Sensitive and specific detection of influenza virus A/H3N2: An electrochemical aptasensor modified with AuNPs-PEI-MWCNTs.

Aftereffects of variance in the perception of facial expressions in crowds.

Advancements in artificial intelligence have propelled affective computing toward unprecedented accuracy and real-world impact. By leveraging the unique strengths of brain signals and ocular dynamics, we introduce a novel multimodal framework that integrates EEG and eye-movement (EM) features synergistically to achieve more reliable emotion recognition. First, our EEG Feature Encoder (EFE) uses a convolutional architecture inspired by the human visual cortex's eccentricity-receptive-field mapping, enabling the extraction of highly discriminative neural patterns. Second, our EM Feature Encoder (EMFE) employs a Kolmogorov-Arnold Network (KAN) to overcome the sparse sampling and dimensional mismatch inherent in EM data; through a tailored multilayer design and interpolation alignment, it generates rich, modality-compatible representations. Finally, the core Multimodal Iterative Attentional Feature Fusion (MIAFF) module unites these streams: alternating global and local attention via a Hierarchical Channel Attention Module (HCAM) to iteratively refine and integrate features. Comprehensive evaluations on SEED (3-class) and SEED-IV (4-class) benchmarks show that our method reaches leading-edge accuracy. However, our experiments are limited by small homogeneous datasets, untested cross-cultural robustness, and potential degradation in noisy or edge-deployment settings. Nevertheless, this work not only underscores the power of biomimetic encoding and iterative attention but also paves the way for next-generation brain-computer interface applications in affective health, adaptive gaming, and beyond.

Age-Related Optic Nerve Optical Coherence Tomography and Optical Coherence Tomography Angiography Changes in Subjects Without Glaucoma: A 5-Year Prospective Study.

The intravoxel incoherent motion (IVIM) three-compartment model (3CM) potentially offers better classification for perfusion-rich pathologies. However, 3CM is often associated with less fitting stability than the two-compartment model (2CM). For liver intravoxel IVIM data, when the fit-starting b-value is adjusted from 2 s/mm2 to bi s/mm2, PFi, which indicates the perfusion fraction at bi, gradually decreases as the initial fitting b-value bi increases and eventually approaches zero. We define Yi = ln [(1 - PFi)/PFi]. With a fitted line between Yi and bi, the intercept with the y-axis is the predicted value of Y0 that follows the bi-exponential model. The PF0 calculated from it eliminates the signals of other very rapid perfusion components and retains a more stable perfusion fraction measurement. The slope of the fitted line is (Dfast-Dslow). Dslow can be calculated by the standard IVIM model. Because the Yi fitting assumes that the signal attenuation is bi-exponential, and the portion of the signal strength that exceeds the bi-exponential model at very low b-values is not used, the perfusion fraction detected is the fast decay perfusion component (Ffast) without the very fast decay perfusion component (Fvfast). In our study on healthy livers, Dataset-1 with 3.0 T data had 17 subjects scanned twice with a 10-12 days' interval, and paired scan-rescan had 13 subjects; Dataset-2 with 1.5 T data had 20 subjects scanned twice during the same scan session, and paired scan-rescan had 17 subjects. For the 2CM, IVIM parameters Dslow, Dfast, and PFi were obtained with a nonlinear least square (NLLSQ) fit. Parameters obtained with segmented fitting were used for Yi analysis. Results show that conventional fitting and Yi fitting derived very comparable IVIM parameters in mean value. Dfast obtained with Yi fitting showed better scan-rescan stability than conventional segmented fitting. Perfusion fraction parameters showed more favorable scan-rescan stability with Yi fitting.

The fast-paced utilization of innovative Internet of Things (IoT) applications emphasizes the critical role that routing protocols play in designing an efficient communication system between network nodes. In this context, the lack of adaptive routing mechanisms in the standard Routing Protocol for Low-power and Lossy Networks (RPL), such as load balancing and congestion mechanisms, especially under heavy load scenarios, causes significant degradation of network performance. In this regard, integrating innovative and effective learning abilities, such as Reinforcement Learning, into an efficient routing policy has demonstrated promising solutions for future networks. Hence, this paper introduces Aris-RPL, an adaptive routing policy for the RPL protocol. Aris-RPL utilizes a multi-objective Q-learning algorithm to learn optimal paths. Each node translates neighboring node information into a Q-value representing a composite multi-objective metric, including Buffer Utilization, Energy Level, Received Signal Strength Indicator (RSSI), Overflow Ratio, and Child Count. Furthermore, Aris-RPL operates effectively during the exploitation and exploration phases and continuously monitors the network overflow ratio during exploitation to respond to sudden changes and maintain performance. The extensive Contiki OS 3.0/COOJA simulator experiments have verified Aris-RPL efficiency. It enhanced Control Overhead, Packet Delivery Ratio (PDR), End-to-End Delay (E2E Delay), and Energy Consumption results compared to other counterparts for all scenarios on average by 39%, 25%, 7%, and 38%, respectively.

