multiple IoT networks in different security settings, as well as the difficulty of adding security to resource-constrained devices, securing IoT devices and the networks they connect to can be challenging. To alleviate these security challenges, the formation of specific trust relationships between these IoT service instances and users necessitates the implementation of a new authentication mechanism that sends a shared secret to all session participants. In this paper, a new multi-party authentication testbed architecture is designed and implemented to dynamically secure communications between participants if members of various security groups choose to use their services while keeping security credentials as secure as possible through resource access. The robustness of the designed protocol is validated using the NuSMV model checkers software tool. This validation process verifies the accuracy of the protocol, ensuring that it meets all requirements. Consequently, this meticulous analysis reduces errors and enhances the protocol’s dependability, security, and credibility. Utilising linear-temporal logic verification, the correctness of the presented framework is explicitly analysed and established. This ensures a rigorous evaluation and provides a comprehensive assessment of the performance and behaviour of the proposed protocol’s features.</p
Unlocking leadership identity and influence:the role of photovoice in organizational sensemaking
No full textPurposeUnderstanding and influencing organizational culture is essential for leaders to drive change. However, capturing and measuring culture remains a challenge due to its intangible nature. In this study, we address this issue, using the concept of sensemaking to explore how creative methodologies – specifically, photovoice – can contribute to the process of interpreting organizational culture.Design/methodology/approachOur study employed a multi-stage photovoice methodology with 36 senior leader apprentices. Participants attended workshops introducing the concepts of strategy and culture and the process of photovoice. After completing a photo collection task, participants engaged in a structured debriefing in collaboration with peers and a reflective writing activity. The study culminated in an exhibition, displaying photographs and narratives to a broader audience. Results were derived from a thematic analysis of participant-generated written narratives and findings from focus groups conducted at the study’s conclusion.FindingsBy making intangible cultural facets visible and comprehensible, we show that photovoice not only aids in understanding organizational culture but also serves as a catalyst for action, empowering leaders to identify opportunities for impactful influence. We contribute to sensemaking literature by highlighting the value of external “sensegivers” in facilitating leaders’ understanding of culture and their influencing opportunities.Originality/valueWe show the value of leaders engaging in deliberate, creative sensemaking. We suggest photovoice is a powerful tool for exploring hard-to-articulate topics, supporting leaders’ role identity and stimulating commitment to influence. Our methodological transparency means that the findings can inform leadership development strategies
The Use of Generative Artificial Intelligence to Develop Student Research, Critical Thinking, and Problem-Solving Skills
No full textThis paper is a case study of supporting students in developing their Generative Artificial Intelligence (GAI) literacy as well as guiding them to use it ethically, appropriately, and responsibly in their studies. As part of the study, a law coursework assignment was designed utilising a four-step Problem, AI, Interaction, Reflection (PAIR) framework that included a problem-solving task that required the students to use GAI tools. The students were asked to use one or two GAI tools of their choice early in their assessment preparation to research and were given a set questionnaire to reflect on their experience. They were instructed to apply Gibbs’ or Rolfe’s reflective cycles to write about their experience in the reflective part of the assessment. This study found that a GAI-enabled assessment reinforced students’ understanding of the importance of academic integrity, enhanced their research skills, and helped them understand complex legal issues and terminologies. It also found that the students did not rely on GAI outputs but evaluated and critiqued them for their accuracy and depth referring to primary and secondary legal sources—a process that enhanced their critical thinking and problem-solving skills
Orodispersible Tablets for Paediatric Use:A Systematic Review and Outlook for Future Research
