Mobile health (mHealth) is a promising tool for improving healthcare access, particularly in low-resource settings. However, limited mobile accessibility and poor connectivity remain significant barriers to implementing mHealth interventions in these regions. To address these challenges and support the development and scalability of mHealth studies, we developed Pharos, a mobile GIS application designed to assess network coverage and facilitate ground-truth mapping. Pharos autonomously measures spatiotemporal variations in mobile network signal strength and enables precise mapping of critical landmarks and environmental features. We deployed Pharos in a four-county region along the shores of Lake Victoria in Western Kenya as part of a preparatory phase for a large-scale mHealth study focused on schistosomiasis control. Over six months and 10,000 km2, Pharos collected high-resolution data on network performance and landmark locations, generating a comprehensive dataset that links network availability with environmental features. These results provide essential insights for planning and implementing mHealth interventions in low-resource settings, with potential applications in infectious disease surveillance and other global health initiatives.

Periodic changes in visual input elicit rhythmic patterns in EEG signals that manifest as narrowband frequency components. These components are typically interpreted as signatures of neural populations sensitive to the modulated stimulus feature. We propose an alternative scenario in which such frequency components arise primarily from retinotopic variations in signal strength, without requiring feature-selective neural mechanisms. Using both simulated and empirical data (Experiment 1: N = 13; Experiment 2: N = 13), we demonstrate that signal fluctuations driven solely by the retinotopic position of a position-modulated stimulus can generate identifiable frequency components. These components are more plausibly attributed to structural properties of cortical organization that shape the relative contribution of different retinotopic areas to the EEG signal. Our findings challenge the conventional assumption that stimulus-related frequency components necessarily reflect feature-specific neural computations, indicating instead that functional interpretations are not guaranteed when spatiotemporal regularities in the stimulus introduce systematic population-level variability.

Abstract We conduct an in-depth study of the quasi-periodic oscillation (QPO) properties of RE J1034+396 by constructing QPO phase-folded light curves from 10 XMM-Newton observations during 2020–2021. Our analysis reveals that the QPO in the source exhibits two mutually convertible lag-energy modes: “hard lag” and “soft lag.” Despite different lag characteristics, the energy dependencies of the rms amplitude of the QPO under both modes are consistent, suggesting the two types of QPO originate from the same physical mechanism. By performing a spectral analysis, we further find a correlation between time-lag modes and spectral states: the soft lag mode typically corresponds to harder X-ray spectra and higher blackbody temperatures. Through comprehensive comparison of multiple theoretical models, we propose that the relativistic precession model of the corona provides a plausible qualitative explanation for the observed complex phenomena, including time-lag mode transitions, and variations of spectral hardness and QPO signal strength.

Accurate radio environment map (REM) construction proves critical for efficient wireless spectrum management. Although conventional 2D REMs have demonstrated effectiveness in wireless network optimization, they inherently overlook vertical signal strength variations, which are vital for UAV operations, particularly in urban landscapes with skyscrapers or diverse terrain features. Current estimation approaches, including ground-based crowdsourcing, random sampling, and predetermined trajectory measurements, show limited capability in generating high-fidelity 3D REMs. This study proposes a joint optimization framework for UAV-enabled adaptive 3D radio mapping, integrating 3D REM construction with adaptive aerial sampling. At the heart of the construction module, a dual-branch encoder-decoder architecture fuses multi-scale features from sparse aerial measurements with building structural data, explicitly modeling obstruction effects through offline pre-training and online refinement to enhance generalization. For adaptive sampling, a diffusion-based trajectory planner dynamically optimizes UAV measurement paths by integrating environmental priors (e.g., building layouts), effectively overcoming the sparse-reward limitations inherent in reinforcement learning methods. Experimental validation demonstrates significant performance improvements across all evaluation metrics. Compared to 2D per-layer estimation methods, our 3D estimator achieves 49% superior structural similarity (SSIM) in construction accuracy, while the feature fusion module yields a 37% reduction in mean squared error (MSE). The diffusion-based planner outperforms reinforcement learning approaches by achieving 45% lower MSE and 18% higher SSIM in resultant map quality after 5,000 step iterations.

