Advanced Services Research Articles

Follow up care for adults with diabetes treated for severe hypoglycemia by emergency medical Services, 2013–2019

AimsTo capture the types and content of healthcare encounters following severe hypoglycemia requiring emergency medical services (EMS) and to correlate their features with subsequent risk of severe hypoglycemia. MethodsA retrospective cohort was obtained by linking data from a multi-state health system and an advanced life support ambulance service. This identified 1977 EMS calls by 1028 adults with diabetes experiencing hypoglycemia between 1/1/2013–12/31/2019. We evaluated the healthcare engagement over the following 7 days to identify rates of discussion of hypoglycemia, change of diabetes medications, glucagon prescribing, and referral for diabetes. ResultsRates of hypoglycemia discussion increased with escalating levels of care, from 11.5 % after EMS calls without emergency department (ED) transport or outpatient clinical encounters to 98 % among hospitalized patients with outpatient follow-up. EMS transport and outpatient follow-up were associated with significantly higher odds of discussion of hypoglycemia (OR 60 and OR 22.1, respectively). Interventions were not impacted by previous severe hypoglycemia within 30 days. Prescription of glucagon was rare among all patients. ConclusionsInterventions to prevent recurrent hypoglycemia increase with escalating levels of care but remain inadequate and inconsistent with clinical guidelines. Greater attention is needed to ensure timely diabetes-related follow-up and treatment modification for patients experiencing severe hypoglycemia.

Conceptualizing service ethics for the complexity of modern service interactions

ABSTRACT The emergence of digital service platforms enabled numerous interaction effects that remain largely unexplored, especially when it comes to the intended or unintended impacts on non-customers. This article conceptualises service ethics for modern interactions enabled by digital service platforms. The conceptual framework is illustrated through two narratives of non-customers intentionally and unintentionally exploited by customer interactions enabled by digital service platforms. By integrating theoretical insights with illustrative narratives, this article demonstrates the potential impact of digital service platforms on non-customer well-being, highlighting instances of exploitation and unintended consequences. This study advances service research by focusing on non-customers who might experience intentional or unintentional exploitation. Furthermore, this article outlines a future research agenda for exploring and advancing the understanding of service ethics along with implications for fostering ethical business practices and shaping ethical societal norms.

The student staffing advantage: Data Science Consulting Service at NC State University Libraries

AbstractThe primarily peer‐to‐peer, graduate student‐staffed Data Science Consulting Service at NC State University Libraries, within the Data & Visualization Services (DVS) department and collaborating closely with the Data Science Academy (DSA), has established a sustainable service and staffing model focused on providing broad data science analytic support to researchers across the university community. The service addresses the needs of university researchers who possess domain knowledge in their fields of study but a skills gap in the data science competencies required for research. The literature shows that it has been difficult for libraries to cover these needs with existing staffing models. Few universities follow the model practiced at NC State University, so a scan of the current landscape of data science consulting at universities across the country was performed to establish context. The support model and its advantages are described, including partnership with the DSA, student success, model sustainability and future directions for the service. Through a summary of the DVS assessment and needs evaluation process, the service's advantages in staying ahead of patron needs are illustrated. This scalable, sustainable, student‐focused model could be implemented by similar research institutions to expand the capacity of their technical research services.

The spatial interlocking of commercial office real estate and advanced producer services: a central flow theory lens

ABSTRACT The paper extends geographical enquiry into the external urban relations described by central flow theory in an exploration of the roles of commercial office real estate (CORE) and advanced producer services (APS) as a conduit for inter-city flows of finance in corporate globalization. The analysis investigates and benchmarks the spatial overlap between these city-based service networks and uses this to consider how their respective servicing strategies influence international capital flows. We find that the networks are interlocked, as the connectivity in one network can significantly explain the connectivity in the other. Both CORE and APS services promote cross-border flows of finance. Cities providing multinational CORE and APS services are able to articulate direct as well as indirect inter-city capital flows and spillovers to non-service sectors.

