Ubiquitous Access Research Articles

Blockchain‐IoT: A revolutionary model for secure data storage and fine‐grained access control in internet of things

AbstractWith the rapid expansion of the Internet of Things (IoT), cloud storage has emerged as one of the cornerstones of data management, facilitating ubiquitous access and seamless sharing of information. However, with the involvement of a third party, traditional cloud‐based storage systems are plagued by security and availability concerns, stemming from centralized control and management architectures. A novel blockchain‐IoT model that leverages blockchain technology and decentralized storage mechanisms to address these challenges is presented. The model combines the Ethereum blockchain, interplanetary file system, and attribute‐based encryption to ensure secure and resilient storage and sharing of IoT data. Through an in‐depth exploration of the system architecture and underlying mechanisms, it is demonstrated how the framework decouples storage functionality from resource‐constrained IoT devices, mitigating security risks associated with on‐device storage. In addition, data owners and users can easily exchange data with one another through the use of Ethereum smart contracts, fostering a collaborative environment and providing incentives for data sharing. Moreover, an incentive mechanism powered by the FileCoin cryptocurrency is introduced, which motivates and ensures data sharing transparency and integrity between stakeholders. Furthermore, in the proposed blockchain‐IoT model, the proof‐of‐authority system consensus algorithm has been replaced by a delegated proof‐of‐capacity system, which reduces transaction costs and energy consumption. Using the Rinkby Ethereum official testing network, the proposed model has been demonstrated to be feasible and economical, emphasizing its potential to redefine IoT data management.

Ontology-Based Data Collection for a Hybrid Outbreak Detection Method Using Social Media.

Given the persistent global challenge presented by rapidly spreading diseases, as evidenced notably by the widespread impact of the COVID-19 pandemic on both human health and economies worldwide, the necessity of developing effective infectious disease prediction models has become of utmost importance. In this context, the utilization of online social media platforms as valuable tools in healthcare settings has gained prominence, offering direct avenues for disseminating critical health information to the public in a timely and accessible manner. Propelled by the ubiquitous accessibility of the internet through computers and mobile devices, these platforms promise to revolutionize traditional detection methods, providing more immediate and reliable epidemiological insights. Leveraging this paradigm shift, our proposed framework harnesses Twitter data associated with infectious disease symptoms, employing ontology to identify and curate relevant tweets. Central to our methodology is a hybrid model that integrates XGBoost and Bidirectional Long Short-Term Memory (BiLSTM) architectures. The integration of XGBoost addresses the challenge of handling small dataset sizes, inherent during outbreaks due to limited time series data. XGBoost serves as a cornerstone for minimizing the loss function and identifying optimal features from our multivariate time series data. Subsequently, the combined dataset, comprising original features and predicted values by XGBoost, is channeled into the BiLSTM for further processing. Through extensive experimentation with a dataset spanning multiple infectious disease outbreaks, our hybrid model demonstrates superior predictive performance compared to state-of-the-art and baseline models. By enhancing forecasting accuracy and outbreak tracking capabilities, our model offers promising prospects for assisting health authorities in mitigating fatalities and proactively preparing for potential outbreaks.

Loanword Phonology of Spanish Anglicisms: New Insights from Corpus Data

Previous research shows that several factors influence the adaptation of English phonemes in Spanish Anglicisms: speaker age, English proficiency, and geographic distance from the U.S.A, among others.Due to globalization, increased mobility, and the ubiquitous availability of English media, the question arises whether these factors are still relevant in today’s world. For the present study, 70 speakers from Mexico and Spain read a word list containing Anglicisms aloud. A generalized linear mixed effects model was applied to analyze which factors directly influence pronunciation. Results show that the realization of Spanish grapheme-phoneme correspondences plays a major role in the adaptation process. Moreover, the analysis shows that it is exposure to the English language that mainly influences the pronunciation: the more exposure speakers from both countries have to the English language, the more likely they are to imitate the English pronunciation instead of the realization of Spanish grapheme-phoneme correspondences. Finally, the analysis revealed differences not only between the phonemes and the speakers but also between the words included in the study and once more highlighted that every word has a history of its own.

