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Abusive supervision in commercial kitchens: Insights from the restaurant industry
Get PDFThis mixed-method study investigates how abusive supervision and bullying impact job satisfaction and turnover intentions among employees in an environment plagued by ingrained incivility: commercial kitchens. Underpinned by social learning theory, we draw from 832 survey responses and 20 in-depth interviews to explore the extent to which supervisory abuse and workplace bullying negatively impact employee perceptions of their working environment while also investigating positive alternatives therein (e.g., authentic leadership and encouragement of creativity). Results suggest that, despite day-today challenges posed by abusive leadership, a strong sense of camaraderie and passion for kitchen work stimulated a commitment to the job. Accordingly, the study concludes that the inherently creative nature of commercial kitchen work and the personalities of fellow staff played a significant role in retaining employees. It thus highlights the complexity of food service employee retention and suggests that a holistic understanding of both leadership dynamics and intrinsic motives is essential
Strategies and interventions used to provide communication education for midwifery students. A scoping review
Get PDFAimTo examine the current literature on educational strategies and interventions developed with the objective of teaching or enhancing communication skills of student midwives during their pre-registration education programmes.DesignA scoping review based on the Joanna Briggs Institute framework was conducted using predefined criteria and reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.MethodsA comprehensive search was conducted using various databases (Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), EMBASE, PsycINFO, Maternity and Infant Care Database (MIDIRS), Web of Science and Education Resources Information Centre (ERIC)) in October 2023.ResultsA total of 120 titles and abstracts were screened. A final number of eight articles were subjected to quality appraisal and included in the scoping review. Five themes were identified which describe educational strategies and interventions including: simulation-based training, the use of role-play, pedagogical approaches, theory-based information workshops and debrief and reflection.ConclusionsThis review highlights a gap in research focusing on the importance of communication skills training for student midwives throughout midwifery education. Despite the limited numbers of studies, different interventions and educational strategies have been recognized for enhancing these skills. To equip midwives with strong communication skills, a combination of interventions is recommended, including communication-focused workshops tailored for midwifery education and debriefing and student reflection sessions specifically designed to enhanced communication skill
Throughput Maximization in RIS-Assisted NOMA-THz Communication Network
Get PDFIn order to overcome spectrum scarcity and provide higher data rates, the sixth-generation (6G) wireless communication network is expected to perform data transmission using terahertz (THz) frequencies. However, the effective implementation of these communication systems is hampered by severe levels of signal degradation to which the THz bandwidth is subject to. Recent improvements and advancements in the fabrication process of electromagnetic (EM) metamaterials have made reconfigurable intelligent surfaces (RIS) a very promising solution to address these THz-related attenuation issues. Additionally, the adoption of non-orthogonal multiple access (NOMA) transmissions represents an effective way to improve spectrum efficiency for 6G networks. In this paper, we investigate the problem of downlink aggregated sum-rate maximisation for a multiple-input multiple-output (MIMO) system assisted by a RIS panel in performing NOMA transmission within the THz bandwidth. More specifically, we propose an optimization algorithm that jointly optimizes the transmitting power at the access point (AP) and the phase-shift coefficients for the RIS elements iteratively. Through simulation results, we demonstrate that the proposed method outperforms conventional benchmark schemes in terms of achieved aggregated throughput
Digital Twin-Empowered Integrated Satellite-Terrestrial Networks toward 6G Internet of Things
No full textIntegrated satellite-terrestrial networks (ISTNs) technology in the sixth generation (6G) wireless networks has been considered a promising candidate for global coverage and seamless connectivity for the Internet of things (IoT). However, integrating these two complex systems poses many deployment , management, and maintenance issues. With the fundamental principle of building a virtual live representation of the networks, the digital twin (DT) technology can be used in complex ISTNs to provide a reliable environment for designing or testing, reducing risk and latency, recovering the networks quickly, and optimizing resource allocation for IoT devices in real-time. In this article, we propose an ISTN framework empowered by DT technology, and discuss its promising models, benefits, potential technologies, research challenges, and future research direction for 6G IoT
Evaluation of the protective efficacy of different doses of a Chlamydia abortus subcellular vaccine in a pregnant sheep challenge model for ovine enzootic abortion
Get PDFChlamydia abortus causes the disease ovine enzootic abortion, which is one of the most infectious causes of foetal death in small ruminants worldwide. While the disease can be controlled using live and inactivated commercial vaccines, there is scope for improvements in safety for both sheep and human handlers of the vaccines. We have previously reported the development of a new prototype vaccine based on a detergent-extracted outer membrane protein preparation of C. abortus that was determined to be more efficacious and safer than the commercial vaccines when administered in two inoculations three weeks apart. In this new study, we have developed this vaccine further by comparing its efficacy when delivered in one or two (1 × 20 µg and 2 × 10 µg) doses, as well as also comparing the effect of reducing the antigen content of the vaccine by 50% (2 × 5 µg and 1 × 10 µg). All vaccine formulations performed well in comparison to the unvaccinated challenge control group, with no significant differences observed between vaccine groups, demonstrating that the vaccine can be administered as a single inoculation and at a lower dose without compromising efficacy. Future studies should focus on further defining the optimal antigen dose to increase the commercial viability of the vaccine