Investing of Chatbots to Enhance the Library Services

Artificial Intelligence (AI) has become an indispensable tool in our daily lives and is a concept that refers to complex tasks that usually require significant human effort. Chatbots stand out as one of the primary applications of artificial intelligence, aiming to foster an interactive dialogue environment between humans and machines to address inquiries through textual or voice interfaces that simulate human cognition. Libraries can serve as an ideal environment for implementing this technology, where it can deliver advanced information services and streamline repetitive routine tasks typically performed by library staff. The paper discusses the applications of chatbots in automating library services, including reference services, user recommendations, information retrieval, data analysis, and other related processes, and their mechanism of action in processing natural language queries and responding to them accurately. Through a web survey, the study identified eighteen chatbot models that could be implemented in libraries. This survey provided details about these bots, including their type (free or commercial), ownership, release date, operating platforms, and key features. This allows libraries to choose from them. Integrating chatbots into libraries will open new horizons for enhancing their services and improving the user experience. Despite the benefits offered by these chatbots, they face several challenges and concerns regarding their usage, notably data privacy and security, and bias in retrieval. It is expected that in the future, these chatbots will become a fundamental component of providing library services.

AI-HybridChain: Picturized authentication and DRL based access control method with secure two fold revocation for ensuring cloud computing security

The Internet of Things is susceptible to seepage of private information during the data sharing. To circumvent this problem, access control and secure data sharing have been familiarized in cloud- IoT which is prevalent now days due to it storage and data access services. An intrusion detection implementation defends the veracity of the data sharing confining record are stored in a data base by recognizing when something changed unpredictably so that security and privacy are major anxieties. Therefore, it is crucial to develop the security apparatus that perceive and mitigate any feasible attack in cloud security services. In this paper, we proposed secure access control method with two-fold revocation for ensuring cloud computing security by hybrid blockchain (AI-Hybrid chain). Initially, all the users and services are registered their credentials to Secure Agent (SA) and provides image-based user authentication. Then, SA provides a long-term secret key which is generated by Improved Advanced Encryption Standard (IAES) algorithm, both user and service credentials are encrypted and stored in cloud chain. After that, gateway verifies the incoming requests are legitimate or illegitimate. For verifying the incoming packets, the proposed work utilizes Di-Fuzzy Inference System (Di-FIS) based on effective metrics. Further, legitimate request is optimally classified as sensitive and non-sensitive request for achieving secure service allocation using Tomtit Flock Optimization algorithm (TFO) based on optimal features. Once the service is allocated to users, we perform novel access control mechanism based on role and attribute of the users, the trust can be validated using Transfer Learning based Double Deep Q Network (TL-DDQN). Furthermore, virtual firewall is deployed in the cloud environment for analyzing the user storage request traffic as normal validated using Transfer Learning based Double Deep Q Network (TL-DDQN). Furthermore, virtual firewall is deployed in the cloud environment for analyzing the user storage request traffic as normal and malicious based on Contractive Residual Network (Con-ResNet). The implementation of proposed work is conducted by Network Simulator – 3.26 and the performance of the proposed AI-Hybrid chain model is itemized based on various performance metrics in terms of attack detection rate, overhead, success ratio, throughput, traffic rate, delay, response time and unauthorized access.

Testing service infusion in manufacturing through machine learning techniques: looking back and forward