Optimizing electronic health records to support artificial intelligence

Electronic health records (EHRs) provide the most important data sources for artificial intelligence (AI). Gaining access to quality data suitable for advanced analytics continues to be challenging. This rapid review documents the current state of available data; identifies foundational AI data/information needs; and explores the benefits of adopting new and emerging technologies to design and implement next-generation EHRs. Opportunities to optimize EHRs for AI purposes are identified. This review was informed by expert knowledge and shared experiences supported by the literature, including technical standards. Main findings include poor ecosystem-wide infrastructures due to the lack of adopting the right set of standards, and current data and knowledge governance no longer fit for purpose. While many jurisdictions are continuing the use of legacy systems, some forward-looking national health systems and health-care facilities are adopting transformational strategies by adopting a strong data and digital focus to transition to new-generation systems. New foundational-level national infrastructures with strong leadership and governance are essential to enhance the governance and quality of available data, from collection at source throughout the entire data supply chain. Secure and ubiquitous access to high-quality EHR data at scale will foster the evolution of more intelligent and trustworthy AI. Key characteristics of next-generation EHRs supported by currently available technologies and standards that are able to meet digital era demands are provided in this paper. We conclude that the use of generative AI in clinical settings can only be reliably achieved when EHRs are optimized throughout the entire global digital health ecosystem.

The Role of DIKWP Assets in Shaping Economic Systems and Implications for an Intelligent Consultant System in Real Estate Investment

Traditional economic theory revolves around information asymmetry, where some possess more knowledge than others. However, the digital age is upending this paradigm. This research proposes a groundbreaking shift: a transition from an Asymmetric Information Economy to a Symmetric Knowledge and Wisdom Economy, fueled by the ubiquitous availability of data and digital communication. The shifting is worked through DIKWP-12 Chains framework (Data-Information-Knowledge-Wisdom-Purpose), which serves as a key pillar supporting this paradigm shift. It integrates Data, Information, Knowledge, Wisdom, and Purpose into a unified semantic processing approach. This novel approach empowers us to navigate this transformative era. Furthermore, this study goes beyond theory by leveraging the DIKWP chains to construct an Intelligent Consultant System for Real Estate Investment. This system analyzes customer behavior data and captures the wisdom behind their purchasing decisions, offering a unique and data-driven approach to real estate investment consulting.

An interactive approach to semantic enrichment with geospatial data

The ubiquitous availability of datasets has spurred the utilization of Artificial Intelligence methods and models to extract valuable insights, unearth hidden patterns, and predict future trends. However, the current process of data collection and linking heavily relies on expert knowledge and domain-specific understanding, which engenders substantial costs in terms of both time and financial resources. Therefore, streamlining the data acquisition, harmonization, and enrichment procedures to deliver high-fidelity datasets readily usable for analytics is paramount. This paper explores the capabilities of SemTUI, a comprehensive framework designed to support the enrichment of tabular data by leveraging semantics and user interaction. Utilizing SemTUI, an iterative and interactive approach is proposed to enhance the flexibility, usability and efficiency of geospatial data enrichment. The approach is evaluated through a pilot case study focused on urban planning, with a particular emphasis on geocoding. Using a real-world scenario involving the analysis of kindergarten accessibility within walking distance, the study demonstrates the proficiency of SemTUI in generating precise and semantically enriched location data. The incorporation of human feedback in the enrichment process successfully enhances the quality of the resulting dataset, highlighting SemTUI’s potential for broader applications in geospatial analysis and its usability for users with limited expertise in manipulating geospatial data.

IoV-6G+: A secure blockchain-based data collection and sharing framework for Internet of vehicles in 6G-assisted environment

The growing need for wide and ubiquitous accessibility to advanced Intelligent Transportation Systems (ITS) has led to the evolution of conventional Vehicle to Everything (V2X) paradigms into the Internet of Vehicles (IoVs). Next-generation IoVs establish seamless connections among humans, vehicles, Internet of Things (IoT) devices, and service platforms to enhance transit efficiency, road safety, and environmental conservation, with notable advancements in IoV technologies, specifically in secure data exchange and user privacy protection. Additionally, Unmanned Aerial Vehicles (UAVs) are envisioned as scalable and adaptable solutions for comprehensive IoV service coverage. The current approaches in IoV mainly concentrate on local infrastructure setups, overlooking the potential of infrastructure-less IoVs that lack extensive edge facilities. Also, the transmission of data over public channels by vehicles equipped with servers is susceptible to interception and tampering by malicious attackers. Moreover, the substantial amount of real-time data generated by vehicles, IoT devices, travelers, and social interactions pose a significant strain on servers as well as latency issues. To address these security concerns, this research suggests a secure-lightweight data collection and sharing framework for 6G-assisted smart transportation using blockchain and UAV, called IoV-6G+. The process entails drones collecting information and transmitting it to dedicated edge servers that securely aggregate the information and generate transactions on a cloud server. A cloud server securely collects data from edge servers, creates transactions, combines them into blocks, and then confirms and adds these blocks to the blockchain through a voting-based consensus method in a peer-to-peer network of cloud servers. Additionally, the security analysis of our proposed IoV-6G+ is assessed using the “Informal and formal (i.e., Scyther Tool, and Real-or-Random (RoR) Model) methods”, showcasing its efficacy in delivering a secure and efficient authentication mechanism for IoV. Moreover, the proposed IoV-6G+ exhibits lower computational and communication costs as well as energy consumption, along with enhanced security features, when compared to existing authentication frameworks.