This study describes the design, development and evaluation of a directional Yagi-Uda antenna transmitter for an amateur FM radio station at the main campus of Cavite State University. The antenna was made of an aluminum rail with a 9.525 mm diameter that includes two directors, a reflector, and a driving element. The Directional Yagi-Uda Antenna Design is the focus of the research goals for the Amateur Radio Station at Cavite State University's Main Campus, which include increasing signal strength, maximizing antenna gain, and reducing interference from unwanted signals and noise sources. These goals are intended to increase signal transmission range by at least 30%. The antenna has an operational frequency range of 88.7 MHz. A measured gain of 7.88 dBi was obtained after simulation using the YagiMAX ver. 3.11 program to assess the design attributes. Following testing at the Department of Computer and Electronics Engineering building, the prototype antenna successfully transmitted signals from the rooftop. Audible sound reception was accomplished throughout the university, with open sections experiencing particularly good reception. This study showcases the effective use of the directional Yagi-Uda transmission antenna and its enhanced signal reception capabilities for the amateur FM radio station at Cavite State University's Main Campus.

Abstract Handover (HO) triggering in fifth-generation (5G) ultra-dense networks (UDNs) is critical for ensuring seamless connectivity as mobile user equipment (UE) transitions between adjacent small cells or base stations. Due to the dense deployment of low-powered cells and high user mobility, UDNs experience frequent handovers, which can lead to service degradation and reduced quality of service (QoS). Efficient and adaptive handover mechanisms are therefore essential for sustaining network performance. This study proposes a hybrid intelligent framework for adaptive handover triggering in hyper-dense 5G scenarios. The framework integrates a modified pelican optimization (MPO) algorithm for efficient user clustering, a hybrid quantum–classical recurrent neural network (QCRNN) for dynamic handover decision-making, and chaos gorilla troops optimization (CGTO) for predictive mobility pattern analysis using historical user data. The QCRNN leverages design constraints including transmission delay, signal-to-interference-plus-noise ratio (SINR), received signal strength (RSS), user motion potential (UMP), and current load conditions (CLC) to determine optimal handover initiation without predefined thresholds, enabling adaptive and context-aware decisions. Simulation results demonstrate that the proposed MPO–QCRNN–CGTO approach significantly outperforms existing methods (RSRP, fuzzy, AHP–TOPSIS–Q, FMCSS, SC-Q). The framework reduces the average number of handovers (NOH) by up to 96%, decreases the probability of ping-pong handovers (PPHO) by up to 87%, and lowers handover failure rates by up to 78%. Furthermore, it improves throughput by up to 287% and reduces network latency by up to 65% across varying user densities and simulation times. These improvements highlight the framework’s ability to minimize unnecessary handovers, prevent service interruptions, and maintain high network efficiency, confirming the effectiveness of the integrated MPO–QCRNN–CGTO approach in enhancing mobility robustness and QoS in 5G UDNs.

Abstract A SimCLR-Enhanced Transformer, referred to as a Contrastive Temporal Transformer (CTT), is proposed for smooth and robust indoor trajectory estimation using visible light positioning (VLP). The framework combines contrastive pretraining to learn stable received
signal strength (RSS) embeddings with a Transformer-based temporal regressor to jointly optimize point-wise localization accuracy and trajectory smoothness. Experiments conducted in a controlled 5 m × 5 m × 3 m synthetic indoor environment modeled using visible light communication (VLC) channel characteristics achieve an RMSE of 0.046 m and a mean absolute error (MAE) of 0.037 m, while reducing trajectory jitter by more than 99% compared to GRU-based baselines. Robustness is evaluated under receiver orientation
perturbations and first-order multipath effects, demonstrating that the proposed method maintains smooth and stable trajectories under non-ideal channel conditions, albeit with increased localization error in the presence of strong multipath. These results indicate that
the CTT framework provides a promising foundation for practical VLP systems, subject to further validation using ray-traced and experimentally measured channels.

