Search Results (277)

Peste des Petits Ruminants (PPR) is a highly contagious viral disease of small ruminants and remains a major threat to food security and rural livelihoods across Africa, the Middle East, and Asia. In the Mediterranean, uneven outbreak reporting and intense spatial clustering hinder the identification of regions where environmental and anthropogenic conditions favor disease occurrence. This study applied an interpretable machine-learning ecological niche modeling framework to characterize PPR spatial suitability in North Africa. A merged outbreak dataset (n = 744) was compiled from the Food and Agriculture Organization (FAO) EMPRES-i and the World Animal Health Information System (WAHIS) databases for 2005–2026. Outbreak locations were linked to environmental and anthropogenic predictors, spatially thinned, and paired with randomly sampled pseudo-absences at a 1:1 ratio. After correlation-based screening and Boruta feature selection, four classifiers were compared under five-fold spatial block cross-validation: a generalized linear model (GLM), a support vector machine (SVM), Random Forest (RF), and extreme gradient boosting (XGBoost). All models showed good discriminatory performance. Random Forest (RF) and extreme gradient boosting (XGBoost) yielded the highest area under the receiver operating characteristic curve value (AUC = 0.94). Random Forest achieved the highest specificity, XGBoost achieved the highest sensitivity, and the support vector machine showed the most even sensitivity–specificity tradeoff among the machine-learning classifiers. Sheep density, mean diurnal temperature range, temperature seasonality, and human population density were consistently the dominant drivers. Predicted PPR suitability based on reported outbreaks was concentrated along the North African coastal belt and low across most arid inland regions. These findings suggest that passive surveillance is likely to be most informative in coastal production systems where host density, environmental suitability, and reporting opportunity overlap. At the same time, areas of lower reported-outbreak suitability should not be interpreted as disease-free and may require complementary active surveillance approaches. Full article

In this article, a low-power low-intermediate-frequency (Low-IF) receiver front-end is presented for Bluetooth Low Energy (BLE) body area network (BAN) applications. The receiver employs an input matching network, an inductorless self-biased inverter-based low-noise transconductance amplifier (LNTA), a single-balanced passive mixer, a common-gate transimpedance amplifier (TIA), and a Gm-C complex filter for image suppression. Native MOS devices are adopted to support low-voltage operation and reduce static power consumption. The interstage on-chip coupling capacitor between the RF front-end and the TIA is removed by aligning the DC operating points of the two stages. The receiver front-end is implemented in a 55 nm standard CMOS process and occupies an active area of 0.081 ${\mathrm{mm}}^2$, excluding bonding pads. Post-layout simulations show that the proposed design achieves 45.2 dB gain, 7.2 dB noise figure, and 28.1 dB image rejection ratio over the 2.4–2.48 GHz band, while consuming 537 $\mathsf{\mu }$W. The proposed front-end is suitable for low-power BLE BAN sensor nodes. Full article

High-precision time and frequency references are essential for satellite navigation, deep-space exploration, and space science missions. To address the large size, high power consumption, and limited integration of conventional Passive Hydrogen Maser (PHM) servo electronics based on discrete analog chains, this paper proposes a miniaturized digital servo architecture for PHMs based on software-defined radio (SDR) and a field-programmable gate array (FPGA). The AD9364 is used as an integrated RF front end for microwave interrogation signal generation, receiver down-conversion, and analog-to-digital conversion (ADC), while digital demodulation, discriminator construction, and closed-loop control are implemented in the FPGA. A dual-frequency interrogation and time-division multiplexing scheme is introduced to separate the atomic and cavity responses, and an oversampling-based processing method combining outlier rejection and averaging decimation is adopted to improve the observation accuracy and noise immunity of weak error signals. Experimental results demonstrate stable closed-loop locking of the atomic transition spectrum, achieving a frequency stability of 1.46 × 10⁻¹² at 1 s, while significantly improving the compactness and integration level of the servo electronics. Full article

Background/Objectives: Osgood–Schlatter disease (OSD) is a common overuse condition in adolescents characterized by increased stiffness of the rectus femoris muscle, which contributes to pain and functional limitations around the knee. We investigated whether repeating 10 min passive joint movements of the hip and knee produces additional immediate reductions in elevated rectus femoris (RF) stiffness in adolescents with OSD. Methods: Fifteen patients (10–14 years of age) diagnosed with bilateral OSD were included. The legs of the participants were randomly assigned to either the intervention or the non-intervention side (control). The intervention side received two sets of 10 min of passive joint movement to the hip and knee, while the control side rested. RF stiffness was measured before the intervention and immediately after one and two sets of passive joint movements. Results: On the intervention side, RF stiffness decreased significantly from pre to post-1 and from pre to post-2; however, RF stiffness did not differ significantly between post-1 and post-2. None of the parameters changed significantly on the control side (rest condition). Conclusions: Passive joint exercise beyond one repetition (one set for 10 min) did not result in a further decrease in RF stiffness and is likely unnecessary for RF muscle stiffness due to OSD. Full article