Artificial Intelligence Chatbots in Chemical Information Seeking: Narrative Educational Insights via a SWOT Analysis

Artificial intelligence (AI) chatbots are next-word predictors built on large language models (LLMs). There is great interest within the educational field for this new technology because AI chatbots can be used to generate information. In this theoretical article, we provide educational insights into the possibilities and challenges of using AI chatbots. These insights were produced by designing chemical information-seeking activities for chemistry teacher education which were analyzed via the SWOT approach. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way learners interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutors. Their ability to support chemistry learning is impressive. However, the processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption will take time. The diffusion can be supported by integrating LLMs into curricula, relying on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in the context of chemistry teacher education.

Integration of Cloud Computing and Wearable Technology for Enhanced Interactivity

The emergence of wearable computing has revolutionized the way we interact with technology, blurring the lines between the physical and digital worlds. In this research, we explore the dynamic interaction between cloud technology and wearable computing, a synergy that is reshaping the landscape of personal technology and data management. The study delves into how cloud comput-ing provides a powerful platform for wearable devices, enabling enhanced data processing capabili-ties, storage, and ubiquitous access to information. We investigate the various applications of this interaction, ranging from health monitoring to augmented reality, emphasizing the transformative impact on everyday life and various industries. The research also addresses the challenges inherent in this integration, such as data security, privacy concerns, and the need for robust, low-latency communication networks. Through a comprehensive analysis of current trends and future prospects, this study highlights the potential of cloud-assisted wearable technology in creating more personal-ized, efficient, and interconnected experiences. The findings suggest that the convergence of cloud technology and wearable computing not only offers significant benefits in terms of functionality and user experience but also poses critical considerations for data governance and ethical implications in an increasingly connected world.

The Impact of Technology-Enhanced Language Learning on Bilingual Education

Bilingual education stands as a pivotal approach in contemporary educational landscapes, aiming to cultivate bilingualism and enhance academic achievement among students. In recent years, technology-enhanced language learning (TELL) has emerged as a promising avenue to augment bilingual education efforts. This paper presents a thorough examination of the impact of TELL on bilingual education, synthesizing theoretical frameworks, exploring various technologies utilized, assessing effectiveness, and addressing challenges and future directions. The theoretical underpinnings of TELL encompass constructivism, sociocultural theory, and cognitive load theory. Drawing from constructivist principles, TELL engages learners actively in constructing knowledge through meaningful interactions with technology-mediated learning environments. Sociocultural theory accentuates the socio-cultural context of language learning, underscoring TELL's potential to foster collaboration and cultural exchange among bilingual learners. Cognitive load theory informs the design of TELL materials to optimize learning outcomes by managing cognitive resources effectively. Technologies employed in bilingual education span diverse platforms, including computer-assisted language learning (CALL), mobile applications, virtual reality (VR), augmented reality (AR), and online collaboration tools. CALL platforms offer interactive exercises and personalized feedback, while mobile applications afford ubiquitous access to language learning resources. VR and AR technologies provide immersive experiences, enriching cultural understanding and language proficiency. Online collaboration tools facilitate synchronous and asynchronous communication, fostering peer interaction and collaboration. Research findings underscore the effectiveness of TELL in enhancing language proficiency, cultural understanding, and overall learning outcomes in bilingual education settings. TELL interventions have demonstrated significant improvements across language skills, motivating learners and fostering positive attitudes toward language learning. However, challenges such as limited access to technology, digital literacy skills, and the need for teacher training persist, hindering widespread implementation. Addressing these challenges requires concerted efforts to promote equitable access to technology, enhance digital literacy skills, and provide ongoing professional development for educators. Collaborative endeavors among stakeholders are essential to develop inclusive strategies for leveraging technology to support bilingual learners effectively. Future research should focus on innovative TELL approaches, long-term impact evaluation, and best practices for technology integration in bilingual education. TELL holds immense promise in enriching bilingual education by providing interactive, engaging, and culturally relevant learning experiences. By leveraging theoretical frameworks and diverse technologies, TELL has the potential to transform language learning practices and cultivate global citizens proficient in multiple languages. However, concerted efforts are needed to overcome challenges and maximize the benefits of TELL for all bilingual learners, emphasizing the importance of collaboration, research, and advocacy in advancing bilingual education in the digital age.