ABSTRACT In urban environments, Global Navigation Satellite System (GNSS) signals are highly susceptible to obstructions from tall buildings, leading to Non-Line-of-Sight (NLOS) errors, and severe positioning degradation. Machine Learning (ML)-based NLOS detection has gained significant attention, due to its high accuracy and the advantage of requiring no hardware modifications. However, the existing studies predominantly rely on single-model architectures, which often suffer from poor generalization, and a tendency to converge to local optima. To overcome these limitations, this study proposes a GNSS NLOS detection method based on a two-layer Stacking Ensemble Learning (SEL) model and five key GNSS signal features. A comprehensive weighting model is then applied to correct NLOS-induced pseudorange errors. Results show that the SEL model, utilizing our selected GNSS signal feature set for NLOS detection, achieves a 13.1% improvement in classification accuracy, compared with the traditional feature sets. This improvement is primarily attributed to the more precise and comprehensive selection of features, which collectively consider satellite geometry, signal strength variations, dynamic characteristics, and pseudorange errors. Moreover, the SEL model integrates multiple heterogeneous base models, demonstrating superior generalization capability and higher detection accuracy, making it particularly well suited for GNSS observation data processing in complex urban areas, encompassing multiple environments. Specifically, it achieves NLOS classification accuracies of 94.5% and 93.1% in low-speed and high-speed dynamic scenarios, respectively. Furthermore, the SEL model exhibits lower positioning errors and enhanced robustness. Compared with single base models, it reduces horizontal and vertical positioning errors by 29.7% and 25.7% in low-speed dynamic scenarios, respectively, while by 26.3% and 25.9% in high-speed dynamic scenarios. Our proposed method requires no additional hardware modifications to low-cost receivers while achieving high positioning accuracy and reliability, offering a new approach for continuous high-precision navigation in smart city applications.

Early detection of corrosion in reinforced concrete using ultrasonic guided wave technique correlated with embedded fiber bragg grating strain sensors.

Drug-induced interstitial lung disease (ILD) is a potentially severe pulmonary complication associated with various immunomodulatory and antineoplastic agents. Despite increasing recognition, comparative disproportionality data across drug classes remain limited. We conducted a retrospective pharmacovigilance study using the FDA Adverse Event Reporting System (FAERS, 2004–2024). Twelve agents with known or suspected associations with ILD were selected. For each drug, the total number of adverse events and ILD reports were extracted. The proportional reporting ratio and reporting odds ratios (RORs) with 95% confidence intervals (CIs) were calculated to assess disproportionality. Methotrexate and rituximab accounted for the highest number of ILD reports. Amiodarone hydrochloride showed the highest proportion of ILD among its adverse events (9.4%) and the strongest disproportionality signal (ROR = 7.11; 95% CI: 6.79–7.45). Elevated RORs were also noted for leflunomide (3.05), tocilizumab (1.94), pembrolizumab (1.89), and methotrexate (1.90). In contrast, TNF-α inhibitors such as adalimumab (ROR = 0.29) and etanercept (ROR = 0.34) were associated with lower disproportionality signals. Significant variation in ILD signal strength was observed across drug classes. Amiodarone and leflunomide showed disproportionately strong ILD signals, while TNF-α inhibitors demonstrated lower reporting frequencies. These findings underscore the need for ongoing pharmacovigilance when using agents with potential pulmonary toxicity.

