Cellular redox disruption and apoptosis: Differential effects of RFR frequencies on Leydig cells.

A compact four-port multiple-input multiple-output (MIMO) staircase rectangular patch antenna with inverse U-shaped resonators and a modified defected ground structure (MDGS) is presented for dual-band wireless communication applications. The antenna integrates four identical radiating elements placed orthogonally to achieve low mutual coupling and enhanced diversity. Fabricated on an FR-4 substrate with dimensions of 38 × 38 × 1.6 mm3, the design operates over two bandwidths (|S₁₁|< − 10 dB) of 6.8–8.9 GHz and 10.3–12.0 GHz, resonating at 7.9 GHz and 11 GHz, respectively. The structure achieves peak gains of 7.36 dBi and 5.82 dBi. MIMO performance is validated with an ECC below 0.01, DG greater than 9.95 dB, CCL below 0.02 bits/s/Hz, isolation exceeding 25 dB, mean effective gain (MEG) between − 9 dB and − 6 dB, and low TARC values. Simulated and measured results exhibit strong agreement, confirming the antenna’s potential for compact high-performance wireless devices within micro- and nanoscale integrated systems. The proposed design addresses key challenges in MIMO systems, such as minimizing mutual coupling, achieving wide dual-band operation, and maintaining compact size for integration in modern wireless devices.

Abstract For centuries, infectious diseases have represented a leading cause of global morbidity and mortality, with high transmissibility posing persistent public health challenges. Because early physiological changes often precede overt clinical symptoms, continuous physiological monitoring may provide opportunities for earlier recognition of illness and timely intervention. In this study, we developed and evaluated a wireless wearable device capable of measuring microvascular tissue oxygen saturation, respiratory rate, respiratory rhythm, breathing depth, heart rate, and pulse strength. Device performance was assessed in a clinical study involving healthy participants performing breath-holding, paced breathing, and mild hypercapnia tasks. Breathing rate and heart rate derived from the wearable device were compared with corresponding measurements obtained from commercial physiological monitoring systems. Chest tissue oxygen saturation measured by the wearable device was additionally evaluated alongside peripheral blood oxygen saturation measured using a commercial pulse oximeter. Across all tasks, wearable-derived breathing rate and heart rate demonstrated strong agreement with commercial systems, with small biases (≤0.19 breaths/min for breathing rate and ≤0.47 beats/min for heart rate). Chest tissue oxygen saturation exhibited distinct and rapid responses during breath-holding and mild hypercapnia, whereas peripheral oxygen saturation showed delayed or inconsistent changes. During breath-holding, changes in tissue oxygenation preceded peripheral desaturation by approximately 10 seconds. Collectively, these findings demonstrate the feasibility of the wearable device for real-time, continuous monitoring of cardiorespiratory parameters and microvascular tissue oxygenation, and suggest that tissue-level oxygenation measurements may provide complementary physiological information relevant to early respiratory stress in both general health and infectious disease research contexts.

A wireless relay network is made up of multiple low-power nodes that pass messages between two endpoints by forwarding them through intermediate helper nodes. This relay process greatly increases the communication range of small wireless devices but requires a custom communication protocol rather than traditional centralized routing methods. In this work, we present the FMTG (Find Me That Guy) protocol, a multi-hop system that uses simple broadcast discovery and chained acknowledgements to build communication paths. It removes the need for periodic control messages entirely, cutting idle overhead by 100% compared to protocols like OLSR and BATMAN and uses a compact 28-byte packet with an 11-byte header to keep communication efficient. Practical experiments with microcontroller-based nodes show that this approach extends communication distance from about 15 m to 38 m across four hops. These results demonstrate that it is a low-overhead and power-efficient option for building distributed communication networks.

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller.