PurposeResponding to calls for deeper analysis of the conceptual foundations of service infusion in manufacturing, this paper examines the underlying assumptions that: (i) manufacturing firms incorporating services follow a pathway, moving from pure-product to pure-service offerings, and (ii) profits increase linearly with this process. We propose that these assumptions are inconsistent with the premises of behavioural and learning theories.Design/methodology/approachMachine learning algorithms are applied to test whether a successive process, from a basic to a more advanced offering, creates optimal performance. The data were gathered through two surveys administered to USA manufacturing firms in 2021 and 2023. The first included a training sample comprising 225 firms, whilst the second encompassed a testing sample of 105 firms.FindingsAnalysis shows that following the base-intermediate-advanced services pathway is not the best predictor of optimal performance. Developing advanced services and then later adding less complex offerings supports better performance.Practical implicationsManufacturing firms follow heterogeneous pathways in their service development journey. Non-servitised firms need to carefully consider their contextual conditions when selecting their initial service offering. Starting with a single service offering appears to be a superior strategy over providing multiple services.Originality/valueThe machine learning approach is novel to the field and captures the key conditions for manufacturers to successfully servitise. Insight is derived from the adoption and implementation year datasets for 17 types of services described in previous qualitative studies. The methods proposed can be extended to assess other process-based models in related management fields (e.g., sand cone).

Practical applications of advanced cloud services and generative AI systems in medical image analysis

The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intelligence technologies represented by computer vision, natural language processing, and machine learning have been widely penetrated into diverse scenarios such as medical imaging, health management, medical information, and drug research and development, and have become an important driving force for improving the level and quality of medical services. The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate synthetic data, enhance images, aid in anomaly detection, and facilitate image-to-image translation. Despite challenges like model complexity, the applications of generative models in healthcare, including Med-PaLM 2 technology, show promising results. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes. However, ethical considerations and collaboration among stakeholders are essential for responsible implementation. Through experiments leveraging GANs to augment brain tumor MRI datasets, the study demonstrates how generative AI can enhance image quality and diversity, ultimately advancing medical diagnostics and patient care.

Technical Perspective: Graph Theory for Data Privacy: A New Approach for Complex Data Flows

Nearly all of the world's population now uses online services that request personal information, covering almost every aspect of our lives. The abundance of personal data in digital form has brought incredible benefits to end users, enabling them to access personalized and advanced services based on the analysis of the data collected. This capability has dramatically improved the user experience in various application domains, ranging from healthcare to e-commerce, finance, logistics, and entertainment, to name a few. Numerous technological advancements in the field of big data have enabled this massive processing of personal data, and recent advances in AI data processing capabilities will expand the ways in which service providers will use personal data in the coming years. Machine learning algorithms, powered by AI, will be used to make increasingly accurate predictions about user behavior by uncovering hidden correlations within massive data sets. There is therefore a tension between the desire to fully exploit personal data in such ecosystems and the need to provide strong privacy and transparency guarantees to the individuals whose data is being exploited. Privacy protection is further complicated because data processing is typically not performed in isolation but through pipelines of different services, with each step making inferences about the personal data consumed by the services in subsequent steps.

Deep Sea Fisherman’s App using WWSN

In this study, we present a method for recognizing daily living activities using Doppler sensors with extended detection ranges, addressing key challenges in cost, accuracy, user convenience, and privacy preservation. We highlight the importance of accurate and affordable activity recognition for enabling advanced services in energy-saving home appliance control and other daily life applications. The proposed method leverages Doppler sensors' capabilities to detect dynamic objects and achieve precise recognition of a wide range of activities, including location-independent tasks like reading. Our evaluation includes optimizing recognition accuracy through logistic regression and assessing ensemble methods to enhance performance. Additionally, we explore the impact of sensor reduction strategies on recognition accuracy, demonstrating the potential of this approach to streamline sensor deployment while maintaining effectiveness. This research contributes to advancing sensor-based technologies for improving everyday life services, emphasizing practical solutions that prioritize cost-effectiveness and user experience

Bouncing your way to labour and birth using biomechanics and fetal optimal positioning