Educating with Paulo Freire: Teaching and learning on the digital culture

In the so-called digital culture, there is a constant presence of radical transformations on the cognitive and affective dimensions of the relations established among teachers and students. Upon this ubiquitous accessibility of information, the very historical and hierarchically verticalized relation between teachers and students has been increasingly questioned. Upon this situation, the authors in this article aim at stating that the revitalization of concepts developed by Paulo Freire, mainly the ones discussed in the books: ‘Pedagogy of Hope’, that sought a re-encounter with ‘Pedagogy of the Oppressed’, ‘Extension or Communication’ and ‘Pedagogy of Freedom’ became essential to understanding how the relationship between teachers and students can be mutually re-signified. In these times of digital culture, we argue that the revitalization of the critical thinking proposed by Freire is able to foment a dialogical communication both in the pedagogical link shared by teachers and students and in the transformation of information into substantially and ethically experienced concepts. Such substantial and ethical experiences are the ones able to attest that the pedagogy of hope and the pedagogy of freedom are possible, provided they are constituted with the students and not for them.

Early Bird or Night Owl: Insights into Dutch Students’ Study Patterns using the Medical Faculty’s E-learning Registrations

Phenomenon : Educational activities for students are typically arranged without consideration of their preferences or peak performance hours. Students might prefer to study at different times based on their chronotype, aiming to optimize their performance. While face-to-face activities during the academic schedule do not offer flexibility and cannot reflect students’ natural learning rhythm, asynchronous e-learning facilitates studying at one’s preferred time. Given their ubiquitous accessibility, students can use e-learning resources according to their individual needs and preferences. E-learning usage data hence serves as a valuable proxy for certain study behaviors, presenting research opportunities to explore students’ study patterns. This retrospective study aims to investigate when and for how long undergraduate students used medical e-learning modules. Approach : We performed a cross-sectional analysis of e-learning usage at one medical faculty in the Netherlands. We used data from 562 undergraduate multimedia e-learning modules for pre-clinical students, covering various medical topics over a span of two academic years (2018/19 and 2019/20). We employed educational data mining approaches to process the data and subsequently identified patterns in access times and durations. Findings : We obtained data from 70,805 e-learning sessions with 116,569 module visits and 1,495,342 page views. On average, students used e-learning for 16.8 min daily and stopped using a module after 10.2 min, but access patterns varied widely. E-learning was used seven days a week with an hourly access pattern during business hours on weekdays. Across all other times, there was a smooth increase or decrease in e-learning usage. During the week, more students started e-learning sessions in the morning (34.5% vs. 19.1%) while fewer students started in the afternoon (42.6% vs. 50.8%) and the evening (19.4% vs. 27.0%). We identified ‘early bird’ and ‘night owl’ user groups that show distinct study patterns. Insights : This retrospective educational data mining study reveals new insights into the study patterns of a complete student cohort during and outside lecture hours. These findings underline the value of 24/7 accessible study material. In addition, our findings may serve as a guide for researchers and educationalists seeking to develop more individualized educational programs.

Near Real-Time Remote Sensing Based on Satellite Internet: Architectures, Key Techniques, and Experimental Progress

Remote sensing has become an essential tool for geological exploration, disaster monitoring, emergency rescue, and environmental supervision, while the limited number of remote sensing satellites and ground stations restricts the timeliness of remote sensing services. Satellite Internet has features of large bandwidth, low latency, and wide coverage, which can provide ubiquitous high-speed access for time-sensitive remote sensing users. This study proposes a near real-time remote sensing (NRRS) architecture, which allows satellites to transmit remote sensing data via inter-satellite links and offload to the Earth Stations from the satellite that moves overhead. The NRRS architecture has the advantages of instant response, ubiquitous access, and intelligent integration. Based on a test communication constellation, a vehicle-mounted Satcom on-the-move experiment was conducted to validate the presented NRRS architecture. The results show that the whole process from demand collection to image acquisition takes no more than 25 min, which provides an engineering reference for the subsequent implementation of near real-time remote sensing.