As Japan becomes an aging society, the number of patients with heart failure (HF) is increasing. The European Society of Cardiology guidelines recommend noninvasive ICT monitoring from the perspective of self-care and team medical care; however, evidence regarding the effectiveness of remote monitoring in Japan is limited. This study assessed the usefulness of OMRON Connect with the Health Data Monitoring System, which provides simultaneous sharing of biomonitoring data of patients with HF using wireless devices. A prospective, single-arm, multicenter observational study for 84 days was performed, including 30 patients with HF (age 72 ± 5.6 years, male, n = 19). They were introduced to the measurement of body weight (BW), blood pressure (BP), electrocardiogram (ECG) recording, and patient-reported symptoms on a smartphone application (PRS on App) using OMRON Connect. The primary outcome was adherence to this system, and the secondary outcome included factors that influence adherence. The adherence measurements were as follows: BW, 97.0% (interquartile range [IQR] 92.3-100%); BP, 88.7% (IQR, 79.8-95.8%); and ECG, 88.7% (IQR, 64.9-94.1%). No patients dropped out during the 84-day period. No significant relationship was found between adherence and the following parameters: age, sex, prior HF admission, left ventricular ejection fraction, New York Heart Association class, serum brain natriuretic peptide level, renal function, cognitive impairment, and living alone or with family. However, the continuation rate of PRS on App gradually decreased to 53%. One of the patients was admitted for HF exacerbation, and this system could clearly detect BW increase before admission. In addition, fatal arrhythmias, such as a short run of premature ventricular contractions or advanced atrioventricular block, could be detected in ECG. The use of OMRON Connect for noninvasive ICT monitoring in patients with HF demonstrates good adherence in checking BP, BW change, and ECG changes. This method proves to be feasible for patient self-management and facilitates appropriate clinical intervention.

The detection of trace nitrogen dioxide (NO2) in port atmospheres is crucial for protecting occupational health and maintaining air quality in coastal cities. Nonetheless, the reliable monitoring of NO2 remains a challenge in complex environments featuring high humidity, diverse gas compositions, and dynamically fluctuating concentrations, which impose substantial interference. Here, the NO2 sensors based on the CeO2/ZnO/WO3 (CZWT) heterostructure films were fabricated via the template-assisted magnetron sputtering. The optimal device exhibited outstanding NO2 sensing performance at 280 °C with high response (81.69 to 50 ppm NO2), rapid response/recovery rate (25/10 s), ultralow detection limit (10 ppb), ideal selectivity, and excellent long-term stability (90 days). Further investigations suggested that the exceptional hydrophobicity of the CZWT heterostructure film and the dynamic Ce3+/Ce4+ redox cycle endowed the sensors with humidity-tolerant response properties. In parallel, a wireless gas-detection device was developed to achieve the monitoring of NO2 in humid environments. This work demonstrates an effective approach for designing humidity-independent MOS gas sensors.

Backscatter communication (BackCom) has emerged as an energy-efficient and low-cost communication paradigm, in which wireless devices transmit information by reflecting incident signals rather than actively generating radio frequency signals. Owing to the extremely low power consumption and hardware cost, BackCom is particularly suitable for Internet of Things (IoT) devices with stringent low energy and cost constraints. However, due to the severe double channel attenuation inherent in backscatter links, conventional ground-based deployment of transmitters and receivers often suffers from poor communication quality and low energy efficiency. Unmanned aerial vehicles (UAVs), with their high mobility and favorable line-of-sight (LoS) links, can act as dynamic aerial transmitters and receivers in BackCom, thereby mitigating channel attenuation and improving both communication reliability and energy efficiency. To enhance the data collection efficiency of UAV-assisted BackCom systems under a limited mission duration, this paper proposes a joint optimization method for communication resource allocation and UAV trajectory design under task time constraints. Specifically, a mixed-integer non-convex optimization problem is formulated to maximize the number of devices served by the UAV within a given task duration. The original problem is then decomposed into two subproblems, namely communication resource allocation optimization and UAV trajectory optimization. An iterative algorithm based on Block Coordinate Descent (BCD) and Successive convex approximation (SCA) is developed to obtain an efficient solution. Simulation results demonstrate that the proposed method can effectively increase the number of served devices within the specified mission time limit.