This work proposes a method of non-invasive blood glucose measurement based on the absorption properties of radio frequency signals as they pass through biological tissues. The core idea is that certain radio frequencies (RF) frequencies within the very high frequency (VHF) range interact differently with tissue depending on glucose concentration, altering signal strength in detectable ways. To investigate this, a series of experimental analysis are conducted on RF signal propagation throughout multiple mediums similar to human tissue indicating a particular frequency band which is sensitive to variations in glucose levels. Within this band, two resonant frequencies 156 MHz and 189 MHz are selected for in-depth study. In this context, a compact and low cost printed antenna is designed, simulated, fabricated, and characterized to operate efficiently at these frequencies. A measurement system, comprising transmitting and receiving antennas, records received signal strength (RSS) variations at the resonant frequencies, both with and without a fingertip present in the sensing region. Rather than using the raw signal strength values recorded during the investigation, a differential measurement approach is employed to enhance sensitivity and reliability. These variations in RSS are then compared with glucometer readings. The clinical trial includes eight volunteers whose participation provide the dataset used for evaluating system performance. A polynomial regression analysis shows moderate accuracy (R² = 0.5850). These results exhibit the potential of this non-invasive, RF-based method for continuous glucose monitoring. With further refinement, this technique could offer a practical, painless alternative to conventional invasive procedures that might completely transform diabetes control.

Abstract In this article, a number of smoothing methods were investigated to enhance the signal-to-noise (S/N) ratio of diverse methanol maser spectral data, encompassing variations in signal strength, multiplicity of peaks, and spectral complexity. The study aimed to improve the accuracy and reliability of astronomical measurements obtained with Irbene radio telescopes RT-16 and RT-32 at the Ventspils International Radio Astronomy Center. Comparing eleven different smoothing techniques, including moving average, Gaussian, Hanning, among others, the Savitzky–Golay smoothing method is identified as the optimal choice. The evaluation criteria included the preservation of spectral features, reduction of noise artifacts, and enhancement of S/N ratio metrics. The Savitzky–Golay method outperformed other techniques by effectively balancing noise reduction with the preservation of spectral details crucial for maser emission analysis.

Drug-associated hyperprolactinemia: A comprehensive disproportionality analysis based on the FAERS database.

This paper demonstrates that, using multiple data points around each gateway allows for the creation of accurate coverage heat maps by using a proposed LoRaWAN coverage estimation algorithm. This method effectively identifies areas with varying signal strength across Da Nang City, highlighting where additional gateways are needed to improve network reliability. Unlike previous studies, this research considers environmental factors such as topography and urban structures, enhancing prediction accuracy. This technique would be essential for optimizing essential for optimizing LPWAN deployments and ensuring efficient resource utilization. Future efforts will focus on refining neural network models and integrating real-time data to support scalable Smart City solutions and a more robust connectivity infrastructure, with this work planned for future research.

The Internet of Things (IoT) is rapidly developing with the promotion of new technologies such as LoRa, which offers extensive coverage, low power consumption, and strong anti-interference capabilities. This study focuses on the application of LoRa technology in multi-floor home environments, particularly addressing the challenges of signal multipath propagation. We conducted comprehensive measurements of LoRa signal strength and path loss across different floors and rooms. Through our path loss model analysis, notable differences were observed in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, with initial path loss values of 58.32 decibels and 51.52 decibels, respectively, and standard deviations of 18.42 decibels for LOS and 2.84 decibels for NLOS. Temporal fading analysis, using Rayleigh and Rician distributions, revealed significant variations in signal strength between daytime and nighttime, with some rooms being more stable during the daytime and others more stable at nighttime due to differences in the architectural structure and functionality of various rooms within the home environment. Packet reception rate (PRR) ranged from 89.07% to 99.89%, highlighting the reliability of data transmission under different conditions. This research fills a critical gap in the literature by providing empirical data on indoor multi-floor home environments and significantly contributes by verifying and modeling path loss and temporal fading, thereby improving the design and deployment strategies for LoRa-based smart home systems.

