The Interpretation of Vital Signs and Other Vital Bedside Information: Expanding the Paradigm

This chapter reviews the symptoms, signs, and other vital information that can and should be obtained at the bedside of the sick; this information should be recorded, vetted for accuracy, and if it changes responded to promptly and appropriately. Some vital information, such as age, sex, and body weight, are stable, whereas others are dynamic and include the traditional vital signs, breathlessness, other subjective patient feelings, changes in breathing, weakness, mobility, and mental status; changes in these are associated with higher in-hospital mortality, higher resource use, longer length of stay, and higher long-term mortality.The five vital signs of respiratory rate, temperature, pulse rate, blood pressure, and oxygen saturation are indicators of hypoperfusion and hypoxemia, which are the final common pathways of clinical deterioration and death. Little else is known about the changes and trends of individual vital signs during the entire course of acute illness in hospital. Therefore, the best judge as to whether a vital sign value is appropriate for a clinical situation is how patients feel and their mental and physical functions. It is unclear if routinely measuring vital signs is effective at promptly detecting adverse events, and to date, there are no high-quality, large, well-controlled studies of continuous vital monitoring that show that it is of benefit. Although patients with three or more seriously abnormal vital signs will require prompt intervention to restore circulatory and respiratory stability, for less sick patients, the situation is unclear. It is possible that simply observing these patients, asking them how they “feel”, and “worrying about them” may detect life-threatening illness earlier than frequent routine vital sign measurements

The Liberal Arts Paradox in Higher Education: Negotiating Inclusion and Prestige

Book revie

Psycho-Intelligent Dialogue Agents for Enhancing Emotional Self-Regulation in Autistic Teenagers

Autistic adolescents often experience the inability to identify their emotions and self-regulate them, thus creating the impulse for the construction of intelligent assistive technologies. Building on this premise, this work proposes a novel Psycho-Intelligent Dialogue Agent (PIDA) system, which attempts to incorporate advances in affective computing, contextualized reasoning , and psychotherapeutic dialogues, in aiding emotional self-regulation with teenagers with autism spectrum disorder (ASD). This system integrates a visual emotion recognition model with an adaptive conversational bow. To train the emotion classifier for real time application trained using transfer learning techniques based on the VGG16 architecture of deep convolutional neural networks, it was trained on a specialized dataset comprising of autistic children's facial expressions and achieved an accuracy of 71% at a 5-emotion recognition task. The Effect recognition module serves the context-aware dialogue manager in real time adapting and personalizing the emotional regulation frameworks to be employed. PIDA's dialogues are based on the principles of clinical psychotherapy, with psychotherapeutic techniques and intervention strategies which are individually tuned to the emotional state and contextual parameters of the situation. The system was designed and built salted with caregiver integration features to enable guardians to monitor progress and active participant in the personalization of the intervention. Primary experimental results reflect the feasibility of this dimension in emotional awareness and emotional regulation and coping strategies. To support we provide uninterrupted emotional assistance to autistic young people and offer flexible support resources during and in between emotional therapy appointments

Exploring gameplay to support the development of leadership skills in post-registration district nursing a mixed-method study

Background:Board games are increasingly used in education to support skills such as communication, decision-making and teamwork. This study explored their role in developing leadership skills among post-registration nursing students at a UK university.Methods:Strategy-based board games were introduced to promote team collaboration, strategic thinking and social interaction. Participants, enrolled in a district nursing programme, identified leadership skills during gameplay. Data were collected through questionnaires, written debriefs and group discussions.Results:Key themes included role awareness, decision making, conflict resolution and communication. Participants reported increased self-awareness, stronger leadership insight and improved teamwork and communication.Conclusions:Board games offer a practical, engaging approach to developing leadership skills in nursing education through active learning and team interaction.Implications for practice:Board gameplay encourages increased awareness of leadership styles and personality types, which impacts the quality of patient care, improves communication within teams and enhances staff morale, wellbeing and overall team performance

The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis, and in-hospital mortality

