Search Results (1,832)

This paper presents a comprehensive statistical analysis of extremely/very low-frequency (ELF/VLF) whistler-mode waves observed within magnetic ducts (B-ducts) using data from NASA’s Magnetospheric Multiscale (MMS) mission. A total of 687 events were analyzed, comprising 504 occurrences on the dawn-side flank of the magnetosphere and 183 in the nightside magnetotail, to investigate the spatial distribution and underlying mechanisms of wave–particle interactions. We identify distinct differences between these regions by examining key parameters such as event width, frequency, plasma density, and magnetic field extrema within B-ducts. Using an independent two-sample t-test, we assess the statistical significance of variations in these parameters between different observation periods. This study provides valuable insights into the magnetospheric conditions influencing B-duct formation and wave propagation, offering a framework for understanding ELF/VLF wave dynamics in Earth’s space environment. Full article

Many vertebrates have evolved resistance to snake venom as a result of coevolutionary chemical arms races. In Australian skinks (family Scincidae), who often encounter venomous elapid snakes, the frequency, diversity, and molecular basis of venom resistance have been unexplored. This study investigated the evolution of neurotoxin resistance in Australian skinks, focusing on mutations in the muscle nicotinic acetylcholine receptor (nAChR) α1 subunit’s orthosteric site that prevent pathophysiological binding by α-neurotoxins. We sampled a broad taxonomic range of Australian skinks and sequenced the nAChR α1 subunit gene. Key resistance-conferring mutations at the toxin-binding site (N-glycosylation motifs, proline substitutions, arginine insertions, changes in the electrochemical state of the receptor, and novel cysteines) were identified and mapped onto the skink organismal phylogeny. Comparisons with other venom-resistant taxa (amphibians, mammals, and reptiles) were performed, and structural modelling and binding assays were used to evaluate the impact of these mutations. Multiple independent origins of α-neurotoxin resistance were found across diverse skink lineages. Thirteen lineages evolved at least one resistance motif and twelve additional motifs evolved within these lineages, for a total of twenty-five times of α-neurotoxic venoms resistance. These changes sterically or electrostatically inhibit neurotoxin binding. Convergent mutations at the orthosteric site include the introduction of N-linked glycosylation sites previously known from animals as diverse as cobras and mongooses. However, an arginine (R) substitution at position 187 was also shown to have evolved on multiple occasions in Australian skinks, a modification previously shown to be responsible for the Honey Badger’s iconic resistance to cobra venom. Functional testing confirmed this mode of resistance in skinks. Our findings reveal that venom resistance has evolved extensively and convergently in Australian skinks through repeated molecular adaptations of the nAChR in response to the enormous selection pressure exerted by elapid snakes subsequent to their arrival and continent-wide dispersal in Australia. These toxicological findings highlight a remarkable example of convergent evolution across vertebrates and provide insight into the adaptive significance of toxin resistance in snake–lizard ecological interactions. Full article

Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed. Full article

Taconis oscillations represent spontaneous excitation of acoustic modes in tubes with large temperature gradients in cryogenic systems. In this study, Taconis oscillations in hydrogen and helium systems are enhanced with a porous material resulting in a standing-wave thermoacoustic engine. A theoretical model is developed using the thermoacoustic software DeltaEC, version v6.4b2.7, to predict system performance, and an experimental apparatus is constructed for engine characterization. The low-amplitude thermoacoustic model predicts the pressure amplitude, frequency, and temperature gradient required for excitation of the standing-wave system. Experimental measurements, including the onset temperature ratio, acoustic pressure amplitudes, and frequencies, are recorded for different stack materials and geometries. The findings indicate that, independent of stack, hydrogen systems excite at smaller temperature differentials than helium (because of different properties such as lower viscosity for hydrogen), and the stack geometry and material affect the onset temperature ratio. However, pressure amplitude in the excited states varies minimally. Initial measurements are also conducted in a cooling setup with an added regenerator. The configuration with stainless-steel mesh screens produces a small cryogenic refrigeration effect with a decrease in temperature of about 1 K. The reported characterization of a Taconis-based thermoacoustic engine can be useful for the development of novel thermal management systems for cryogenic storage vessels, including refrigeration and pressurization. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

Metasurfaces have drawn extensive research attention for their unique optical properties and vast application potential. Among the various resonant modes induced in metasurfaces, BIC and electric anapole modes stand out as particularly interesting due to their distinctive physical characteristics. In this work, we designed and investigated novel dimeric dumbbell-shaped metasurfaces incorporating two independently tunable asymmetric parameters. This structural innovation enables the simultaneous excitation of both electric anapole and QBIC modes under normally incident MIR illumination. More importantly, by adjusting these two asymmetric parameters, one can independently tune the resonance peaks of the two modes, thereby overcoming the performance limits of conventional single-peak modulation. This metasurface design demonstrates outstanding performance for dielectric environment-sensing applications. We conducted a comprehensive investigation of the sensing sensitivity for dumbbell-shaped metasurfaces of various geometries. Our simulation results show that the circular-shaped configuration achieved high sensitivity, reaching 20,930 GHz/RIU. This work offers a novel design paradigm for multi-mode control and functionalization of metasurface structures. Full article