Ambient backscatter systems enable passive sensing and information transfer by utilizing the reflection and modulation of incident radio-frequency (RF) signals. However, in real-world scenarios involving non-cooperative targets such as off-the-shelf printed circuit boards (PCBs), the backscattered signal is extremely weak and often obscured by strong direct-path self-interference (SI) at the receiver. This issue becomes even more severe when unintentional PCB structures act as radiating elements. In this work, we explore ambient backscatter leakage from a compromised PCB using a realistic measurement setup that includes separated transmit and receive antennas and a direct-conversion Universal Software Radio Peripheral (USRP)-based receiver. We demonstrate that residual carrier frequency offset (CFO), caused by oscillator mismatch and hardware imperfections, can spread the dominant SI in the baseband and completely mask the weak backscattered signal. To solve this problem, a software-based post-processing framework is applied. This method leverages the complex baseband representation enabled by the homodyne receiver to jointly manage the carrier and SI components without relying on intermediate-frequency processing or prior knowledge of the target signal parameters. Experimental results show that this approach significantly improves the detectability of weak backscattered baseband information that would otherwise be concealed within the raw I/Q data. This study emphasizes the importance of CFO-aware digital processing in ambient backscatter systems and offers new insights into unintended electromagnetic leakage mechanisms from commercial PCB platforms. Full article

This paper proposes a SIM-compatible hardware coordination architecture for secure radio-frequency (RF)-triggered activation in mobile devices. The proposed concept functions as a passive coordination layer rather than as an additional wireless transceiver, enabling controlled interaction between external low-frequency RFID or high-frequency NFC fields and wireless subsystems already available in the host device. The architecture assumes a flexible nano-SIM-compatible form factor integrating passive RF detection structures, a trusted decision component, and a trigger-generation interface aligned with standard SIM/UICC electrical and logical interaction models. Upon detection of an external electromagnetic field, the coordination layer evaluates predefined authorization conditions and produces a controlled trigger event intended to propagate through existing telephony and system-service pathways. In contrast to architectures that embed active wireless transmitters, the proposed approach seeks to minimize hardware redundancy and reduce potential attack surfaces by relying on the host device’s native Bluetooth Low Energy (BLE) capabilities. Rather than directly controlling wireless modules, the interface operates as a hardware-originated coordination mechanism that may support low-power and context-aware activation scenarios in mobile and embedded environments. This paper focuses on the architectural model, system assumptions, security rationale, and implementation constraints of such a SIM-compatible interface. Particular attention is given to integration considerations related to smartphone baseband architectures, operating-system mediation, and secure-element isolation. The presented concept establishes a foundation for future prototype implementation and platform-specific validation of SIM-compatible RF-triggered coordination mechanisms. Full article

Accurate occupancy information is critical for optimizing energy efficiency in buildings. Hybrid machine learning models have demonstrated great potential in previous studies; however, their application in passive ultra-low-energy buildings remains underexplored. This study conducts an empirical evaluation of real-time occupancy rate prediction using a CNN-ResNet-RF hybrid model based on multi-source environmental and behavioral data from a passive ultra-low-energy educational building. The model integrates Convolutional Neural Networks (CNN) for local feature extraction, Residual Networks (ResNet) to enhance deep feature representation, and Random Forests (RF) for ensemble-based generalization. Indoor CO₂ concentration exhibits the strongest linear correlation with occupancy rate (r = 0.54), indicating a meaningful association with occupancy dynamics. The model demonstrates strong predictive performance on the test set, with a coefficient of determination (R²) of 0.964, a root mean square error (RMSE) of 0.054, and a residual prediction deviation (RPD) exceeding 5. Compared with baseline models such as CNN, RF, and CNN-RF, the proposed framework exhibits generally lower prediction errors and improved stability. Further lightweight compression experiments reveal that the structured compact CNN-ResNet-RF-25 variant achieves even better accuracy (R² = 0.9748, RMSE = 0.0449, RPD = 6.327) while substantially reducing model complexity, demonstrating strong deployment potential in resource-constrained environments. Full article