Patients with an elevated admission National Early Warning Score (NEWS) are more likely to die while in hospital. However, it is not known if this increased mortality risk is the same for all diagnoses. The aim of this study was to determine and compare the increased risk of in-hospital mortality associated with an elevated NEWS and different primary discharge diagnoses in unselected emergency admissions to a UK university teaching hospital.BACKGROUNDPatients with an elevated admission National Early Warning Score (NEWS) are more likely to die while in hospital. However, it is not known if this increased mortality risk is the same for all diagnoses. The aim of this study was to determine and compare the increased risk of in-hospital mortality associated with an elevated NEWS and different primary discharge diagnoses in unselected emergency admissions to a UK university teaching hospital.A non-interventional observational study of 122,321 consecutive, unselected, adult patients with complete data admitted as an emergency between 2014 and 2022.METHODSA non-interventional observational study of 122,321 consecutive, unselected, adult patients with complete data admitted as an emergency between 2014 and 2022.The overall in-hospital mortality was 4.3%. Eighty diagnostic groupings accounted for 85.8% of all admissions and 89.4% of all in-hospital deaths. Depending on diagnostic grouping, the risk of mortality associated with an admission NEWS ≥ 3 ranged from 2.3- to 100-fold. For example, the in-hospital mortality of COPD patients increased from 1.9% for those with admission NEWS < 3 to 35.6% for those with NEWS ≥ 3, for chest pain mortality increased from 0.1 to 3.9%, and for patients with an opiate overdose from 0.2 to 7.7%. Conversely, for admission NEWS < 3, aspiration pneumonia and intracranial hemorrhage had in-hospital mortalities of 13.7% and 12.1%, respectively.RESULTSThe overall in-hospital mortality was 4.3%. Eighty diagnostic groupings accounted for 85.8% of all admissions and 89.4% of all in-hospital deaths. Depending on diagnostic grouping, the risk of mortality associated with an admission NEWS ≥ 3 ranged from 2.3- to 100-fold. For example, the in-hospital mortality of COPD patients increased from 1.9% for those with admission NEWS < 3 to 35.6% for those with NEWS ≥ 3, for chest pain mortality increased from 0.1 to 3.9%, and for patients with an opiate overdose from 0.2 to 7.7%. Conversely, for admission NEWS < 3, aspiration pneumonia and intracranial hemorrhage had in-hospital mortalities of 13.7% and 12.1%, respectively.There is enormous variation in the mortality risk associated with an increased admission NEWS in different commonly encountered diagnoses. Therefore, the mortality risk of some 'low risk' conditions can be dramatically increased if their admission NEWS is elevated, whereas some 'high risk' conditions are still likely to die even if their admission NEWS is low.DISCUSSIONThere is enormous variation in the mortality risk associated with an increased admission NEWS in different commonly encountered diagnoses. Therefore, the mortality risk of some 'low risk' conditions can be dramatically increased if their admission NEWS is elevated, whereas some 'high risk' conditions are still likely to die even if their admission NEWS is low

Machine learning driven cyber resilience framework for mobile tactical networks with graph-based threat detection and adversarial security engineering in cyber physical systems

Advanced mobile network connectivity is being fueled by the rapid evolution of 5G and the forthcoming 6G technologies. This however has exposed mobile networks to new cyber threats and security vulnerabilities. Consequently, cyber resilience, that is, the cope to prepare, identify, respond and recover from cyber-related incidents has become crucial. This paper focuses on a cyber resilience framework for mobile networks utilizing machine learning (ML) aiming at emerging threats. Machine Learning supervised, unsupervised and deep learning algorithms can perform anomaly detection, intrusion detection, prediction and automated threat response systems. Major ones like IDS and anomaly detection are discussed and analyzed with practical instances. The study examines and proposes federated learning, reinforcement learning and explainable AI (XAI) suffice in addressing issues of scarcity of data, time-sensitive processing, and emerging cyber threats. Integrating IoT, edge computers and 6G networks can also improve resilience. It is evident that there is great potential for cyber resilience through machine learning however it has been suggested that standardization, benchmarking and effective test frameworks are put in place

Parallel Recommendation for Multi Interactive Resources in Mobile Networks Based on Label Attributes and Behavior Sequence