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance.

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models.

The increasing demand for compact, wideband and efficient antennas in 5G sub-6GHz networks has driven research toward designs offering stable gain and high radiation performance within limited dimensions. This paper presents a gain stabilized wideband patch antenna developed through a systematic six-stage design approach incorporating circular parasitic stubs, L-slots, a meandered feed line and a defected ground structure. These elements interactively work for the provision of multi-frequency impedance matching and the introduction of resonance modes takes place with further extension of the effective electrical length of the circular parasitic stubs, whereas the L-slots are used for the perturbation of the surface currents. The meandered feed line further elongates the current path, lowering the fundamental resonance frequency by 49% without physical size increase. The defected ground structure disrupts ground plane currents, creating capacitive loading that fine-tunes impedance matching and suppresses higher order modes, collectively enabling wideband operation with minimal gain variation. The optimized design exhibits dual resonances at 3.6 and 6.1 GHz with return losses of - 44 and - 27dB respectively, covering a wide operational band of 3.2-6.6GHz. The antenna maintains a consistent realized gain of 2.6-2.7dBi (± 0.8dB), radiation efficiency above 90%, group delay variation below 0.5 ns and a low quality factor ([Formula: see text]). The novelty lies in the systematic integration of these complementary techniques to simultaneously achieve wideband operation, exceptional gain stability and low group delay, making it suitable for compact 5G wireless devices.

The first generation of invasive brain-computer interfaces was conceived as a centralized and monolithic technology composed of an array of microelectrodes implanted on the brain surface and wired to an external unit for telemetry and power supply. Although this technology exceeded the expectations and possibilities of the noninvasive one, risks associated with implantation of wired devices and procedures appeared. Advances in electronics miniaturization and wireless power transfer methods, among others, have favored the proposal of wireless submillimeter implantable devices with stimulation and telemetry capabilities. This constitutes the foundation stone of a next generation of submilliliter implanted neurosensors (NG-SINs) based on massive distributed networks of untethered neuroimplants deployed on the brain. This particular ecosystem, because of the inherent severe restrictions in size, power consumption, electromagnetic safety, and computational capacity, presents significant challenges in the field of communications and internetworking, such as media access control, addressing, data error rate, and others. In this study, we propose a combination of orthogonal multiple access (OMA) and nonorthogonal media access (NOMA) as the base for the telemetry of the NG-SINs, with a focus on the detection of neural spikes in real time. Specifically, we propose the use of asynchronous code division multiple access (aCDMA) and power domain non orthogonal media access (PD-NOMA) with successive interference cancelation (SIC) over backscattered communications. While aCDMA mimics the spontaneous nature of brain activity, and provides addressing and grant-free random-access mechanism, its inherent near-far issue can be conveniently used to improve the performance by means of PD-NOMA and SIC. Based on our simulations, we conclude that this combination of NOMA and OMA techniques is a viable and promising approach for the NG-SINs applied to neural spikes detection that enables scalability with improved performance.

Ultrasound-guided blue dye localization of a clipped axillary lymph node after neoadjuvant chemotherapy in breast cancer a prospective feasibility study and critical review of axillary localization techniques.