Advancements and Challenges in Antenna Design and Rectifying Circuits for Radio Frequency Energy Harvesting
Get PDFThe proliferation of smart devices increases the demand for energy-efficient, battery-free technologies essential for sustaining IoT devices in Industry 4.0 and 5G networks, which require zero maintenance and sustainable operation. Integrating radio frequency (RF) energy harvesting with IoT and 5G technologies enables real-time data acquisition, reduces maintenance costs, and enhances productivity, supporting a carbon-free future. This survey reviews the challenges and advancements in RF energy harvesting, focusing on far-field wireless power transfer and powering low-energy devices. It examines miniaturization, circular polarization, fabrication challenges, and efficiency using the metamaterial-inspired antenna, concentrating on improving diode nonlinearity design. This study analyzes key components such as rectifiers, impedance matching networks, and antennas, and evaluates their applications in biomedical and IoT devices. The review concludes with future directions to increase bandwidth, improve power conversion efficiency, and optimize RF energy harvesting system designs
Design and Synthesis of Novel Aminoindazole-pyrrolo[2,3-b]pyridine Inhibitors of IKKα That Selectively Perturb Cellular Non-Canonical NF-κB Signalling
Get PDFThe inhibitory-kappaB kinases (IKKs) IKKα and IKKβ play central roles in regulating the non-canonical and canonical NF-κB signalling pathways. Whilst the proteins that transduce the signals of each pathway have been extensively characterised, the clear dissection of the functional roles of IKKα-mediated non-canonical NF-κB signalling versus IKKβ-driven canonical signalling remains to be fully elucidated. Progress has relied upon complementary molecular and pharmacological tools; however, the lack of highly potent and selective IKKα inhibitors has limited advances. Herein, we report the development of an aminoindazole-pyrrolo[2,3-b]pyridine scaffold into a novel series of IKKα inhibitors. We demonstrate high potency and selectivity against IKKα over IKKβ in vitro and explain the structure–activity relationships using structure-based molecular modelling. We show selective target engagement with IKKα in the non-canonical NF-κB pathway for both U2OS osteosarcoma and PC-3M prostate cancer cells by employing isoform-related pharmacodynamic markers from both pathways. Two compounds (SU1261 [IKKα Ki = 10 nM; IKKβ Ki = 680 nM] and SU1349 [IKKα Ki = 16 nM; IKKβ Ki = 3352 nM]) represent the first selective and potent pharmacological tools that can be used to interrogate the different signalling functions of IKKα and IKKβ in cells. Our understanding of the regulatory role of IKKα in various inflammatory-based conditions will be advanced using these pharmacological agents
Towards efficient IoT communication for smart agriculture: A deep learning framework
Get PDFThe integration of IoT (Internet of Things) devices has emerged as a technical cornerstone in the landscape of modern agriculture, revolutionising the way farming practises are viewed and managed. Smart farming, enabled by interconnected sensors and technologies, has surpassed traditional methods, giving farmers real-time, granular information into their farms. These Internet of Things devices are responsible for collecting and sending greenhouse data (temperature, humidity, and soil moisture) for the required destination, to provide a comprehensive awareness of environmental factors critical to crop growth. Therefore, ensuring that the received data are accurate is a challenge, thus this paper investigates the optimization of Agriculture IoT communication, proposing a complete strategy for improving data transmission efficiency within smart farming ecosystems. The proposed model intends to maximize energy efficiency and data throughput in the context of essential agricultural factors by using Lagrange optimization and a Deep Convolutional Neural Network (DCNN). The paper focus on the ideal communication required distance between IoT sensors that measure humidity, temperature, and water levels and central control systems. The investigation emphasizes the critical necessity of these data points in guaranteeing crop health and vitality. The proposed technique strives to improve the performance of agricultural IoT communication networks through the integration of mathematical optimization and cutting-edge deep learning. This paradigm change emphasizes the inherent link between precise achievable data rate and energy efficiency, resulting in resilient agricultural ecosystems capable of adjusting to dynamic environmental conditions for optimal crop output and health
An Optimised CNN Hardware Accelerator Applicable to IoT End Nodes for Disruptive Healthcare
Get PDFIn the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized implementation of a Convolutional Neural Network (CNN) on the Basys3 FPGA, designed specifically for accelerating the classification of cytotoxicity in human kidney cells. Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. The CNN, quantized to 8-bit precision and tailored for the FPGA’s resource constraints, significantly accelerates training by a factor of three while consuming only 1.33% of the power compared to a traditional software-based CNN running on an NVIDIA K80 GPU. The network architecture, composed of seven layers with excessive hyperparameters, processes downscale grayscale images, achieving notable gains in speed and energy efficiency. A cornerstone of our methodology is the emphasis on parallel processing, data type optimization, and reduced logic space usage through 8-bit integer operations. We conducted extensive image pre-processing, including histogram equalization and artefact removal, to maximize feature extraction from the augmented dataset. Achieving an accuracy of approximately 91% on unseen images, this FPGA-implemented CNN demonstrates the potential for rapid, low-power medical diagnostics within a broader IoT ecosystem where data could be assessed online. This work underscores the feasibility of deploying resource-efficient AI models in environments where traditional high-performance computing resources are unavailable, typically in healthcare settings, paving the way for and contributing to advanced computer vision techniques in embedded systems
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