With the rapid changes in the mobile network environment and the dynamical user's interest, existing recommendation algorithms are unable to provide resources that meet user needs, which means both the accuracy and efficiency of resource recommendation are not good enough. Therefore, this article proposes a parallel recommendation algorithm for multi interactive resource based on label attributes and behavior sequences in mobile network. The proposed method first obtains users' preferences for resources based on label attributes to increase the accuracy of recommendation; and then computes the similarity between resources to remove the redundant resources and improve recommendation efficiency. Then, Deep Convolution Generative Networks (DCGN) is used to process interaction data between multiple users and resources. Here, the input interaction behavior sequence is fed into a dual Gated Linear Unit (GLU) , and Gated Recurrent Unit (GRU) based on attention mechanism is used to extract the change of user's interest. At the same time, a feature crossover module is used to learn the target resource connection to make recommendations more relevant. Finally, a Deep Convolutional Neural Network (DCNN) is used to output the user resource interaction score to complete the resource recommendation. Experimental results show that the Normalized Discounted Cumulative Gain (NDCG) and hit rate are 0.35 and 0.18 respectively when the length of recommendation list is 8, with minimum Logloss 0.2567 and maximum Area Under the ROC Curve (AUC) 0.9157, which means the coverage rate of proposed resource recommendation is high. The resource recommendation takes 46.72 seconds to process large-scale data, which indicates that the proposed algorithm has high recommendation efficiency

Emotionally Adaptive AI Companions for Supporting Routine Management in Autistic Adolescents

Autistic Adolescents usually experience difficulties in the management of emotions, routine transitions and social cue interpretation. Many existing tools that aim to fill in the gap are often non-personalise, static or lack real-time responsiveness in handling these challenges. This study conceptualises and empirically validates a prototype of an emotionally adaptive AI companion that focuses on reducing stress due to routine transition, emotional regulation and social cue interpretation while increasing personalised management by providing contextual support. A quasi-experimental, mixed methods design is adopted. The core of this system conducts facial multimodal emotion recognition through facial expression and simulated voice tone using transfer learning across three CNN architectures (ResNet-18, MobileNetV2, and EfficientNet-B0) as comparison tests. The resulting emotion output is feeds into a contextual engine for real-time personalised interventions which can also be continuously improved through critical feedback-in-the-loop control architecture based on caregiver logs. The key model trade-offs are validated, the findings established that ResNet-18 possesses the highest accuracy of 48%, EfficientNet-B0 with a superior F1 Score of 0.31 and MobileNetV2 proves to be efficient but slightly lower performance compared to other architectures. Simulated user feedback validation resulted in high preliminary acceptability, as high as 87.5% acceptability for an intervention like ”Reassurance”. This validated the utility of this responsive system. This transfer-learning based, multi-modal pipeline is robust. The results of the comparative analysis uncovered a very profound and instructive trade-off between the complexity of models, their efficiency, and performance metrics relating to accuracy versus the F1-scor

Emerging technologies for security and privacy in 6G wireless communication networks

With the approaching implementation of 6G wireless communication networks, there will be opportunities and problems that have never been seen before, notably in privacy and security. In an era of hyper-connected, intelligent, and data-driven communication, this abstract investigates the cutting-edge technologies set to strengthen the integrity of 6G networks and protect users' privacy. Innovative methods, such as homomorphic encryption, differential privacy, and secure multi-party computation, are being investigated to protect the privacy of users. These approaches make it possible to process data without compromising sensitive information, which is in line with the growing desire for technology that protects individuals' privacy. In addition, biometric authentication serves as the primary form of verification, offering an additional layer of identity verification that is both robust and tailored in comparison to traditional approaches. As a result of the fact that it is anticipated that 6G networks would make use of network slicing, security measures are dynamically altered through isolated slices to meet several different service requirements. Wireless transmissions can be made more secure by the utilization of modern beamforming and signal processing techniques, which are part of the physical layer security. Traditional trust assumptions are called into question by the paradigm of zero-trust security models, which advocates for continuous authentication and authorization. This chapter provides a glimpse into the disruptive technologies that are going to determine the landscape of privacy and security in 6G wireless communication networks. These developments are critically important in laying a foundation that is secure, trustworthy, and privacy-focused for the hyper-connected future of wireless communication. The old ways of trusting people are being reconsidered because of a new security approach called zero-trust. Biometric authentication has an extra layer of security and can be more personalized than traditional methods. This approach suggests always checking and allowing access to systems and data, instead of relying on trust. This story shows how new technologies are changing the way we keep things private and safe in 6G wireless networks. These advancements are the basis for a future that is safe, dependable, and focused on privacy in wireless communication