• Nanocellulose is applied to design fully bio-based and sustainable frequency-selective surface devices for high-frequency wireless communication. • Nanocellulose aerogels combined with metal–organic framework materials achieve extremely low dielectric properties, providing an ultralight substrate suitable for millimeter-wave and terahertz operation. • Biochar incorporated into nanocellulose films enables conductive, laser-patterned filters that allow controlled transmission and resonance-based attenuation of electromagnetic waves. • Multilayer structures based entirely on nanostructured cellulose demonstrate tunable transmission, broadened attenuation bands, and improved phase modulation across the 60 GHz to 0.51 terahertz range. With rapid progress in wireless communication, the need for advanced frequency-selective surfaces (FSSs) that operate efficiently at high frequencies is growing. This study presents sustainable, high-performance FSSs by laminating cellulose nanofiber (CNF) film filters, laser-patterned with millimeter-scale apertures, onto ultralight cross-linked CNF foams (aerogels). These biogenic FSSs cover a broad frequency range from 60 GHz to 0.51 THz. Added biochar enhances film conductivity, while metal–organic frameworks (MOFs) improve foam dielectric properties and surface area. The resulting foams exhibit near-air permittivity (ε r ≈ 1.01) and low loss tangent, confirming their suitability as high-frequency substrates. The patterned CNF films enable tuning of transmission and reflection properties, achieving high signal transmission (>90%) between 60 and 90 GHz and targeted resonance-based attenuation. Multilayered CNF-based stacks of perforated films and foams show enhanced transmission, wider attenuation bands, and improved phase modulation compared to conventional FSS structures. This work highlights the potential of nanostructured, bio-derived cellulose for next-generation FSS devices, particularly for 5G and 6G applications.

The rapid expansion of internet of things (IoT) networks has intensified spectrum scarcity due to the massive growth in wireless device connectivity. Cognitive radio sensor networks (CRSNs) offer a promising solution by enabling dynamic access to underutilized spectrum bands. However, existing spectrum sensing techniques in CRSNs often suffer from high energy consumption, low adaptability, and limited prediction accuracy posing challenges in energy-constrained environments. This paper proposes an energy-efficient spectrum sensing (EESS) framework using an adaptive hybrid learning model (AHLM) that integrates wavelet transform-based signal decomposition (WT-SD), deep reinforcement learning (DRL), entropy-based hierarchical clustering (EHC), and meta-learning-based transfer learning (ML-TLM). WT-SD extracts key spectral features, while DRL with policy-gradient optimization dynamically predicts spectrum availability. The EHC mechanism clusters sensor nodes to minimize redundant sensing, and ML-TLM enhances adaptability with minimal retraining. The proposed model achieves substantial improvements over traditional methods. Experimental results show a 36% reduction in sensing time, 60% lower energy consumption than energy detection (ED) methods, and an 18.3% increase in network lifetime. The model also achieves a probability of detection of 0.998 and accuracy of 98.1%. These results confirm that the proposed EESS-AHLM framework provides a scalable and intelligent solution for energy-aware spectrum sensing in next-generation cognitive radio (CR)-IoT environments.

A compact metasurface-based tri-band MIMO antenna with minimalist decoupling for multi-standard wireless devices.

BackgroundThe widespread use of mobile devices has markedly increased global exposure to nonionizing electromagnetic waves (EMWs). Emerging evidence indicates potential biological effects of EMW exposure in susceptible populations, particularly pregnant women; however, findings remain inconsistent.ObjectiveThis protocol delineates a systematic review aimed at synthesizing and critically evaluating the teratogenic and pregnancy-related effects of nonionizing EMW exposure in pregnant women.MethodsThis protocol adheres to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2020 guidelines and has been registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42023475665). A comprehensive literature search will be conducted in PubMed/MEDLINE, Scopus, Web of Science, Embase, ScienceDirect, SpringerLink, Wiley Online Library, and Google Scholar, with supplementary searches of the World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov. Eligible studies will include pregnant women exposed to nonionizing EMWs from mobile phones and related wireless devices. The primary outcomes will be pregnancy complications and fetal anomalies, with secondary outcomes assessed as previously reported. Study selection, data extraction, and risk of bias assessment will be performed independently by 2 reviewers. Where appropriate, a random-effects meta-analysis will be conducted.ResultsFunding for this study was secured in March 2026. The literature search and study screening are planned for April to July 2026, with data extraction, risk of bias assessment, and synthesis expected to be completed by September 2026. The final results are anticipated to be submitted for publication in late 2026.ConclusionsThis systematic review is expected to provide consolidated evidence on the potential teratogenic and pregnancy-related effects of nonionizing EMW exposure, thereby supporting future research and evidence-based recommendations for public health.