Non-Terrestrial Networks (NTN) are envisioned to provide ubiquitous connectivity for next-generation wireless communication systems. However, due to the unique characteristics of the NTN environment, such as low signal strength variation and long propagation delay, users may experience non-robust handover. To address these challenges and enhance the user experience, the 3rd Generation Partnership Project (3GPP) has studied mobility enhancements for NTN since Release 17. Two mechanisms initially designed for Terrestrial Networks, Random Access Channel-less Handover and Conditional Handover, have been adapted and extended to suit the NTN scenario. In addition, a novel mobility enhancement called satellite switching with resynchronization was introduced in 3GPP Release 18, aiming to reduce signaling overhead and improve handover efficiency in satellite communication scenarios. This article provides an overview of the mobility enhancements for NTN in the 3GPP 5G New Radio system, along with a technical background detailing the key modifications and new features introduced to address the distinct challenges of satellite-based communication environments.

<sec>Non-magnetic optical non-reciprocal devices are conducive to constructing optical information processing networks for weak signals without using any external magnetic field. In this work, the non-reciprocal transmission of electromagnetically induced transparency (EIT) in a cesium atomic gas through laser exciting a Λ-type three-level atomic system is observed experimentally.</sec><sec>With the help of cesium atoms, which have several advantages over other alkali atoms, such as a rich and readily adjustable energy level structure, bigger ground state hyperfine energy levels, and lower saturation light intensity. An 894.596 nm laser, as probe light, excites energy level from 6S<sub>1/2</sub> (<i>F</i> = 4) to 6P<sub>3/2</sub> (<i>F</i> = 5), and an 894.594 nm laser, as coupling light, is divided into two beams to excite energy level from 6S<sub>1/2</sub> (<i>F</i> = 3) to 6P<sub>3/2</sub> (<i>F</i> = 5). The coupling light enters the cesium atomic gas cell in two directions: either collinearly incident in the same direction as the probe light, or in the opposite direction. The probing light that interacts with the coupling light inside the cesium atomic gas and then is detected by the detector avalanche photodiode, and the outcomes are shown and measured on an oscilloscope.</sec><sec>The experimentally observed non-reciprocal transmission of EIT proves optical signal isolation in a cesium atomic system. Under the experimental conditions, a series of experiments is conducted on the regulation of the optical non-reciprocal isolation ratio at room temperature by adjusting the power of the probe light and coupling light as well as the detuning. The influence of adjustable parameters on the non-reciprocal isolation ratio is analyzed. The results show that moderate probe light power helps maintain the intensity of EIT in the absorption intensity curve, ensuring a high isolation ratio, which provides a reference for implementing the performance metrics of optical isolators. The observed isolation ratio increases with the increase of coupling power, which is consistent with the theoretical calculation. Within a certain range of coupling light power, a high-performance optical non-reciprocal system is achieved. This trend is exactly in line with that of EIT signal strength variation during co-directional coupling light excitation. A maximum isolation ratio 26 dB is obtained when many parameters are appropriate. The results indicate that in the coherently prepared cesium atom systems, optically tunable parameters can provide an effective means for achieving ideal optical isolation with a high isolation ratio. Compared with existing research on high isolation ratio cavity-free non-reciprocity based on atomic coherence, our proposed experimental scheme can be conducted by using a three-level system at room temperature. With the development of chip-level integrated gas cells, the achieving miniaturization and system integration become easier, which provides experimental support for achieving the miniaturization and integration. This work provides a certain basis for exploring high-performance non-reciprocal devices with high isolation ratios and new perspective for designing the next generation of optical equipment.</sec>

