In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control.
The dense deployment of Internet of Things (IoT) networks in smart cities poses severe challenges in spectral efficiency, energy consumption, and interference management. This paper addresses the joint optimization of three-dimensional (3D) beamforming, subcarrier assignment, and power allocation in a multi-carrier non-orthogonal multiple access (MC-NOMA) network supporting both device-to-infrastructure (D2I) and device-to-device (D2D) communications. A robust percentile-based channel model with spatial shadowing correlation is adopted to cope with urban propagation uncertainties, and an accurate elliptical footprint model derived from the 3-dB antenna pattern is used to evaluate coverage gaps and beam overlaps. The resulting mixed-integer nonlinear programming problem is solved by a three-layer memetic particle swarm optimization (Hybrid PSO) algorithm that combines a fixed-point Successive Interference Cancellation (SIC-aware) power solver, an iterative Hungarian method for subcarrier assignment, and an adaptive multi-phase local search. Simulation results demonstrate fast convergence, with the network power consumption stabilizing at 88 mW at a 600 MHz carrier frequency. The proposed MC-NOMA with 3D beamforming consistently outperforms baseline schemes that employ OFDMA with shared spectrum or uniform linear arrays, especially under high channel estimation errors, strong external interference, stringent coverage constraints, and increasing user densities. The findings confirm that the joint framework significantly enhances energy efficiency and robustness, making it a scalable solution for next-generation urban IoT networks.
This paper introduces an advanced Internet of Things (IoT)-driven smart furniture system designed to dynamically adapt to individual users by integrating deep reinforcement learning with federated meta-learning. Personalization is formulated as a Markov decision process, enabling the system to make optimized, sequential adjustments tailored to each user's behavior. To estimate hidden ergonomic preferences in real time, an adaptive Kalman filter is applied, while a sparse autoencoder reduces raw sensor signals by 82 %, preserving key temporal features essential for accurate modeling. In a comprehensive user study involving 48 participants and more than 160,000 time-series sensor samples, the framework significantly reduced cumulative user dissatisfaction by 43 % and cut energy consumption by 21 %, compared with conventional rule-based control systems. Real-time adaptations occur with an average latency of 280 ms, and constraints for ergonomics are upheld in 95 % of use cases, confirming the system operates swiftly and safely. Federated learning (FL) enables privacy-preserving collaboration across distributed furniture units. Training converges to 87 % of global performance within 30 global iterations, without any raw data exchange, reinforcing both scalability and data privacy. These empirical results strongly support the framework's suitability for deployment in health-aware workspaces, smart homes, and eldercare environments, delivering a robust, responsive, and interpretable solution for enriching human-furniture interaction.
[This retracts the article DOI: 10.1155/2023/4776770.].
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In her interesting response 'Suicide is always a public health issue', Susan Pennings puts forward a critique of a current controversy article written by us, 'When is suicide a public health issue?'. Most notably, Pennings argues that national suicide prevention strategies have an important expressive function and that the framework proposed in our original piece would undermine this function. In this article, we will be responding to some of the objections raised by Pennings. We will be arguing that the approach proposed in the original article would not be undermining the expressive function of suicide prevention, but rather championing the principle of autonomy as opposed to a modified sanctity of life ideal. We will aim to demonstrate that this constitutes a more inclusive and nuanced approach, better able to accommodate the increasing cultural and religious pluralism Pennings herself alludes to in her critique.
To explore the impact of sleep disturbances on children with cerebral palsy (CP) and their families. Semi-structured online interviews were conducted with families of children with CP using a qualitative descriptive design. Children with CP aged 3 to 18 years, who experienced clinical sleep disturbance, their siblings, and parents were recruited. Interviews were video-recorded, transcribed verbatim, and analysed using inductive, semantic thematic analysis. Rigour was established using member checking, analytical discussion, and rich, thick description. Thirty participants from 10 families were interviewed. Six themes were identified: (1) It's hard to know; the challenges recognizing when sleep disturbances require clinical attention; (2) So many things; health, caregiving, and the family environment influence sleep disturbances; (3) I'm tired and grumpy with friends; sleep disturbances affect mood and relationships; (4) Stuff is harder to do; sleep disturbances affect daily life; (5) Tiredness just makes anything that was already physically hard much worse; sleep disturbances affect physical health; (6) We don't know what is out there; parents seek support to manage sleep disturbances. Tailored, multidisciplinary, and holistic sleep assessment and interventions are warranted; these should include mental health and social care support for the family.