Abstract High-resolution spectroscopy provides a unique opportunity to directly probe atmospheric dynamics by resolving Doppler shifts of planetary signals as a function of orbital phase. Using the optical spectrometer the Keck Planet Finder, we carry out a pilot study on high-resolution phase-curve spectra of the ultrahot Jupiter KELT-9 b. We spectrally and temporally resolve its dayside emission from posttransit to preeclipse (orbital phase ϕ = 0.1–0.45). The signal strength and width increase with orbital phase as the dayside rotates into view. The net Doppler shift varies progressively from −13.4 ± 0.6 to −0.4 ± 1.0 km s −1 , the extent of which exceeds its rotation velocity of 6.4 ± 0.1 km s −1 , providing unambiguous evidence of atmospheric winds. We devise a retrieval framework to fit the full time-series spectra, accounting for the variation of the line profiles due to the rotation and winds. We retrieve a supersonic day-to-night wind speed up to 11.7 ± 0.6 km s −1 on the emerging dayside, representing the most extreme atmospheric winds in hot Jupiters to date. Comparison to 3D circulation models reveals weak atmospheric drag, consistent with relatively efficient heat recirculation, as also supported by space-based phase-curve measurements. Additionally, we retrieve the dayside chemistry (including Fe i , Fe ii , Ti i , Ti ii , Ca i , Ca ii , Mg i , and Si i ) and temperature structure, and we place constraints on the nightside thermal profile. Our high-resolution phase-curve spectra and the measured supersonic winds provide excellent benchmarks for extreme physics in circulation models, demonstrating the power of this technique in understanding the climates of hot Jupiters.

Spatially offset Raman spectroscopy (SORS) offers non-invasive, molecularly specific access to subsurface tissues, showing strong potential for biomedical diagnostics. However, clinical translation remains limited by the need to balance Raman signal strength with laser safety constraints. This study introduces an open-source, Python-based framework integrating photon transport simulation, probe geometry optimization and photothermal safety modelling within a unified workflow. Monte Carlo photon transport is coupled with Pennes' bioheat and Arrhenius/CEM43 thermal damage models to assess four SORS configurations-conventional puck-point, ring-collector, inverse SORS (iSORS) and a new reinforced iSORS (riSORS)-on a multi-layer skin model. Results show that ring-based illumination markedly reduces thermal loading, extending safe laser exposure times by one to two orders of magnitude relative to point illumination, thus permitting up to 60-100× greater Raman energy accumulation before predicted damage onset. Among tested geometries, riSORS achieved the best trade-off between subsurface selectivity and photon collection efficiency, outperforming conventional designs in both signal yield and safety margin. Sensitivity analyses across optical properties further demonstrate robustness to patient variability. Although simplified assumptions require experimental validation, this framework quantitatively links probe design to safety-limited performance, offering a practical roadmap for clinically viable, thermally safe SORS system design.

Abstract Recent low-frequency array (LOFAR) radio signal detections bearing from the τ Boötis system have been cautiously attributed to auroral emissions from the hot Jupiter τ Boötis Ab. The auroral emissions are believed to be excited by interaction between the exoplanet and the winds of its host star. Since stellar winds respond to stellar surface magnetism, three-dimensional stellar wind modelling, able to account for the star’s contemporaneous magnetic field geometry, can aid the interpretation of radio detections. For the first time, we present spectropolarimetric observations of τ Boötis A from the same epoch as the LOFAR detections. We derive a contemporaneous large-scale magnetic map of τ Boötis A, which shows a poloidally dominated field with mean strength 1.6 G. From our magnetic map, we create a three-dimensional numerical wind model and characterise the wind properties around τ Boötis Ab. To compute the wind power dissipated in τ Boötis Ab’s magnetosphere, we apply two approaches: (A) the solar system-based empirical relation called Bode’s law; and (B) a resolved numerical model of the planetary magnetosphere. When consistently applying best-case assumptions, we predict radio flux densities around 50 mJy and 0.68 mJy respectively. Our values are much too small to be consistent with the reported observation of $890^{+690}_{-500}$ mJy; a stellar surface magnetic field scaling ≳ 10 is required to reproduce the observed signal strength. As τ Boötis A has a rapid magnetic cycle, we speculate that wind variations cased by variation in stellar magnetism may explain the lack of detections from follow-up observations. Our work emphasises the importance of contemporaneous observations of stellar magnetism and observational signatures of star-planet interaction.

