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The growing expansion of the Internet of Things and wireless sensor networks has created an urgent demand for compact and reliable radio frequency energy-harvesting circuits. This study introduces the design, simulation and extensive performance of a high-efficiency single band radio frequency detection system optimized for 1.8 GHz operation. The detector is realized on a Rogers RO4003C substrate and employs the SMS7630-079LF Schottky diode, selected for its excellent detection capability and economic viability. The introduction of this filtering stage effectively suppresses undesired harmonic components produced during the rectification process, thereby improving the sensitivity and overall power conversion efficiency of the system. The circuit shows a sensitivity of 1.8 mV for every dBm through its simulation tests. The system shows increased sensitivity to 2.2 mV/dBm because of the band stop filter implementation. The system reaches its peak power conversion efficiency of 65.28% at a 1.5 kΩ load, which makes it suitable for applications that require low-power energy harvesting. These combined attributes establish the developed 1.8 GHz detector as a strong candidate for next-generation energy harvesting modules, self-powered sensor networks and intelligent embedded computing platforms within the expanding domain of the Internet of Things.

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 increasing pace of Internet of Things (IoT) and Industrial Internet of Things (IIoT) applications has exacerbated the security challenges in resource-constrained environments, where traditional cryptographic protocols incur prohibitively high computational and energy costs. These constraints are also worsened by the advent of quantum computing, which poses a long-term security risk to popular crypto-key cryptographic-based efforts. To overcome these difficulties, this paper proposes an Energy-Efficient Cryptographic Protocol Framework (EECPF) that provides mutual optimization between energy consumption, security level, and communication latency to achieve sustainable IoT security. The presented framework proposes an adaptive encryption selection mechanism that dynamically chooses cryptographic algorithms depending on device capabilities, network conditions, and threat levels derived from intrusion detection outputs. EECPF combines privacy-preserving federated learning for distributed intrusion detection with collaborative threat intelligence sharing, eliminating centralized data sharing. In addition, lattice-based post-quantum cryptography primitives are added and combined with lightweight blockchain-enforced identity management to ensure long-term authentication resilience. The models on which the framework is based are mathematically based, modeling the consumption of energy, the robustness of security, and latency, providing principled multi-objective optimization under resource constraints. The publicly available Edge-IIoTset dataset was subjected to extensive experimental assessment under realistic IIoT and IoT attack scenarios. Experiments show that EECPF can reach an intrusion detection rate of 94.7%, while reducing energy consumption by 47.3% and latency by 23.8% compared with other commonly used lightweight cryptographic methods. These were continually noticed across different heterogeneous devices and deployment environments. In general, EECPF offers an energy-aware, quantum-resilient, and scalable security solution that can be used for next-generation IoT systems, such as smart healthcare, industrial automation, and smart city infrastructures.

What is this summary about?Schizophrenia is a serious mental health condition that affects how people think, feel, and behave. It can cause hallucinations, delusions, and altered behaviors that change how a person experiences and understands reality. People with schizophrenia often feel less engaged with life. This means different things to different people and could include feeling less productive or energetic, not wanting to be around others, and feeling less like themselves. Improving patient life engagement can be an important goal for people with schizophrenia when they receive treatment.This summary covers two studies about measuring and improving life engagement for people with schizophrenia. In the first study, published in 2024, researchers interviewed adults with schizophrenia and experts to understand what questions are relevant to measure life engagement and then developed a new tool as a result. The other study, published in 2025, used this new tool to measure if life engagement and daily functioning improved over time with a medication called brexpiprazole, which is approved for treating schizophrenia.What were the results?In the first study, researchers selected 14 relevant questions (or ‘items’) to include in the new tool. The tool was named the ‘14-item life-engagement scale’. Importantly, adults living with schizophrenia said engagement with life is meaningful to them.In the second study, the 14-item life-engagement scale helped researchers to measure that life engagement showed improvements with brexpiprazole treatment, and that daily functioning tended to improve at the same time.What do the results mean?For people living with schizophrenia, improving life engagement is meaningful, and treatments such as brexpiprazole may help them to feel more engaged with life and better able to function in daily life.[Box: see text][Box: see text][Box: see text]Link to original article here and here.

To address the demanding requirements for high gain, wide bandwidth, and stable circularly polarized (CP) radiation in wireless local area network (WLAN) applications, this paper proposes and implements a broadband circularly polarized array antenna primarily targeting the 2.4-2.484 GHz ISM band. The design employs a coplanar waveguide fed broadband CP monopole antenna as the radiating element. A sequential rotation technique is utilized to form a four-element array, and a windmill-shaped defected ground structure is introduced to further extend the bandwidth. The antenna is fabricated on a low-cost FR4 substrate with overall dimensions of 0.98λ0 × 0.98λ0 × 0.008λ0 at 2.4 GHz. Simulation and measurement results show that the array antenna achieves a -10 dB impedance bandwidth of 1.22-2.78 GHz (87.1% relative bandwidth) and a 3-dB axial ratio bandwidth of 1.85-2.66 GHz (35.0% relative bandwidth), ensuring sufficient margin over the target WLAN band. At the center frequency of 2.45 GHz, the antenna exhibits left-hand circular polarization radiation, with a measured peak gain of 8.2 dBic and a cross-polarization discrimination better than 20 dB. To verify its performance advantages in practical systems, the designed antenna was integrated into a ZigBee wireless communication system for data transmission testing. Under controlled conditions, the system employing the proposed antenna achieves a packet loss rate of 3.0% ± 0.4% in a complex multipath environment, significantly outperforming a traditional linear-polarized whip antenna (19.0% ± 1.1%). The results demonstrate that the proposed antenna, featuring wide bandwidth, high gain, and strong anti-interference capability, is a robust solution for WLAN access points and internet of things gateways.