The Constrained Application Protocol (CoAP) is widely adopted to ensure end-to-end reliability in resource-constrained Artificial Intelligence of Things (AIoT) and Wireless Sensor Networks (WSNs). However, CoAP's default retransmission timeout (RTO) mechanism lacks algorithmic responsiveness under volatile channel conditions, and state-of-the-art benchmarks like CoCoA+ and FASOR often suffer from over-conservative backoff states or destabilizing retransmission storms. To overcome these operational bottlenecks, this paper proposes a novel dual-adaptive Dynamic RTO algorithm specifically engineered for heterogeneous IoT deployment scales. The proposed framework dynamically adjusts its parameter inspection cycle (N) based on instantaneous round-trip time (RTT) variance while simultaneously scaling its tuning coefficient (α) in response to real-time packet loss indicators. To rigorously validate the algorithmic resilience, performance evaluations were conducted within a highly volatile network environment governed by the Gilbert-Elliott dynamic loss model across multi-hop linear (1 × 6) and grid (3 × 6, 5 × 6) topologies. Experimental results demonstrate that the proposed Dynamic RTO consistently optimizes the throughput-latency trade-off, achieving a total communication time of 25.92 s in complex grids-outperforming CoCoA+ and FASOR by 14.28% and 8.89%, respectively. Furthermore, the proposed mechanism significantly curtails transmission overhead, restricting the cumulative retransmission footprint to just 59 counts under severe localized impairments, thereby establishing a scalable, resource-efficient, and empirically robust transport-layer solution for next-generation edge-computing infrastructures.
The rapid expansion of Internet of Things (IoT) systems has introduced significant security challenges, particularly in resource-constrained environments where traditional security mechanisms are often impractical. This paper presents a secure and lightweight hybrid framework that integrates cryptographic techniques with machine learning-based anomaly detection for IoT-based cyber defense. The proposed framework employs Elliptic Curve Cryptography (ECC) for key exchange, SPECK for lightweight encryption, and SHA-3 for data integrity, combined with a Random Forest classifier for anomaly detection. The framework is implemented and evaluated on a Raspberry Pi-based edge environment using the CIC-BCCC-NRC-IoT-2023 dataset. Experimental results demonstrate an accuracy of 89.5% and an F1-score of 90%, with an average end-to-end latency of 1.08 ms and energy consumption of approximately 4.5 mJ per inference. These results indicate that the proposed approach achieves a practical balance between security, computational efficiency, and detection performance under constrained conditions. While the framework shows promising results, its evaluation is limited to a controlled setup and a single primary dataset. Future work will focus on cross-dataset validation, adversarial robustness, and large-scale deployment analysis.
The rapid development of Internet of Things technologies has accelerated the deployment of smart home systems. However, perception solutions based on visual sensors remain constrained by illumination sensitivity, occlusion, and privacy concerns. Frequency-modulated continuous-wave (FMCW) millimeter-wave radar provides a promising alternative because it operates independently of lighting conditions, is robust to environmental changes, and preserves user privacy. To address multiple-extended-target tracking in cluttered indoor environments, this paper proposes a high-accuracy tracking algorithm that combines an improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, an optimized Nearest-Neighbor Data Association (NNDA) scheme, and an Extended Kalman Filter (EKF). The improved DBSCAN algorithm introduces spatial-extent constraints, velocity-consistency checks, and candidate-cluster validation to cluster raw radar point clouds and convert extended targets into representative point targets with little additional computational cost. The optimized NNDA scheme then integrates clustering information into the association process, improving the matching accuracy between existing tracks and current measurements. Finally, the EKF estimates the state of each target from the associated measurements. Real-world experiments show that the proposed algorithm achieves tracking errors below 0.4 m in typical motion scenarios, maintains continuous tracking in two-person crossing scenarios, and reaches 93.3% counting accuracy in five-person scenarios. These results outperform the tracking system based on the commercial Texas Instruments (TI) IWR6843ISK millimeter-wave radar evaluation board. The proposed method offers a reliable and privacy-preserving sensing solution for smart homes, elderly care, and intelligent building applications.