No full textBackground: Children are often underserved by oral medicines designed for adults, leading to off-label use and workarounds that risk dosing inaccuracy. Objective: To synthesise recent advances in paediatric orodispersible tablets (ODTs), covering manufacturing technologies, disintegrant choices, taste-masking strategies and in-vitro disintegration methods. Methods: Following PRISMA, we searched PubMed, EMBASE, MEDLINE, Scopus and Google Scholar for experimental studies formulating ODTs relevant to paediatric use. Two reviewers screened records and extracted data on technology, excipients, disintegration/dissolution testing and key outcomes; risk of bias was evaluated using a six-domain framework. Results: Sixty-four studies met inclusion criteria. Direct compression was the predominant approach, with additional use of freeze-drying, sublimation, spray-drying, nanoparticle-in-tablet systems, and semi-solid extrusion/3D printing for personalised dosing. Crospovidone, croscarmellose sodium and sodium starch glycolate were the most frequent superdisintegrants; natural and co-processed systems showed promise as cost-effective alternatives. Disintegration time was commonly assessed with pharmacopoeial methods, but multiple modified set-ups were reported to better simulate oral conditions. Conclusions: Paediatric ODT development has accelerated, with direct compression remaining first-line and 3D-printing emerging for dose individualisation. Though paediatric ODTs are now a practical platform, translation hinges on three priorities: (i) harmonised, physiologically relevant disintegration tests aligned with Ph. Eur./USP; (ii) routine, age-stratified acceptability reporting alongside in-vitro data; and (iii) GMP-ready workflows. Head-to-head benchmarking of co-processed and natural/synthetic superdisintegrants using common endpoints, plus attention to dose flexibility, heat-stable packaging, and affordability, will accelerate equitable uptake
Sterically Stabilized (Zr,Ti)-(Al,Sn,Pb,Bi)-C MAX Phase Solid Solutions with Zn Additions and Enhanced Chemical Complexity on the A-Site
No full textThe MAX phases constitute a family of nanolaminated ternary carbides and nitrides renowned for their compositional versatility, as reflected in the easy formation of solid solutions with variable chemical complexity. Synthesizing MAX phase solid solutions with intentionally tailored chemical complexity can produce materials that are able to meet the property requirements of the targeted application(s). This work presents an effective strategy specifically developed to design and fabricate highly phase-pure ceramics based on chemically complex MAX phase solid solutions by sterically stabilizing their unit cells. Steric unit cell stabilization is achieved via a judicious balance of dissimilar M- and A-elements, which targets the minimization of lattice distortions. This work produced high-purity (up to 88.7 wt %) (Zr0.8,Ti0.2)2(Al,Sn,Pb)C and (Zr0.8,Ti0.2)2(Al,Sn,Pb,Bi)C 211 MAX phase solid solutions by spark plasma sintering at 1350–1500 °C. Molten Zn- and/or Pb-/Bi-containing intermetallics facilitated the synthesis of soft (3–5 GPa), coarse-grained (length >20 μm, thickness >10 μm), and damage-tolerant ceramics. Intermetallics comprising Zn, Pb, and Bi improved (a) C/carbide dissolution, (b) Sn/C diffusion, and (c) carbide wetting, thus producing a 312 (Zr0.8,Ti0.2)3(Al,Sn,Pb,Bi)C2MAX phase solid solution. Forming (Zr0.8,Ti0.2)3(Al,Sn,Pb,Bi)C2contributed to the growth of very large platelets (length >100 μm) with a distinct (312-core)/(211-shell) morphology. Zn did not occupy the A-site, unlike Al, Sn, Pb, and Bi. Sterically balanced A-site elemental occupancies, albeit nonequimolar, alleviated lattice distortions and aided the steric stabilization of the crystal structure, whereas the chemical complexity on the A-site increased the configurational entropy of the synthesized MAX phase compounds, despite pre-existing M-site compositional restrictions, further enhancing their thermodynamic stability.</p
A Miniaturized Flexible Fractal Rectenna for Wireless Communication and Power Transfer
No full textThis paper proposes a miniaturized fractal rectenna to address the demands of integration and small size in wireless communication and wireless power transfer (WPT)system. The miniaturized fractal antenna adopts a three-layer symmetric stacked structure, all fabricated by felt material that provide mechanical stability and electromagnetic isolation. The radiation patch layer uses a square patch with slots etched at the centers of its four sides to optimize miniaturization and radiation performance; the transmission line layer employs two vertically placed parallel lines to enhance electromagnetic coupling attenuation and reduce port crosstalk; the ground layer uses a PEC plate matching the substrate size to ensure good grounding and electromagnetic shielding. For the WPT system, a rectifier is integrated to convert the AC power received by the antenna into usable DC power. In terms of performance, the antenna with two ports both resonates at 2.45 GHz, with a high-gain of 3.8 dBi and 3.8 dBi; the integrated WPT system effectively achieves power reception and conversion via the rectenna, enabling 65% conversion efficiency. This miniaturized fractal rectenna can potentially provide structural design references and performance data support inwireless communication fields (e.g., small-scale IoT devices, portable communication terminals) and low-power SWIPT scenarios (e.g., wirelessly powered sensors, wearable electronic devices)