Intelligent Networking for Energy Harvesting Powered IoT Systems

As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can severely deteriorate, rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, although the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this article first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this article develops DeepIoTRouting , a distributed and scalable deep reinforcement learning (DRL)-based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization, DeepIoTRouting achieves at least 38.71% improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods.

Highly Tunable Cellulosic Hydrogels with Dynamic Solar Modulation for Energy-Efficient Windows.

Smart windows that can passively regulate incident solar radiation by dynamically modulating optical transmittance have attracted increasing scientific interest due to their potential economic and environmental savings. However, challenges remain in the global adoption of such systems, given the extreme variability in climatic and economic conditions across different geographical locations. Aiming these issues, a methylcellulose (MC) salt system is synthesized with high tunability for intrinsic optical transmittance (89.3%), which can be applied globally to various locations. Specifically, the MC window exhibits superior heat shielding potential below transition temperatures, becoming opaque at temperatures above the Lower Critical Solution Temperature and reducing the solar heat gain by 55%. This optical tunability is attributable to the particle size change triggered by the temperature-induced reversible coil-to-globular transition. This leads to effective refractive index and scattering modulation, making them prospective solutions for light management systems, an application ahead of intelligent fenestration systems. During the field tests, MC-based windows demonstrated a 9°C temperature decrease compared to double-pane windows on sunny days and a 5°C increase during winters, with simulations predicting an 11% energy savings. The ubiquitous availability of materials, low cost, and ease-of-manufacturing will provide technological equity and foster the ambition toward net-zero buildings.

A Dual Pharmacological Strategy against COVID-19: The Therapeutic Potential of Metformin and Atorvastatin.

Metformin (MET) and atorvastatin (ATO) are promising treatments for COVID-19. This review explores the potential of MET and ATO, commonly prescribed for diabetes and dyslipidemia, respectively, as versatile medicines against SARS-CoV-2. Due to their immunomodulatory and antiviral capabilities, as well as their cost-effectiveness and ubiquitous availability, they are highly suitable options for treating the virus. MET's effect extends beyond managing blood sugar, impacting pathways that can potentially decrease the severity and fatality rates linked with COVID-19. It can partially block mitochondrial complex I and stimulate AMPK, which indicates that it can be used more widely in managing viral infections. ATO, however, impacts cholesterol metabolism, a crucial element of the viral replicative cycle, and demonstrates anti-inflammatory characteristics that could modulate intense immune reactions in individuals with COVID-19. Retrospective investigations and clinical trials show decreased hospitalizations, severity, and mortality rates in patients receiving these medications. Nevertheless, the journey from observing something to applying it in a therapeutic setting is intricate, and the inherent diversity of the data necessitates carefully executed, forward-looking clinical trials. This review highlights the requirement for efficacious, easily obtainable, and secure COVID-19 therapeutics and identifies MET and ATO as promising treatments in this worldwide health emergency.

War, forced displacement, and alcohol abuse: experiences and perceptions of war-affected south Sudanese refugee youth living in Bidibidi refugee settlement in northern Uganda.

Refugees are at high risk of alcohol abuse due to their experiences of structural, physical, sexual, and psychological violence in their countries of origin, during flight, and within host communities. Given the prolonged civil war in their country, South Sudanese have continued to flee profound forms of violence and now constitute the largest population of refugees in Uganda. However, little is known about their displacement experiences, as well as the reality of alcohol use and abuse within refugee settlements. Drawing upon the direct voices of a sample of war-affected South Sudanese young people, this article explores their experiences of forced displacement and their links to alcohol abuse, as well as their perceptions regarding appropriate alcohol treatment interventions for refugees in the camp. A total of 22 semi-structured qualitative interviews were conducted with 14 refugee youth (aged 18-25) alongside eight adult key informants who work with the youth (religious leaders, sports coaches, educators, social workers, and settlement administrators). Using thematic analysis, the study revealed a series of key themes influencing and shaping the high incidence of alcohol abuse among the youth. These included traumatic wartime and migration experiences, family separation, poor prospects, and the ubiquitous availability of alcohol in the settlement. In addition, we show how alcohol operates as a strategic tool for survival for the youth, as well as highlight how these perceptions can help to inform alcohol treatment interventions in the Bidibidi refugee settlement. To our knowledge, this is the first in-depth study of alcohol abuse among war-affected South Sudanese refugee youth in Uganda, addressing a significant gap in the current literature on war-affected youth, forced displacement, and alcohol abuse. We contend that involving youth in the design of interventions can be helpful for culturally sensitive and relevant prevention, treatment, and care in refugee settings. In addition, providing employment opportunities and meaningful engagement for growth through social participation can help to address harmful alcohol use among youth in the camps.