The implementation of digital technologies within health services promises increased performance, quality, and efficiency. However, evidence is limited regarding the application and outcomes of digital technologies implemented at the level of health services (meso-level) or health systems (macro-level), rather than those evaluated solely as patient-level clinical interventions (micro-level). We systematically review state-of-the-art digital technologies and their applications in healthcare systems and conduct a framework synthesis to review outcomes related to maturity and implementation. Eleven databases were searched (Embase, HMIC, Medline, PsycInfo, SPP, AgeLine, AMED, CDAS, CINAHL, SCOPUS, WoS) on 4th November 2024 for systematic and non-systematic reviews published within the previous five years that provided an overview of digital technologies applied at a systems/service-level (macro/meso-level) in healthcare or evidence for their outcomes. Studies into individual interventions (micro-level) were excluded. Risk of bias/quality assessment tools used in the reviews were recorded. We review types and applications of technology, then use a framework synthesis methodology to assess outcomes relating to digital transformation awareness/maturity, implementation, UK context, and challenges. Our searches identified 1423 records, with 1011 remaining after deduplication. Of these, 131 were assessed for eligibility, with 28 reviews reporting on 1606 individual studies (with an additional 2437 included in a bibliometric analysis) included in the final analysis. We identified five main groups of technology in healthcare: Integrated Technology/Industry 4.0, Artificial Intelligence (AI), Big Data, Internet of Things (IoT), and Blockchain. Our framework synthesis revealed recent conceptual recognition of digital transformation, and variability across regions with regards to the sophistication and maturity of uptake of technologies. We report on four reviews which provided quantitative evidence on the impact of digital technologies in health services. Barriers included major concerns related to data privacy and trust, equity, ethics, and implementation logistics. UK frameworks exist for implementation of technologies, but do not focus exclusively on the systems-level. Digital technologies are increasingly integrated into health service delivery, yet the maturity and evaluation of these tools vary across regions. More robust implementation research is needed for advanced systems. Our synthesis provides a framework for healthcare leaders to assess digital readiness and prioritise technologies aligned with service transformation goals. Limitations of evidence include the heterogeneity of technologies, settings, and outcomes across included reviews. The review was not part of a trial and was not registered.
To evaluate the feasibility and clinical impact of an IoT-based teleassistance system for remote monitoring of preterm neonates receiving home oxygen therapy. A historically controlled cohort study was conducted in Manizales, Colombia, with three phases: (1) baseline data collection from neonates receiving traditional follow-up (n=32), (2) development of an Internet of Things (IoT)-based teleassistance system integrating physiological monitoring, cloud storage, and mobile applications, and (3) pilot testing in a prospective intervention cohort (n=25). The primary outcome was the duration of home oxygen therapy. Median duration of home oxygen therapy was reduced from 34 days (IQR 25-46) in the control group to 10 days (IQR 7-17) in the intervention group (p<0.001). Reliable communication was achieved through 4G and Wi-Fi, while LoRa performance was limited by geographic constraints. IoT-based teleassistance was associated with a reduction in the duration of home oxygen therapy in preterm neonates and demonstrated feasibility for remote monitoring in resource-limited settings.
This paper presents a compact wearable patch antenna operating in the 2.4 GHz ISM band for biomedical Internet of Things (IoT)-based healthcare monitoring applications. The proposed antenna is intended for integration with wearable biomedical sensors in order to support real-time physiological data transmission in remote patient monitoring systems. The antenna was designed on an FR4 substrate to achieve good impedance matching and stable radiation performance. The antenna showed good performance, with a reflection coefficient of -39.56 dB and a gain of 3.01 dB. SAR analysis confirmed compliance with IEEE and ICNIRP safety standards for wearable applications. In addition, the antenna prototype was fabricated and experimentally validated using a vector network analyzer (VNA), showing good agreement between simulated and measured results. Furthermore, the proposed system was implemented by integrating an ESP32 microcontroller with a MAX30100 physiological sensor, where the sensor is responsible for acquiring real-time biomedical data, including heart rate and blood oxygen saturation (SpO2). The ESP32 processes the acquired data and enables wireless transmission through the proposed antenna to a smartphone and laptop using the Blynk IoT platform, which allows real-time remote monitoring and visualization of physiological parameters. The obtained results confirm the suitability of the proposed antenna for wearable biomedical devices, remote healthcare monitoring, and IoT-enabled healthcare applications.