Accurate modeling of RF capacitors and inductors is critical for predicting circuit behavior and ensuring operational robustness in high-frequency electronic systems. However, SPICE models are often unavailable from manufacturers, and there is still a lack of reliable methodologies for accurate modeling of such passive components over a wide frequency range. This paper presents a systematic and practical equivalent-circuit modeling methodology for capacitors and inductors based on measured impedance data. The proposed approach partitions the entire frequency range into multiple sub-bands and models each using a combination of a core series RLC network and frequency-dependent parallel RC, RL, and RLC sub-networks. This piecewise construction enables the dominant resistive, inductive, and capacitive behaviors to be independently identified and accurately captured in their respective frequency regions, resulting in an accurate broadband equivalent circuit. The resulting models exhibit excellent agreement with target data, demonstrating the reliability of the method. This work provides a practical and systematic procedure for developing accurate broadband models of RF passive components, with validation demonstrated for capacitors up to 6 GHz and inductors up to 20 GHz. Full article

Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape (“FSR”), the Atewa Range Forest Reserve (“FR”), and the surrounding disturbed area (“SR”). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r²) and root mean square error (RMSE). The global RF model achieved the best performance (r² = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The “SR” models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the “FR” regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the “SR” and “FSR” regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. Full article

Low-Temperature Co-Fired Ceramic (LTCC)-based mechanical sensors are inherently limited by the thickness and rigidity of multilayer ceramic stacks, which restrict miniaturization and mechanical compliance. To overcome these constraints, this work presents a hybrid LTCC/Kapton^® platform enabling high-sensitivity mechanical sensing through mechanically tunable RF passive components. The proposed approach integrates a flexible polyimide membrane, bonded onto an LTCC substrate at low temperatures using selectively electroplated indium pillars that simultaneously define the air gap and provide mechanical fixation. Inductance tuning is achieved via metal-shielding proximity effects, whereas capacitance tuning relies on force-controlled air-gap modulation in a metal–insulator–metal configuration. The fabrication process ensures precise gap control, high compliance, and structural robustness without requiring deformable ceramic membranes. Experimental characterization, including three-dimensional surface profiling and impedance measurements, demonstrates a 48% inductance tuning range with a sensitivity of 0.715 nH/mN and a 36% capacitance tuning range with a sensitivity of 47.3 fF/mN at 1 MHz. The proposed hybrid platform provides a compact and scalable solution for high-sensitivity sensors and mechanically reconfigurable RF components suitable for harsh-environment and adaptive electronics applications. Full article

As a key parameter of the Earth’s ecosystem, soil moisture significantly influences land-atmosphere interactions and has important applications in meteorology, hydrology, and agricultural studies. However, existing passive microwave remote sensing products of soil moisture are limited by their discontinuous temporal coverage and relatively coarse spatial resolution (typically 25–55 km), which cannot meet the requirements for fine-scale applications. This study developed and compared four machine learning-based downscaling approaches to improve the spatiotemporal resolution of MCCA SMAP soil moisture products. The methodology involved establishing complex nonlinear relationships between soil moisture and various high-resolution surface parameters including albedo, evapotranspiration, precipitation, and soil properties. High-resolution soil moisture maps were generated by leveraging the scale-invariant characteristics between soil moisture and surface parameters, followed by comprehensive evaluation using in situ ground observations and triple collocation analysis. The results demonstrated that all downscaling models showed excellent consistency with original MCCA SMAP observations (R > 0.93, RMSE < 0.033 m³ m⁻³), while successfully providing enhanced spatial details. The Random Forest (RF) model exhibited superior performance, showing higher correlation coefficients and lower biases when compared with in situ measurements. Uncertainty analysis revealed relatively low uncertainty levels for all models except Backpropagation Neural Network (BPNN) model. The RF-downscaled products accurately tracked temporal variations of soil moisture and showed good responsiveness to precipitation patterns, demonstrating their potential for fine-scale hydrological applications and regional environmental monitoring. Full article

Rural transportation corridors remain weakly instrumented for continuous low-altitude airspace monitoring. At the same time, Vehicle-to-Everything (V2X) roadside units (RSUs) are increasingly deployed for transportation safety services. This work investigates whether existing RSUs can be extended with passive, cooperative RF sensing to detect small UAVs without modifying standards-compliant ITS communications in the protected 5.9 GHz band. A calibrated simulation study evaluates corridor-scale operation under realistic propagation conditions, including terrain masking and narrowband interference. All results reported in this paper are derived from simulation and do not include field measurements or hardware prototyping. False alarm performance under diverse ISM emitters is not quantified. The results show that cooperative processing across neighboring RSUs improves epoch-level verified detection coverage compared with single-RSU sensing. Bearing variability is reduced for weak or partially masked signals. These gains result from feature-level validation across spatially separated receivers rather than deterministic signal combining. RF calibration constrains detections to physically plausible kilometer-scale ranges. The resulting angular accuracy is sufficient for early warning and track initiation, but not for precise localization. Overall, the findings indicate that existing V2X infrastructure can support supplementary early warning capability for corridor-scale airspace monitoring while preserving primary V2X safety functions. Full article