Background/Aims ‘Labour hopscotch’ is a visual tool that encourages activity during pregnancy and childbirth. It has been used in one advanced midwife practitioner service since 2017 and more widely across in Ireland since 2020. The biomechanics for birth toolkit was added to advanced midwife practitioner care in 2022. This study's aim was to ascertain if the biomechanics for birth toolkit impacted gestation of spontaneous onset of labour rates and birth outcomes. Methods A retrospective study of onset of labour and birth outcomes was conducted for women before (n=155) and after (n=154) the biomechanics for birth toolkit was incorporated into care. Results Before the toolkit was included, there was a 57.2% spontaneous onset of labour rate, with 33.2% birthing before 41 weeks. After the kit's inclusion, there was a 72.2% spontaneous onset of labour, with 91.8% birthing before 41 weeks. Induction rates dropped from 42.8% to 27.8%, with emergency caesarean section rates following induction dropping from 33.1% to 23.8%. Conclusions Using the biomechanics for birth toolkit alongside the labour hopscotch tool could increase spontaneous onset of labour rates, optimise physiological birth, reduce inductions and emergency caesarean section following induction.

Unveiling the role of congruity in service robot design and deployment

PurposeThis paper aims to examine the congruency effects of physically embodied robots in service encounters, which addressed a significant research gap concerning the synthesis of robot design elements (e.g., appearance and voice) and their service purposes.Design/methodology/approachGrounded in congruity theory and human-robot interaction literature, this study conducted a pretest and two experimental studies revealing the need to view robot design holistically and recognizing the pivotal role of congruity in shaping consumers’ service robot adoption. The moderating role of service purposes (utilitarian vs hedonic) was also investigated in terms of robot design and consumer reactions.FindingsConsumers generally tend to favor robots with congruent designs, particularly for utilitarian service purposes. The serial mediation through perceived congruence and perceived intelligence explains such a favorite tendency.Practical implicationsThis study advances service robot design research by highlighting the critical role of congruity in enhancing consumer engagement. It supports the use of comprehensive, congruent designs for services with utilitarian purposes and recommends adaptable designs for hedonic settings.Originality/valueThis study addressed the research gap by examining service robot design from a holistic perspective. The research findings highlight the importance of congruency effects in service robot design and deployment and provide valuable insights and guidelines to industry practitioners for optimal investment in service robots.

Patient-related Factors Associated with Potentially Unnecessary Transfers for Pediatric Patients with Asthma: A Retrospective Cohort Study.

Asthma is a common chronic medical condition among children and the most common diagnosis associated with interfacility transports for pediatric patients. As many as 40% of pediatric transfers may be unnecessary, resulting in potential delays in care and unnecessary costs. Our objective was to identify the patient-related factors associated with potentially unnecessary transfers for pediatric patients with asthma. We used patient care data from the California Department of Health Care Access and Information patient discharge and emergency department (ED) datasets to capture ED visits where a pediatric patient (age 2-17years) presented with asthma and was transferred to another ED or acute care hospital. The outcome of interest was a potentially unnecessary transfer, defined as a visit where length of stay after transfer was <24 hours and no advanced services were used, such as respiratory therapy or critical care. Patient-related characteristics were extracted, including age, gender, race/ethnicity, primary language, insurance status, and clinical characteristics. First, we used descriptive statistics to compare necessary vs unnecessary transfers. Second, we used generalized estimating equations accounting for clustering by ED to estimate odds ratios (OR) and identify factors associated with potentially unnecessary transfers. A total of 4,233 pediatric ED patients were transferred with a diagnosis of asthma, including 461 (11%) transfers that met criteria as potentially unnecessary. Median age was 12years (interquartile range 7-15), and 46% were female. Factors associated with increased odds of potentially unnecessary transfer while controlling for key factors included younger age (eg, 2-5years, OR 2.0, 95% confidence interval [CI] 1.4-2.9), male gender (OR 1.4, 95% CI 1.1-1.7), and Hispanic ethnicity (OR 1.6, 95% CI 1.2-2.1), while multiple hospitalizations for asthma per year was associated with decreased odds (OR 0.2, 95% CI 0.1-0.4). Several patient-related factors were associated with increased or decreased odds of potentially unnecessary transfers among pediatric patients presenting to the ED with asthma. These factors can be considered in future work to better understand, predict, and reduce unnecessary transfers and their negative consequences.