This systematic review aims to evaluate current digital twin (DT) applications in healthcare, explore their technological foundations, and propose a roadmap for scalable, patient-centered implementation. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, a systematic search was conducted across Medline, Scopus, Web of Science, and EBSCO up to May 2025. Eligible studies included peer-reviewed research on DT applications in clinical or healthcare settings involving human or patient-related data. Methodological quality was assessed using appropriate Joanna Briggs Institute critical appraisal tools based on study design. The systematic review protocol was prospectively registered in Prospective Register of Systematic Reviews (registration number: CRD420251120304). 26 studies were included, with most published between 2023 and 2025. DT applications spanned diagnostics, therapy optimization, physiological monitoring, and system-level modeling. Simulation-based designs dominated, often integrating artificial intelligence, internet of things, and machine learning. While several studies reported strong technical performance (e.g. up to 96.3% accuracy), real-world clinical integration was rare. Notable outcomes included better glycemic control, pain management, and disease progression prediction. Barriers included insufficient infrastructure detail, limited validation, and equity concerns. The roadmap highlights three enablers: privacy-preserving, validation pipelines, and interoperability. DTs offer transformative potential for predictive, personalized, and participatory healthcare. Realizing clinical impact requires bridging the translational gap and scaling personalization. This review outlines key strategies for interdisciplinary innovation and deployment of DTs in healthcare.
Cancer care is becoming increasingly complex globally, with rising cases and challenges for conventional systems. Technology aids in managing this complexity, but it does not replace clinical expertise. The integration of digital tools, artificial intelligence (AI) in imaging and diagnostics, and machine learning in treatment planning marks a shift in oncology. However, successful implementation requires careful evaluation, particularly in nursing practice, where AI assists in symptom tracking and care coordination. Challenges include digital literacy among older patients and ethical issues such as data protection and algorithmic transparency. Rapid adoption without adequate understanding may contribute to long-term negative outcomes. The narrative review explores the present and future applications of modern technology, such as artificial intelligence, robotics, machine learning, telemedicine (including remote patient monitoring), the internet of medical things (such as wearable devices), genomic technologies, and nanotechnology in medicine, among others, in the management of cancer.
Pregnancy-related complications are increasing globally, necessitating timely and accurate risk prediction for effective clinical intervention. This paper presents NurtureNest, an Internet of Things (IoT)-enabled machine learning framework for automated pregnancy risk assessment. The system integrates wearable sensor data from smartwatches with user-provided clinical parameters via a mobile application, enabling continuous remote monitoring. Historical clinical data are used to train multiple machine learning models for multi-class classification of pregnancy risk into low-, medium-, and high-risk categories. Ensemble learning techniques are independently trained and evaluated alongside conventional machine learning classifiers to assess their effectiveness in pregnancy risk prediction. Experimental results show that LightGBM achieves the highest performance with 92.86% test accuracy and 95.04% cross-validation
accuracy. Model performance is validated using ROC analysis and feature importance evaluation. The proposed framework enables early risk detection and supports timely clinical decision-making, improving maternal healthcare outcomes.
Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications.
The two NFAT transcription factors NFATc1 and NFATc2 are the most prominent Ca++-dependent TFs in the nuclei of activated peripheral lymphocytes. They control the activity of thousands of genes during immune responses. Although their structure and function show numerous things in common, their expression and activity differ markedly in most types of lymphocytes. Over the last 40 years, the work of our laboratory revealed a strong inducible transcription of the Nfatc1 gene upon lymphocyte (co-)activation, compared to the 'tonic' transcription of Nfatc2. This leads to the inducible expression of a short NFATc1 isoform that we designated as NFATc1/αA, which differs from longer NFATc1 proteins and NFATc2 by an individual N-terminal 'α' peptide and the absence of a C-terminal peptide of approximately 250 amino acid residues. While comprehensive experimental studies led to the conclusion that NFATc2 supports (i) apoptosis, (ii) the induction of anergy, and (iii) the 'exhaustion' of peripheral T cells, opposite conclusions can be derived from our studies of NFATc1/αA. This view on the 'two faces' of NFAT transcription factors will be presented in this review and discussed in the role of NFATs in cancerogenesis.
In this essay I review and summarize four older "thought" papers about how science is communicated and practiced. These papers were written by the Nobel Prize winner Peter Medawar, the eminent physiologist Julious Comroe, Eugene Robin a renowned clinical investigator, and David Horrobin an innovative scientist and early player in biotechnology. These papers all question how we frame what we do and tell stories about what we find. Several caution against excessive objectivity, hype, groupthink, and what we now call fear of missing out. They argue for imagination, creativity and critical thinking. While there are many threats to biomedical research beyond the control of individual scientists, I believe these papers offer insight about things we can do from the inside out to improve the practice and culture of science. I am also hopeful that insights from these papers, if broadly acted on, could help improve public confidence in and support for biomedical research.