Ubiquitous Access Research Articles

297. Comparative In Vitro Microbiologic Activity of Two Antimicrobial Intravenous Connectors

Abstract Background Intravenous (IV) connectors are ubiquitous access junctions in vascular access circuits. In accessing IV connectors (IVCs) for infusions, they become vulnerable to microbial contamination and colonization. Antimicrobial IVCs are treated with antimicrobial agents to resist microbial colonization. In this in vitro study, we compared the microbiologic efficacy of IVCs treated with silver and with a novel coating of Minocycline (M), Rifampin (R) and Chlorhexidine (C). MRC vs Silver Antimicrobial IV Connectors: 7-Day Microbiologic Challenge Test Colonization of MRSA, P. aeruginosa, and C. albicans on silver and MRC IVCs following 7 days of elution. Non-antimicrobial IVCs were used as positive controls. Data is presented as the median CFU/connector from six replicates; bars indicate the range. Methods M, R, and C were spray coated onto IVCs in sequential laminate layers to produce MRC IVCs. Commercially available silver IVCs were purchased (Antimicrobial Microclave Connector, ICU Medical Inc., San Clemente, CA). MRC and silver IVCs were immersed separately in tubes filled with 50% serum + 50% saline to simulate elution of the coatings. After 7 days elution (typical interval of use prior to replacement of IV connectors), eluted connectors were exposed to challenge inocula (5x105 CFU/mL) of key CLABSI clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA), multidrug-resistant Pseudomonas aeruginosa (PA), and Candida albicans (CA) for 24 hours at 37C. The number of viable colonizing microbes (CFU/connector) were enumerated by sonication, serial dilution, plating, and counting. Non-coated IVCs were positive controls. Log10 Reduction of MRC and Silver IV Connectors vs Positive Control Log10 reduction of the median number of CFU/connector recovered from six replicates following colonization of MRSA, P. aeruginosa, and C. albicans to silver IVCs and MRC IVCs. Results Figure 1 presents the median number of CFU/connector with range bars indicating the highest and lowest number of viable colonies recovered from six replicates after 7 days of elution and following 24-hour microbial challenges with MRSA, PA, and CA. Table 1 shows the median log reductions between positive control and coated connectors with p-values comparing MRC and silver IVCs calculated by Wilcoxon rank sum test. Conclusion The absence of microbial colonization of the MRC IVCs demonstrates the efficacy and durability of the novel MRC coating. The silver IVC was unable to fully eradicate biofilms of any of the tested pathogens. MRC IVCs were superior to silver IVCs (p< 0.05) for all pathogens tested. MRC-coated IVCs merit further testing in vitro and in vivo for their potential to prevent CLABSI. Disclosures All Authors: No reported disclosures

Anti-consumption agents: Tirzepatide and semaglutide for treating obesity-related diseases and addictions, and improving life expectancy.

American culture encourages overconsumption, fueled by ubiquitous availability and pervasive marketing of ultra-processed foods and other addictive substances. This chronic overindulgence has contributed to rising rates of obesity, type 2 diabetes (T2D), substance abuse, mental health disorders and premature mortality. Glucose-like peptide-1 agonists (GLP-1RAs) affect the brain's reward pathway that mediates addiction to foods and various other substances. Evolving data suggest that tirzepatide and semaglutide may be the first effective "anti-consumption" agents with potential applications in reducing food cravings, obesity, alcohol consumption, nicotine addiction, recreational drug use, and even uncontrollable shopping behaviors. Tirzepatide and semaglutide, unlike prior weight-loss drugs, are effective and relatively safe/well-tolerated medications that are associated with reduced risks for myocardial infarction, stroke, cardiovascular death, heart failure, progressive kidney and liver disease, obstructive sleep apnea, debilitating osteoarthritis, polycystic ovarian syndrome, neurodegenerative disease and premature mortality. Observational studies show that GLP-1RAs are associated with spontaneous nonvolitional reductions in use of alcohol, nicotine, and recreational drugs. Because obesity and substance abuse are so prevalent in the United States, GLP-1RA drugs may be uniquely helpful in addressing overconsumption and addiction issues thereby improving overall health and life expectancy.