Adversarial Service Networks: A Study of Service Firms’ Response to Manufacturer-led Servitization in Aviation

This study examines the adversarial dynamics that emerge when service firms assess that manufacturers' servitization initiatives pose an existential risk to their survival as independent organizations. Adopting an industrial networks perspective, this investigation explores how service firms respond to manufacturers' servitization initiatives that threaten to disintermediate them. The study explores how Maintenance, Repair, and Overhaul firms (MROs) in the aviation industry respond to servitization initiated by Original Equipment Manufacturing firms (OEMs). Empirically, interviews with 49 experienced managers were conducted in the service network to understand their strategic pathways. Their responses reveal that MROs can resist servitization by strengthening their relationships with airlines or developing service-led advanced services. Alternatively, MROs can support OEMs' servitization by becoming subcontractors or licensed resource integrators. This study shows that servitization depends on the orchestration of service network actors with differing interests, making it contingent and multilateral.

Reality and Challenges of Adapting Financial Technologies in Islamic Banks: An Overview of The Arab Countries

This study aims to explain what Financial Technology (Fin-tech) is, its advantages, and current financial platforms of Fin-tech for Islamic banks. The current study used descriptive methodology to analyze the reality and challenges of Fin-tech in Islamic banks. The study reached several conclusions including: that Fin-tech is important for financial inclusion in Islamic banks as it helps to reach all parts of the world, and all segments of society. Also, that although Islamic banks while suffering from a weak structure, they managed to open electronic windows for advanced services, rather than using traditional windows, where mobile banking was one of the most important advancements in this area. Finally, Islamic banks managed to establish several platforms for Fin-tech based on Islamic Sharia', which are still growing. On the other hand, some problems relating to adapting Fin-tech in Islamic banks in the Arab countries included mainly: the overall weak financial and organizational structures of Islamic banks which limit their ability to keep pace with other industries globally. The study recommended Islamic banks to work on rechanneling their funds into developing their technological infrastructure, the need to further develop financial platforms, additional training and education on crypto-currencies, and further discussions on how to develop these in accordance with Islamic Sharia'.

Multi-dimensional management of smart distribution networks: Comparative analysis of box and polyhedral methods for modeling uncertainties

By using advanced automation services, distribution systems have been transformed from passive networks to smart and active infrastructures. In this regard, due to the complex interaction of different entities, operators have faced multifaceted challenges in securely exploiting the electric power system. However, optimal planning and operation of smart distribution networks have made the reach of a more stable and flexible electricity network possible. To this end, this study proposes a new operational model of effectively integrating demand response and battery energy storage systems (BESS) to reduce the power exchange with the sub-distribution substation. To optimize the BESS charging and discharging cycles, the degradation limit is considered for its optimal operation in the electric system. Herein, locally controllable generators are taken into account to support reactive power as well as assist with the flattening of the voltage profile. This paper considers emission cost in the performance of local controllable generators (LCG), which adds a critical layer of optimization to distribution network management. This enables the system to balance grid stability, affordability, and environmental responsibility. Moreover, box and polyhedral methods have been investigated and compared for effective and uncertain-aware optimization of the system. Findings show that the polyhedral method provides more robust and confident results than the box method.