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.

With the rapid development of the Internet of Things (IoT) and mobile computing, edge computing has emerged as a promising paradigm for providing low-latency and energy-efficient services. However, in some extremely computation-intensive scenarios, conventional terrestrial edge computing may fail due to the insufficient computing capability of ground base stations. Fortunately, multi-UAV-assisted edge computing offers a promising solution to this challenge. Nevertheless, existing methods often struggle to provide efficient horizontal cooperative deployment for multiple UAVs with low computational overhead. To address this issue, this paper considers user randomness and inter-UAV collaboration, and proposes a low-complexity yet highly adaptive approach for cooperative deployment and task-scheduling optimization in multi-UAV-assisted edge computing systems. Specifically, we formulate the problem as a stochastic optimization problem that minimizes the energy consumption of ground users while ensuring UAV battery endurance and overall system performance. We then propose a dynamic cooperative deployment and task scheduling (DCDTS) algorithm that integrates K-means clustering with the Lyapunov optimization framework. Through Lyapunov optimization, the original dynamic optimization problem is transformed into a deterministic problem and further decomposed into multiple subproblems that can be solved in parallel. K-means is exploited to enable cooperative UAV deployment and user offloading decisions, while non-convex optimization and nonlinear programming are employed to solve the task-scheduling and resource-allocation subproblem. Extensive parameter analysis and comparative experiments demonstrate that the proposed dynamic cooperative deployment algorithm can effectively reduce user energy consumption while maintaining UAV energy constraints and system performance.

Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts-Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to 1.8× higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet.

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.

Poor indoor air quality, inadequate ventilation, and unnoticed local disturbances can reduce occupant well-being and compromise practical safety in smart-home and small-building environments. Although low-cost Internet-of-Things (IoT) sensing technologies are widely available, many monitoring systems remain focused on single-modality sensing and do not jointly evaluate environmental conditions, vibration activity, communication reliability, and gateway-side interpretation within one framework. This study presents the design, implementation, and proof-of-concept evaluation of a low-cost, privacy-conscious, non-imaging IoT-based indoor environment and safety-awareness monitoring framework built with ESP32/Arduino sensor nodes and a Raspberry Pi gateway. The system integrates carbon dioxide, temperature, humidity, gas-resistance/VOC-trend indication, and vibration sensing with MQTT-based communication and edge-side analytics. Controlled subsystem experiments showed that CO2 concentration differentiated ventilation conditions, increasing from 395.47 ppm in the valid empty/open-door baseline to 1083.16 ppm in the closed occupied condition. Vibration states were distinguished using root-mean-square acceleration features across calm, surface-disturbance, footstep, play, and jump conditions. MQTT evaluation using 1000-message batches showed no observed message loss or duplicates across the tested QoS/network combinations, although latency and throughput varied by network configuration and QoS level. QoS 1 provided a practical balance between low latency and protocol-level delivery assurance in the tested local/Wi-Fi setting. A final integrated validation run further demonstrated synchronized acquisition from indoor environmental, vibration, and outdoor CO2 reference publishers through the same Raspberry Pi gateway, with zero missing or duplicate sequence flags across the three streams. Overall, the findings indicate that lightweight open-source IoT hardware can support a reproducible building-level sensing and edge-analytics prototype for indoor environment and safety-awareness monitoring. Broader deployment in standard-sized rooms, multi-room buildings, and smart-city infrastructure remains future work.

This article makes three claims. First, that in his foundational writings, Hugo Grotius conceptualised the seas as institutional 'wildernesses' where public and private persons stand on equal footing. His purpose behind doing so was to advance the interests of European colonial companies. Second, while modern international law rejects Grotius's fundamental juridical assumptions concerning the seas, it nevertheless retains the possibility of stretches of it reverting to wildernesses where juridical accountability is systematically foreclosed. Third, this foreclosure is exacerbated by, among other things, the European Union's tendency to flit at will between an international legal person and an amorphous 'union of values.' This enables European Union officials to weaponise professed values of human rights and international law to advance distinctly neo-Grotian, neo-imperial agendas of resource extraction not just on the high seas, but in other 'wildernesses' like Non-Self-Governing Territories, whilst simultaneously avoiding juridical accountability.

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.

With the growing need for long-term secure communications in Internet-of-Things (IoT) and sensor-network environments, practical and robust post-quantum key-establishment mechanisms have become increasingly important. In this work, we revisit the ephemeral-only Ding key exchange (DKE) proposed at ACNS 2019, which is based on one-sample Ring Learning With Errors (Ring-LWE) with rounding, and the original analysis of which covers only passive security. Building on the DKE framework, we propose MoRo-KEM, a Module Learning With Errors (Module-LWE)-based key-encapsulation mechanism using rounding. First, we lift the construction from the Ring-LWE setting to the Module-LWE setting, retaining ring-level efficiency while enabling more flexible parameter choices and reducing reliance on rigid algebraic structure. Second, we replace discrete Gaussian sampling for secrets and errors with centered binomial sampling, thereby simplifying constant-time vectorized implementations while preserving the required noise behavior. Third, we extend the resulting key-exchange core to an IND-CPA-secure public-key encryption scheme and further obtain an IND-CCA-secure KEM via the Fujisaki-Okamoto transform. Finally, at security level I, MoRo-KEM achieves a decryption failure rate of 2-166, lower than the 2-139 reported for CRYSTALS-Kyber, thus improving robustness against decryption-failure attacks. These properties make the proposed design attractive for secure key establishment among sensor nodes, edge devices, and gateways operating under constrained computation, memory, and communication budgets. Overall, our construction provides a concrete path from ephemeral key exchange to a practical IND-CCA-secure KEM instantiated over Module-LWE.