Dynamic Traffic Grooming Based on Virtualization-Plane-Aided Optimization for Elastic Optical Satellite Networks

With the increase in global wireless traffic, the use of large-scale satellite networking to provide ubiquitous access is one of the essential trends of future 6G network development. Elastic optical satellite networks (EOSNs) are widely considered a flexible solution for future satellite communication. However, with the continuous proliferation of network devices and users, the growing disparity between user demands and the limited bandwidth and capacity of the network is becoming increasingly noticeable. This has led to issues such as constrained network resource utilization and resource fragmentation. Therefore, EOSNs must efficiently address the challenge of allocating scarce bandwidth resources. Effective traffic grooming methods will be applied to EOSNs to solve the problem of bandwidth shortage. This paper proposed a dynamic traffic grooming algorithm based on virtualization-plane-aided optimization (DTG-VPO) to facilitate the bandwidth allocation for EOSNs. Firstly, the nodes of the alternative paths were graded, and the weights of the subsequent hop links were modified. Then, the path was evaluated using link weights, alternative paths were selected in the virtual and physical topologies, respectively, and a path set was constructed. Finally, a resource block evaluation parameter was designed to quantify the quality of candidate resource blocks and rank them. A series of simulations have evaluated the traffic-blocking probability and wavelength utilization under different traffic loads. The link resource was more fully utilized compared with other traffic grooming algorithms. The blocking probability can be reduced by 75%, while wavelength utilization can be improved by 8.1%.

Towards human-centered AI and robotics to reduce hospital falls: finding opportunities to enhance patient-nurse interactions during toileting.

Introduction: Patients who are hospitalized may be at a higher risk for falling, which can result in additional injuries, longer hospitalizations, and extra cost for healthcare organizations. A frequent context for these falls is when a hospitalized patient needs to use the bathroom. While it is possible that "high-tech" tools like robots and AI applications can help, adopting a human-centered approach and engaging users and other affected stakeholders in the design process can help to maximize benefits and avoid unintended consequences. Methods: Here, we detail our findings from a human-centered design research effort to investigate how the process of toileting a patient can be ameliorated through the application of advanced tools like robots and AI. We engaged healthcare professionals in interviews, focus groups, and a co-creation session in order to recognize common barriers in the toileting process and find opportunities for improvement. Results: In our conversations with participants, who were primarily nurses, we learned that toileting is more than a nuisance for technology to remove through automation. Nurses seem keenly aware and responsive to the physical and emotional pains experienced by patients during the toileting process, and did not see technology as a feasible or welcomed substitute. Instead, nurses wanted tools which supported them in providing this care to their patients. Participants envisioned tools which helped them anticipate and understand patient toileting assistance needs so they could plan to assist at convenient times during their existing workflows. Participants also expressed favorability towards mechanical assistive features which were incorporated into existing equipment to ensure ubiquitous availability when needed without adding additional mass to an already cramped and awkward environment. Discussion: We discovered that the act of toileting served more than one function, and can be viewed as a valuable touchpoint in which nurses can assess, support, and encourage their patients to engage in their own recovery process as they perform a necessary and normal function of life. While we found opportunities for technology to make the process safer and less burdensome for patients and clinical staff alike, we believe that designers should preserve and enhance the therapeutic elements of the nurse-patient interaction rather than eliminate it through automation.

A service function chain mapping scheme based on functional aggregation in space-air-ground integrated networks

Being a novel network architecture, the Space-air-ground integrated network (SAGIN) offers advantages such as extensive network coverage and seamless ubiquitous access. However, it also encounters the challenge of balancing a growing user demand with limited network service resources. In order to further improve the utilization rate of network resources, a group of virtual network functions can be connected according to certain business logic to form a dynamic reconfigurable service function chain. This can provide diversified and high-quality network services for users. So by aggregating the same type of network function, we can reduce the cost of service chain mapping. Based on the above ideas, this paper proposes an Aggregation of Service Function Chain mapping algorithm (A-SFC). The aggregated service function chain topology is mapped according to an improved isomorphic graph search mapping algorithm, resulting in a deployment solution that minimizes the mapping cost by aggregating static SFC requests by end-system division. Experimental results show that the algorithm is able to find an effective mapping scheme under resource constraints. In addition to reducing computation, the algorithm effectively minimizes the consumption of computational resources while delivering excellent performance in terms of service delay and mapping cost.