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities through collecting movement data. For specific applications of sensor based human activity recognition (HAR), however, logistical challenges and burgeoning costs render especially the ground truth annotation of such data a difficult endeavor, resulting in limited scale and diversity of datasets available for deriving effective HAR systems and less than ideal recognition capabilities. Transfer learning, i.e., leveraging publicly available labeled datasets to first learn useful representations that can then be fine-tuned using limited amounts of labeled data from a target domain, can alleviate some of the performance issues of contemporary HAR systems. Yet they can fail when the differences between source and target conditions are too large and / or only few samples from a target application domain are available – each of which are typical challenges in real-world human activity recognition scenarios. In this paper, we present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework–Cross-Domain HAR–which follows the teacher-student self-training paradigm to more effectively recognize activities with very limited label information. It bridges conceptual gaps between source and target domains, including sensor locations and type of activities. Cross-Domain HAR enables substantial performance improvements over the state-of-the-art in sensor-based HAR scenarios. Through our extensive experimental evaluation on a range of benchmark datasets we specifically demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios. We also present a detailed analysis into how the individual components of our framework affect downstream performance and provide practical suggestions for using the framework in real-world applications.

An empirical analysis of user intention to use chatbots for airline tickets consultation

PurposeThis study aims to empirically analyze the factors influencing users’ intention to use chatbots for airline ticket consultation. It seeks to introduce a comprehensive framework based on the technology acceptance model (TAM) that integrates key factors alongside traditional TAM constructs to understand what drives behavioral intention to use chatbots in the context of airline ticket consultation.Design/methodology/approachThe study uses the partial least squares-structural equation modeling (PLS-SEM) approach to validate the proposed model empirically. Data were collected through a survey questionnaire distributed to potential users in Saudi Arabia, with 393 valid responses from a total of 409 received being included in the analysis.FindingsThe empirical analysis confirms the significance of perceived usefulness and user satisfaction as direct determinants of behavioral intention. Additionally, it reveals that factors such as perceived ubiquitous access, perceived completeness, perceived accuracy, perceived unbiased response and perceived convenience have both direct and indirect significant impacts on the behavioral intention to use chatbots for airline ticket consultation.Originality/valueThis research advances theoretical understanding and holds practical implications for designing and implementing effective chatbot services. By investigating the complex interplay of these factors, the study makes substantive contributions to both theoretical advancements and practical applications in the field, particularly in enhancing the user experience and acceptance of chatbots for airline ticket consultations.

Simplify, Consolidate, Intervene: Facilitating Institutional Support with Mental Models of Learning Management System Use

Measuring instructors' adoption of learning management system (LMS) tools is a critical first step in evaluating the efficacy of online teaching and learning at scale. Existing models for LMS adoption are often qualitative, learner-centered, and difficult to leverage towards institutional support. We propose depth-of-use (DOU): an intuitive measurement model for faculty's utilization of a university-wide LMS and their needs for institutional support. We hypothesis-test the relationship between DOU and course attributes like modality, participation, logistics, and outcomes. In a large-scale analysis of metadata from 30000+ courses offered at Virginia Tech over two years, we find that a pervasive need for scale, interoperability and ubiquitous access drives LMS adoption by university instructors. We then demonstrate how DOU can help faculty members identify the opportunity-cost of transition from legacy apps to LMS tools. We also describe how DOU can help instructional designers and IT organizational leadership evaluate the impact of their support allocation, faculty development and LMS evangelism initiatives.

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.

Secondary School – Higher Education Institution Continuity on the Example of Teaching Computer Science and Information Technology

The problem of continuity at different levels of education is not new for pedagogy, teaching individual disciplines, including computer science and information technology. It is impossible not to take into account that information technologies are an independent discipline, a means through which the transformation of school and university education has been taking place in recent decades. The content of informatics at school and university should to a certain extent correspond to the level of science development and the requirements of society. The variety of digital tools and the ubiquitous availability of information create huge opportunities for their creative use within their discipline, related natural science subjects, and in various other spheres of human activity. Based on a comparative analysis, the article draws conclusions about the problems faced by both domestic and foreign specialists in the field of education. The article analyzes the strengths and weaknesses, opportunities and problems of the school and university education systems in Russia and abroad, and considers their relationship. It is substantiated that the lack of consistency and discreteness in the process of teaching computer science and information technology at school lead to a discrepancy between the training of school graduates and the requirements for the basic knowledge of first-year university students. The article presents the results of the analysis of basic curricula at school (in the field of computer science) and curricula in the preparation of future computer science teachers at the university (using the Transbaikal State University as an example). The authors analyze the results of diagnostic testing, which was conducted with first-year students of the Transbaikal State University in September 2023/2024, in order to monitor their existing knowledge and skills in the field of computer science. The article suggests ways to solve the problem of secondary school – higher education institution continuity in the field of computer science. The results of the study can be used in the preparation of future computer science teachers in higher educational institutions. Keywords: continuity of education, lifelong education, computer science and educational technologies, information competence, computer literacy

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

An interactive approach to semantic enrichment with geospatial data

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

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

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.