Abstract Global Navigation Satellite Systems-Reflectometry (GNSS-R) technique is used to explore the Earth environment by using the Earth surface reflected GNSS signal. The Earth surface reflected GNSS signal can be used to retrieve the Earth surface parameters. The space based GNSS-R, which set receiver on the satellite in space to receive the Earth reflected GNSS signal, is developed from the early 21st century. Triton, a Taiwan designed and manufactured experimental micro-satellite, is one of the satellites for GNSS-R mission and was launched in October 9th, 2023. The mission payload of Triton is Taiwan Space Agency (TASA) self-developed GNSS-R receiver and used to process ocean surface reflected Global Positioning System (GPS) signal. The product of mission payload is delay-Doppler map (DDM) for ocean surface wind speed retrieving. The GNSS-R retrieval system of Triton is developed in Taiwan R/RO process system (TROPS) to retrieve ocean surface wind speed by using DDM. In the retrieval process, the first step is DDM calibration, which is used to remove the influence of payload hardware in the signal strength. Then the calibrated DDM is used to calculate supporting data, such like normalized bistatic radar cross section (NBRCS). After that, the calibrated DDM and supporting data can be used to retrieve ocean surface wind speed. Before retrieving ocean surface wind speed by using GNSS-R function in TROPS, the geophysical model function (GMF) needs to be developed. The ocean surface wind speed product of Triton has been released freely in Taiwan Analysis Center for COSMIC (TACC). In this paper, the detail of retrieval process is introduced. The retrieval ocean surface wind speed is compared with those obtained from European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (EAR5) for validation. Furthermore, some supporting data is also be demonstrated.

This study presents a comprehensive geospatial analysis of GSM network coverage and Wi-Fi security infrastructure across five representative zones of Amman, Jordan's capital city (population 4+ million), examining telecommunications equity and cybersecurity preparedness in urban environments. Using wardriving methodology, we collected data from over 38,000 Wi-Fi networks and 3,000 GSM tower records during April-May 2015, providing critical baseline documentation of Middle Eastern telecommunications infrastructure. Data collection employed three Android devices with operator-specific SIM cards (Zain, Orange, Umniah) using WiGLE WiFi Wardriving and GPSLogger applications. Five zones were selected representing diverse socioeconomic characteristics: Abdali (central business), Sweifieh (affluent residential), Sweileh (mixed-use), Marka (working-class), and Wehdat (dense residential). Results revealed significant spatial heterogeneity in infrastructure distribution (chi-square = 869-1198, p < 0.001, Cramer's V = 0.176-0.212). Zain deployed 304 unique towers, Umniah 250, and Orange 151. Orange demonstrated superior signal strength despite lower tower density. Wi-Fi security showed stark disparities: WPA2 adoption ranged from 59.8% in Sweifieh to 29.3% in Wehdat, while open networks varied from 7.4% to 22.1%. WEP usage persisted at 14.5-20.5% despite known vulnerabilities. These findings document substantial spatial inequalities in telecommunications infrastructure and cybersecurity preparedness characteristic of rapidly developing Middle Eastern urban contexts, with implications for digital equity policy and infrastructure planning across the region. Keywords: Wardriving; Geospatial Analysis; GSM Networks; Wi-Fi Security; GEOINT; Telecommunications Infrastructure; Urban Informatics; Digital Divide; Spatial Inequality; Middle East.

The growing need to foster inclusive education in university environments has driven the development of technological solutions aimed at improving the academic experiences of students with disabilities. These individuals often face barriers to autonomy and participation, especially on large and complex campuses. This article presents the performance evaluation of a LoRaWAN network specifically designed for monitoring people with disabilities on a university campus. The system aims to provide equitable access to campus resources and real-time support to students with disabilities. Leveraging the advantages of Low-Power Wide-Area Networks (LPWAN), particularly LoRaWAN, the proposed system enables real-time tracking with broad coverage and minimal power consumption, without requiring any active user interaction. Each student receives a wearable LoRa-enabled device that wirelessly communicates with a network of gateways strategically installed throughout the campus. To evaluate the system’s performance, this work conducts link-level experiments focusing on the communication between the LoRa end devices (nodes) and the central gateway. The analysis focuses on the network coverage, signal strength (RSSI), signal-to-noise ratio (SNR), and packet reception rate (PRR). The experimental results confirmed that the proposed system is technically robust and operationally effective under real campus conditions. Beyond its technical contributions, the proposed solution represents a concrete step toward building safer and more accessible academic environments that reinforce the autonomy and inclusion of students with disabilities.