Baishouwu, derived from Cynanchum auriculatum (CA) Royle ex Wight, Cynanchum bungei (CB) Decne., and Cynanchum wilfordii (CW) (Maxim.) Hemsl., is a valuable traditional Chinese medicine. CA is also recognized as a new food resource by China's National Health Commission. Given the considerable variations in flavor and chemical composition among these species and lack of their qualitative assessments, accurately differentiating between the species constituting Baishouwu is essential. To develop a method combining electronic tongue (E-tongue), electronic nose (E-nose), and ultra-performance liquid chromatography-quadrupole-time of flight/mass spectrometry (UPLC-Q-TOF/MS) to differentiate between Baishouwu samples. Fifteen batches of Baishouwu samples were analyzed using E-tongue, E-nose, and UPLC-Q-TOF/MS. Flavor differences and key differential metabolites were determined through principal component analysis and orthogonal partial least squares discriminant analysis. E-tongue results revealed umami, sweetness, and richness as the predominant flavors of Baishouwu, with CA having the highest umami response, CW exhibiting the highest bitterness, and CB the highest sweetness. E-nose sensors showed consistent responses across species, with variations in signal strength; W1W and W2W sensors showed the highest response values. A total of 158 and 41 characteristic variables in the positive and negative ion modes, respectively, were selected as candidate differential metabolites, of which 29 and 14 were confirmed through database comparison. Eight critical differential metabolites, including C21 steroids and acetophenone compounds, were identified. This study presents a strategy for differentiating among the species constituting Baishouwu, providing a basis for broader application and establishing quality standards for these medicinal compounds.

Conventional techniques for identifying heavy metal ions in water are laborious and time-consuming. Therefore, it is necessary to create innovative sensing technologies that can detect with greater sensitivity and speed. Although there have been reports of optical-based assays utilising fluorescent nanomaterials, these assays usually rely on variations in signal strength. However, this approach has significant drawbacks when it comes to environmental monitoring. Fluorescence carbon dots (CDs) have been prepared by facile synthesis from Blood berries. A homemade heavy metal optical detector is constructed to accurately identify heavy metal ions, exclusively Cr6+ ions in a water medium. Their optical emission signature varies based on the specific chromium ions in solution, and the emission intensity also changes depending on its concentration. The quenching behaviour is attributed to the interaction between the metallic cations and the fluorescent surface states of the carbon dots. Another application is the encapsulation of CDs in PVDF polymer to form a flexible film and use it as a phosphor for LED conversion.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

In Vehicular Ad-Hoc Networks (VANETs), ensuring efficient intrusion detection while minimizing false alarms is paramount for maintaining network security and reliability. Traditional methods often struggle to strike this balance effectively. This study proposes a novel approach that employs fuzzy ranking to enhance intrusion detection efficiency in VANETs while mitigating false positive and negative factors. At the core of our approach lies the development of an innovative fuzzy ranking mechanism to identify optimal features extracted from VANET nodes. These features serve as critical indicators of potential security threats within the network. By systematically collecting and analyzing data from various sources including vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, our approach aims to capture nuanced patterns and anomalies indicative of malicious behavior. The fuzzy ranking technique enables us to prioritize features based on their relevance to intrusion detection, thereby facilitating more effective feature selection. This process ensures that only the most informative features are utilized, enhancing the overall efficiency of the intrusion detection system. Features such as packet transmission rates, signal strength variations, route deviation patterns, and network topology characteristics are carefully evaluated and ranked to optimize detection accuracy. To train our intrusion detection model, we leverage machine learning algorithms such as deep neural networks, support vector machines, and decision trees. By utilizing labeled datasets containing examples of both benign and malicious network activities, our model learns to distinguish between normal and anomalous behavior, thus enabling timely threat detection. Furthermore, we integrate fuzzy logic-based anomaly detection techniques to identify previously unseen or zero-day attacks, enhancing the system's robustness. In addition to improving detection accuracy, our approach focuses on preventing harmful warnings caused by false alarms. We achieve this by implementing a comprehensive filtering mechanism that distinguishes between transient anomalies and genuine security threats. Contextual factors such as traffic conditions, environmental variables, and historical network behavior are considered to reduce false positives while ensuring reliable threat detection.Through extensive experimentation and evaluation, we demonstrate the effectiveness of our approach in enhancing intrusion detection efficiency and reducing false positive/negative factors in VANETs. The proposed methodology offers a promising solution to the security challenges faced by VANETs, paving the way for safer and more secure vehicular communication systems in the future.