Fault Diagnosis of Wind Turbine Drivetrains Using XGBoost-Assisted Discriminative Frequency Band Identification and a CNN–Transformer Network
No full textTraditional wind turbine drivetrain health assessment generally depends on feature extraction guided by expert experience and prior knowledge. However, the effectiveness of this approach is often limited when such knowledge is insufficient or when fault features are obscured by high levels of ambient noise. In response to these issues, this study proposes a new data-driven framework that combines intelligent frequency band identification with a deep learning architecture. In the proposed approach, vibration signals from the bearings are transformed into their spectral representation, and the frequency spectrum is divided into multiple frequency bands. The relative importance of each band is evaluated and ranked using XGBoost, enabling the selection of the most informative features and significant dimensionality reduction. A hybrid CNN–Transformer model is then employed to combine local feature extraction with global attention mechanisms for accurate fault classification. Experimental evaluations using two open-source datasets indicate that the proposed framework achieves high classification accuracy and rapid convergence, offering a robust and computationally efficient solution for wind turbine drivetrain fault diagnosis
Implicit Layer Empowered Deep Learning Networks for 6G Adaptive Channel Estimation
No full textResearch on sixth-generation (6G) wireless networks has gained significant attention as wireless communications technologies advance. In the upcoming 6G era, artificial intelligence (AI) is expected to play a significant role in enhancing mobile communications. In particular, the application of AI techniques in channel estimation can enable accurate channel state information, even in dynamic scenarios. However, the limited computational resources in user equipment often prevent the deployment of complex algorithms, necessitating adaptive channel estimation solutions, balancing the accuracy and complexity dynamically. Conventionally, AI-based channel estimation algorithms rely on explicitly stacking deep learning (DL) layers/blocks, making adaptation challenging. This paper proposes an adaptive Implicit DL Channel Estimation Network (ICENet) that employs a lightweight, implicit network design to achieve dynamic adaptability. Numerical results show that our approach can achieve the trade-off between algorithm complexity and channel estimation accuracy by adapting based on channel quality. Additionally, it offers reduced memory cost compared to explicit layer/block-stacked networks while maintaining or surpassing their estimation accuracy. Furthermore, we analyze key factors influencing forward and backward propagations in ICENet and regularize the Jacobian matrix to ensure stable convergence during the training process
A Multilevel Perspective of Organized and Intentional Corporate Social Irresponsibility
No full textWe develop a multi-level perspective on how acts of Corporate Social Irresponsibility (CSI) emerge and develop over time. We specifically focus on what we label as organized and intentional CSI – the irresponsible activities undertaken by several colluding actors that cause harm to multiple stakeholders. Our choice was informed by the prevalence of organized and intentional CSI in countries with strong regulatory safeguards and monitoring mechanisms. By merging the literature on CSI and institutional entrepreneurship, we conceptualize the dynamic unfolding of organized and intentional CSI in three stages: actuating, propagating, and collectivizing. We identify six areas for further investigation that can meaningfully inform policies: the intentional dark side of institutional entrepreneurs, the unintended actions of regulators, the role of investigative journalists, the impact of institutional conditions, various forms of interrelated multi-level decoupling, and multiple moral ethos. We also propose several policy implications. First, simplifying regulation and decreasing the number of regulatory agencies can reduce institutional uncertainties and mitigate chances for opportunistic behavior. Second, through extensive consultations with stakeholders and limiting opportunities for preferential access, policymakers can minimize the risk of regulatory capture. Finally, promoting self-regulation that incorporates norms of responsible leadership and power distribution could complement other efforts in curbing CSI
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