A device-free human detection system based on a received signal strength indicator (RSSI) monitors and analyzes the change of RSSI signals to detect human movements in a wireless network. This study proposes and implements a real-time, device-free human detection system based on an RSSI distribution-based detection method with an autonomous threshold. The novelty and contribution of our solution is that the RSSI distribution concept is considered and used to calculate the optimal threshold setting for human detection, while thresholds can be automatically determined from RSSI data streams gathered from test environments. The proposed system can efficiently work without requiring an offline phase, as introduced in many existing works in the research literature. Experiments using 2.4 GHz IEEE 802.15.4 technology have been carried out in indoor environments in two laboratory rooms with different numbers of wireless links, human movement patterns, and movement speeds. Experimental results show that, in all test scenarios, the proposed method can monitor and detect human movement in a wireless network in real time. It outperforms a comparative method and achieves high accuracy (i.e., 100% detection accuracy) with a low computational complexity requirement.

Micro-motors are widely used in industrial applications, which require effective fault diagnosis to maintain safe equipment operation. However, fault signals from micro-motors often exhibit weak signal strength and ambiguous features. To address these challenges, this study proposes a novel fault diagnosis method. Initially, the Jump plus AM-FM Mode Decomposition (JMD) technique was utilized to decompose the measured signals into amplitude-modulated–frequency-modulated (AM-FM) oscillation components and discontinuous (jump) components. The proposed process extracts valuable fault features and integrates them into a new time-domain signal, while also suppressing modal aliasing. Subsequently, a novel Global Relationship Matrix (GRM) is employed to transform one-dimensional signals into two-dimensional images, thereby enhancing the representation of fault features. These images are then input into an Optimized Convolutional Neural Network (OCNN) with an AdamW optimizer, which effectively reduces overfitting during training. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy rate of 99.0476% for multiple fault types, outperforming four comparative methods. This approach offers a reliable solution for quality inspection of micro-motors in a manufacturing environment.

Timely detection of viral infections, particularly in settings outside centralized laboratories, is essential for controlling outbrakes. Small electrochemical biosensors are appealing because they are fast, require microliter sample volumes, and can be integrated into portable devices. However, shrinking the sensor area often weakens the signal, making it difficult to detect low viral loads. We previously showed that combining redox cycling with controlled droplet evaporation can boost the signal. Yet, this method faces signal variability due to user-dependent droplet placement. Here, we introduce a selective-wettability, evaporation-enhanced redox cycling (SW-E2RC) device that passively centers and pins the droplet on the sensing area, improving both signal strength and reproducibility. The chip combines a wettable sensing zone surrounded by water-repellent micropillars that guide the droplet into place and stabilize it during evaporation, concentrating redox-active species over the electrodes. Using SARS-CoV-2 and avian influenza H5N1 as model pathogens, we show that SW-E2RC reduces the LOD to 9.2 × 103 copies/mL, corresponding to a ∼103-fold improvement in sensitivity compared with E2RC. This platform can be adapted to various capture probes and targets, enabling more sensitive and reliable point-of-need viral diagnostics.

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R2 values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates.

Antibiotics are widely used in the management of bacterial infections However; most antibiotics are not known for DRESS. Our objective is to find out the association of DRESS with available antibiotics using disproportionality analysis. Retrospective pharmacovigilance disproportionality analysis based on the FDA Adverse Event Reporting System (FAERS) database from a period of 2004 Q1- 2022 Q3 was conducted using OpenVigil 2.1 tool. Disproportionality measures like Proportional reporting Ratio with associated Chi- square values (PRR ≥ 2 with associated χ2 ≥ 4), ROR with a 95% confidence interval (lower limit of 95% C.I. of ROR is greater than 1), and the number of cases of co-occurrence (n) were used for the identification of novel signals. A total of 13,918 cases of DRESS were reported, out of which 5,455 cases were found with various classes of antibiotics. The signal of DRESS was identified with a total of 40 antibiotics. Sub groups analysis results have shown variation in the strength of signal based on gender, age groups and geographical locations. The sensitivity analysis results have shown a decrease in the strength of signal after removal of cases of concomitant drugs. 22 antibiotics were identified which can be associated with DRESS; however, further causality assessment is required to confirm the association.