As a simple and inexpensive channel characteristic, received signal strength (RSS) finds extensive usage in localization applications. However, the quick changes in signal strength impact the localization precision. By averaging over access points (APs) with multiple frequencies and/or heights, this article suggests a novel approach to lowering RSS fluctuation. Initially focused on the plane-earth loss model, the study was later extended to include a multipath indoor propagation scenario that was simulated. We used ray-tracing software to model the indoor propagation situation. This research takes into account the results of three distinct methods for averaging RSS: height averaging, frequency averaging, and hybrid frequency and height (FH) averaging, which combines the two. We discovered that the Height-only strategy considerably decreased the RSS variation with distance for both settings we looked at. Using the frequency-only method even further reduced the variation. Using the Hybrid FH technique greatly enhances the results. Root mean square error values of 4.427 dB, 3.70 dB, and 3.5 dB, respectively, are provided for the averaging approaches and the ideal scenario in which no variance occurs. Another finding is that averaging with APs that have double the height or frequency will not improve the RSS distance variation.

Abstract Preclinical models of Sonic Hedgehog (SHH)-driven tumors support the therapeutic benefit of pharmacological inhibitors, yet clinical outcomes are conflicted due to biphasic tumor responses that include complete remission or faster disease progression. In such studies, the activity of the SHH pathway in the tumor-adjacent stroma supported or restrained tumor progression. Our prior studies show that altered SHH ligand levels can lead to faster tumor growth during pharmacological inhibition suggesting a role for the SHH pathway strength in the biphasic tumor responses of a triple-negative breast cancer (TNBC) model. In this study, the role of SHH pathway strength altered by tumor-fibroblast proximity was examined on the tumor growth, invasion, and transcriptional activity using in vitro and in vivo models. Tumor spheroid models composed of TNBC cell lines and fibroblasts were used to monitor SHH signal strength as a function of the culture modality and SHH ligand levels. In vitro, SHH signal strength was altered by tumor-fibroblast proximity and ligand concentration. Mixed cultures exhibited significantly higher SHH activity and reduced invasion than adjacent cultures. This effect was reversed by pharmacological inhibition of SMO, confirming the involvement of an SHH-dependent mechanism. In a tumor xenograft model, tumor metastasis outcomes were suppressed by pharmacological inhibition of the SHH pathway, but opposite results were observed in tumor xenografts composed of co-injected with fibroblasts indicating that the biphasic tumor response can be driven by the fibroblasts. Spatial transcriptional analysis identified extracellular matrix (ECM) activities in fibroblasts as the top altered bioprocesses in both in vitro and in vivo. This finding was confirmed in cultures, where fibroblast-derived decellularized matrix stimulated tumor invasion of single TNBC spheroids at levels observed in co-cultures. Further analysis of differentially expressed genes (DEGs) identified a small leucine-rich repeat proteoglycan (SLRP) as the primary ECM target altered by variations in SHH signal strength in fibroblasts. Furthermore, we confirmed the role of SLRP cues in governing tumor invasion potency within spheroid cultures embedded in a collagen hydrogel matrix. While high levels of SLRP in the matrix inhibited spheroid invasion, low SLRP matrix promoted spheroid invasion and increased secondary invasion sites. These findings agreed with the Vimentin expression levels observed in TNBC cells and confirmed the potency of SLRP in regulating tumor invasion outcomes. In summary, our study underscores the significance of ECM remodeling activities in fibroblasts and the role of SLRPs in governing tumor invasion, with implications for understanding the impact of altering SHH signaling activity in TNBC. Citation Format: Emanuel A. Carrasquillo-Dones, Heizel M. Rosado-Galindo, Ana M. Reyes-Ramos, Wandaliz Torres-Garcia, Said Cifuentes, Jan P. Rios-Grant, Gabriela Ortiz-Soto, Natalia A. Ramos-Acevedo, Miosotis Acevedo-Esquilin, Monica Colon-Vargas, Israel Almodovar-Rivera, Camilo Mora, Michelle M. Martinez-Montemayor, Maribella Domenech. In-vitro modeling of Sonic Hedgehog signaling identifies the extracellular matrix as a regulator of tumor invasion outcomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4208.