Background/Objectives: Accurate three-dimensional (3D) facial scanning is increasingly important in digital dentistry for diagnosis, treatment planning, and virtual patient creation. Multiple facial scanning technologies are available; however, their metric reliability varies depending on acquisition principles and anatomical orientation. This study aimed to evaluate the trueness, orientation-dependent performance (vertical midline versus horizontal facial measurements), and scanning time of four facial scanning technologies using calibrated manual anthropometry as the reference standard. Methods: Thirty dentate adult participants received adhesive fiducial markers on five predefined facial landmarks. Four linear facial distances were measured clinically using a digital caliper and compared with corresponding measurements obtained from standardized 3D facial scans. Digital measurements were extracted following uniform metric normalization. Inter-examiner reliability, measurement trueness, orientation-related differences, and scanning time were analyzed. Results: Inter-examiner reliability was excellent for both clinical and digital measurements (ICC > 0.93). All facial scanning technologies significantly overestimated manual distances (p < 0.001). The structured-light scanning system showed the smallest deviations (typically <1 mm) and the highest overall accuracy, followed by the depth-fusion system, while photogrammetry-based and NeRF-based approaches demonstrated larger errors, frequently exceeding 2–3 mm. Horizontal facial distances consistently showed greater deviations than vertical midline measurements across all systems. Scanning time differed significantly between technologies, with passive image-based approaches being the fastest and NeRF-based acquisition requiring the longest capture time. Conclusions: Active structured-light facial scanning demonstrated the highest trueness for linear facial anthropometry, whereas passive photogrammetry and NeRF-based approaches showed lower metric trueness and are currently more suitable for educational applications. Full article

In grain storage and transportation, biological activity, including respiration and metabolism, generates heat, creating temperature gradients that can induce moisture migration and form high-humidity areas. This accelerates fungal and insect activity, leading to quality degradation. Long-term, distributed temperature monitoring inside grain piles is essential for ensuring safe storage and early risk warning. Radio Frequency Identification (RFID) technology has become widely adopted in storage temperature monitoring due to its low cost, maintenance-free operation, and high security. However, traditional RFID systems have limited communication ranges, and the large size of storage facilities necessitates the deployment of multiple readers, which increases costs. Additionally, the high density and fluctuating moisture content of bulk grain lead to significant RF signal absorption and scattering, weakening the accessibility of purely wireless systems to deeper parts of the grain pile. To address these issues, a passive distributed temperature monitoring system based on RFID technology is proposed. The system utilizes RFID readers to harvest RF energy for passive power supply, with an external antenna ensuring stable energy harvesting and data transmission. Single-bus multi-point temperature sensor modules are integrated into the system, enabling distributed temperature measurements across grain piles or warehouses. Experimental results show that the system achieves a temperature collection success rate of 98%, with an accuracy of ±1 °C and a polling cycle of less than 30 s, providing a low-cost, battery-free, and scalable solution for grain storage monitoring, significantly improving storage quality. Full article

Understanding the drivers of soil carbon efflux is critical for predicting forest carbon cycles under climate change. This study investigates how plant roots and phenology govern the spatio-temporal variability of boreal forest floor respiration (Rf) in an ectomycorrhizal-dominated forest. By analyzing stabilized soil carbon fluxes (NEE, Ra, and Rh) one year after root exclusion in northern Sweden, we challenge the passive physicochemical paradigm. Results show that the spatial distribution and magnitude of Rf are primarily driven by plant roots, with Ra accounting for >60% of total efflux. The collapse of respiration in trenched plots confirms the mycorrhizal bridge as the essential conduit for these spatial patterns. Regarding temporal variability, we identified a biological pulse driven by plant phenology. After temperature-normalization, Ra maintained a strong seasonal peak in July and August. Notably, static drivers like fine root biomass failed to explain spatial variation (R < 0.3, p > 0.05), whereas dynamic NEE showed significant positive correlations (R = 0.52, p < 0.0001). This holistic perspective suggests that the forest floor operates as an integrated metabolic continuum, where root activity and phenological pump are the main regulating factors on carbon release. Future models should reposition plant–fungal phenology as the primary engine of soil metabolism. Full article