Servitization: The Journey to Advanced Services

Servitization: The Journey to Advanced Services

A Survey of Internet of Things based Indoor Positioning Systems based on Bluetooth Low Energy Beacon

The global positioning systems (GPS) and cellular systems face inherent limitations when it comes to enclosed environments like shopping malls, hospitals, warehouses, as they have significant shadowing effects that cause the signals to be distorted. There have been many studies on different technologies and techniques aiming to find the optimum approach to be used for indoor positioning systems (IPS). Among all the technologies adapted by researchers all around the world, Bluetooth Low Energy (BLE) beacons have been a prevalent choice for many, due to its deploying flexibility, low cost, low power consumption and ability to provide very advanced services to users. It is arduous to obtain RSSI values with great accuracy because the signals can be easily intervened by many factors like humidity, temperature, physical barriers, and interference from decoherent signals. In this paper, we presented a comparative analysis of different, fairly recent IPS that are based on BLE beacons by classifying the different proposed approaches in a structured and systematic manner and traversing the issues and challenges that exist with each technique. The accuracy of the results can be enhanced by using the most suitable positioning algorithm, the orientation of the beacon setup, and optimization methods used. A preliminary result from a case study using BLE beacons with K-Nearest Neighbours (KNN) based fingerprinting approach for indoor positioning estimation shows 86.67% accuracy.

Consensus Guidelines for Assessment and Comprehensive Outcomes Evaluation in Rural Pain Clinics

Recent advancements in nurse anesthesiology fellowship training programs have helped to establish advanced pain management services for rural communities. Consensus guidelines to direct the rural provider toward the most valid and reliable measures for pain assessment and functional outcomes evaluation are not presently available. The primary aim of this initiative was to establish consensus guidelines for a comprehensive outcome evaluation program with specific time intervals for assessments that can be utilized by all rural pain clinics. The American Association of Nurse Anesthesiology Nonsurgical Pain Management Advisory Panel members provided formative and expert feedback for this initiative. The Delphi model was utilized to achieve consensus through multiple rounds of surveys. Items achieving >70% agree/strongly agree were kept; items with >70% disagree/strongly disagree were rejected; items meeting neither advanced to the following round for evaluation until consensus was met. During round I, consensus was reached for: (1) the use of the Numerical Rating Scale for pain severity; and (2) timing of pain severity & functional pain outcomes on each office visit and before/after each intervention. Round II, consensus was achieved for: (1) the use of Wong-Baker FACES Pain Rating Scale as a suitable instrument when literacy or communication pose a barrier but not as a primary assessment; and (2) the use of the Brief Pain Index-Short Form for functional outcome measures. During round III, consensus was reached for: (1) the use of the Oswestry Disability Index as a functional outcome measure; and (2) pain reassessment being performed within 14 days of intervention. This initiative provides rural pain clinics with a comprehensive outcome evaluation program with specific time intervals for assessments.

A Lightweight, Efficient, and Physically Secure Key Agreement Authentication Protocol for Vehicular Networks

In the contemporary era, Vehicular Ad Hoc Networks (VANETs) have emerged as a vital technology in intelligent transportation systems, substantially enhancing the overall travel experience by providing advanced services to vehicles while ensuring driver safety. Despite the notable improvements, the inherent complexity of VANETs presents persistent security challenges, encompassing issues such as privacy preservation for vehicles, message authentication, and constraints in computational power and network bandwidth. Various authentication protocols have been designed for VANETs. However, many of these protocols exhibit significant vulnerabilities, rendering them insecure and unreliable in the face of diverse security threats, such as denial of service, replay, forgery, and impersonation attacks. Moreover, some existing schemes encounter limitations, including high computational complexity and the introduction of additional communication overhead and computational costs. To tackle these concerns, we designed a lightweight and secure identity authentication protocol based on elliptic curve cryptography with the objective of furnishing an effective and secure data transmission mechanism across a public communication channel for the Internet of Vehicles. In addition, we introduce Physically Unclonable Functions (PUFs) to ensure physical layer security during the communication process. A detailed security analysis demonstrates that the proposed protocol is resilient against various attacks. Through a comparative analysis with existing relevant protocols, in scenarios with a high density of vehicles, the algorithm demonstrates significantly lower computational costs and communication overhead than the related protocols, indicating that the proposed protocol is lightweight and efficient. Consequently, the empirical findings indicate that our protocol surpasses others in terms of reliability, user convenience, and practicality for ensuring secure data transmission within VANETs.