Whether radiofrequency electromagnetic fields (RF-EMF) at wireless telecommunication frequencies can alter brain physiology remains a matter of debate. The 700 MHz band, recently allocated for 4G and early 5G deployment, is increasingly prevalent in the environment, yet its biological effects are poorly documented. Here, we investigated the impact of 700 MHz 5G-modulated RF-EMF exposure on two complementary central nervous system cell models: primary rat cortical astrocytes and human SH-SY5Y neuroblastoma cells. Cells were exposed in transverse electromagnetic (TEM) cells at specific absorption rates (SAR) of 0.08 W/kg and 4 W/kg, for 1 h or 24 h, and analyzed immediately or after a 24 h recovery period. Multiparametric flow cytometry quantified mitochondrial reactive oxygen species (ROS), cell viability, and apoptosis stratified as early and late, together with astrocytes' proliferation. Across all exposure conditions, no statistically significant differences were detected compared to sham controls, while positive controls with hydrogen peroxide elicited significant increases in ROS and apoptosis, validating assay sensitivity. These results demonstrate that, under strictly controlled iso-thermal conditions, 5G-modulated 700 MHz RF-EMF exposure does not induce measurable oxidative stress, apoptosis, or proliferative alterations in astrocytic and neuronal models. Our findings provide evidence supporting the absence of acute or subacute biological effects in vitro at isothermal exposure levels up to 4 W/kg, thereby reinforcing the scientific basis for current exposure guidelines.

Abstract The paper presents a compact triple-port equilateral triangular dielectric resonator antenna (ET-DRA) optimized for 5G N77 band applications (3.3–4.2 GHz). The antenna employs a shared radiator structure integrated with dielectric split ring resonators (DSRRs) to improve performance while maintaining a smaller footprint by localized H-field confinement. An ultra-wide impedance bandwidth is achieved due to the excitation of multiple modes in the ET-shaped DRA. Meanwhile, the integration of DSRR elements enhances the realized gain. The proposed antenna achieves a wide impedance bandwidth of 35% (3.2–4.44 GHz), an average gain of 5.7 dBi, and port isolation better than 15 dB across the operating band. The proposed design was fabricated and experimentally validated. Diversity parameters such as ECC, TARC, MEG, DG, and CCL were evaluated and found to be well within acceptable limits. This work presents an effective antenna that has a balance between compact size, wide bandwidth, and strong diversity performance, making it a promising solution for next-generation wireless communication devices.

Radiofrequency radiation, emitted from commonly used wireless communication devices, has been implicated in disrupting cellular homeostasis; however, its effects on testicular somatic cells such as Leydig cells remain poorly understood. To address this, the present study investigated the frequency- and time-specific effects of RFR on cellular morphology, proliferation, and cell cycle dynamics in TM3 Leydig cells. Cells were exposed to mobile phone radiation and radiofrequency signals at 1800MHz and 2450MHz for 15-120min under non-thermal conditions. Following exposure, morphological alterations were examined using Giemsa staining, while proliferation and cell cycle progression were evaluated by BrdU-ELISA and PI-based flow cytometry. BrdU assays showed a progressive reduction in DNA synthesis across conditions, indicating suppressed proliferative activity. Consistently, cell cycle analysis revealed accumulation of cells in G1 phase with a corresponding decline in S-phase population at longer durations, suggesting checkpoint activation. These changes were supported by morphological alterations such as cell rounding, loss of adherence, and membrane blebbing, features associated with stress-induced antiproliferative responses. Overall, these findings indicate that RFR disrupts cellular morphology, DNA synthesis, and cell cycle progression in a frequency- and time-dependent manner, highlighting Leydig cell vulnerability to prolonged exposure and potential implications for male reproductive health.