Magnetotelluric (MT) sounding is a crucial technique in mineral exploration. However, MT data are highly susceptible to various types of noise. Traditional data processing methods, which rely on the assumption of signal stationarity, often result in severe distortion when suppressing non-stationary noise. In this study, we propose a novel, adaptive, and less parameter-dependent signal decomposition method for MT signal denoising, based on time–frequency domain analysis and the application of modal decomposition. The method uses Variational Mode Decomposition (VMD) to adaptively decompose the MT signal into several intrinsic mode functions (IMFs), obtaining the instantaneous time–frequency energy distribution of the signal. Subsequently, robust statistical methods are introduced to extract the independent components of each IMF, thereby identifying signal and noise components within the decomposition results. Synthetic data experiments show that our method accurately separates high-amplitude non-stationary interference. Furthermore, it maintains stable decomposition results under various parameter settings, exhibiting strong robustness and low parameter dependency. When applied to field MT data, the method effectively filters out non-stationary noise, leading to significant improvements in both apparent resistivity and phase curves, indicating its practical value in mineral exploration. Full article

Background: Understanding the origin and natural organization of early infant vocalizations is important for predicting communication and language abilities in later years. The very frequent production of speech-like vocalizations (hereafter “protophones”), occurring largely independently of interaction, is part of this developmental process. Objectives: This study aims to investigate the gap durations (time intervals) between protophones, comparing typically developing (TD) infants and infants later diagnosed with autism spectrum disorder (ASD) in a naturalistic setting where endogenous protophones occur frequently. Additionally, we explore potential age-related variations and sex differences in gap durations. Methods: We analyzed ~1500 five min recording segments from longitudinal all-day home recordings of 147 infants (103 TD infants and 44 autistic infants) during their first year of life. The data included over 90,000 infant protophones. Human coding was employed to ensure maximally accurate timing data. This method included the human judgment of gap durations specified based on time-domain and spectrographic displays. Results and Conclusions: Short gap durations occurred between protophones produced by infants, with a mode between 301 and 400 ms, roughly the length of an infant syllable, across all diagnoses, sex, and age groups. However, we found significant differences in the gap duration distributions between ASD and TD groups when infant-directed speech (IDS) was relatively frequent, as well as across age groups and sexes. The Generalized Linear Modeling (GLM) results confirmed these findings and revealed longer gap durations associated with higher IDS, female sex, older age, and TD diagnosis. Age-related differences and sex differences were highly significant for both diagnosis groups. Full article

This paper presents the design, simulation, and experimental validation of a dual-Circularly Polarized (CP) array antenna to be used as single element for a bistatic radar system, aimed at detecting and tracking objects in Low Earth Orbit (LEO). The antenna operates at 412 MHz in reception mode and consists of an array of 19 slotted-patch radiating elements with a cavity-based metallic superstrate, designed to support dual circular polarization. These elements are arranged in a hexagonal configuration, enabling the array structure to achieve a maximum realized gain of 17 dBi and a Side Lobe Level (SLL) below −17 dB while maintaining high polarization purity. Two identical analog feeding networks enable the precise control of phase and amplitude, allowing the independent reception of Right-Hand and Left-Hand Circularly Polarized (RHCP and LHCP) signals. Full-wave simulations and experimental measurements confirm the high performance and robustness of the system, demonstrating its suitability for integration into large-scale Space Situational Awareness (SSA) sensor networks. Full article

Secure autonomous secret key distillation (SKD) systems traditionally depend on external common randomness (CR) sources, which often suffer from instability and limited reliability over long-term operation. In this work, we propose a novel SKD architecture that synthesizes CR by combining a keystream of a shared-key keystream generator $KSG\left(K_G\right)$ with locally generated binary Bernoulli noise. This construction emulates the statistical properties of the classical Maurer satellite scenario while enabling deterministic control over key parameters such as bit error rate, entropy, and leakage rate (LR). We derive a closed-form lower bound on the equivocation of the shared-secret key $K_G$ from the viewpoint of an adversary with access to public reconciliation data. This allows us to define an admissible operational region in which the system guarantees long-term secrecy through periodic key refreshes, without relying on advantage distillation. We integrate the Winnow protocol as the information reconciliation mechanism, optimized for short block lengths ($N=8$), and analyze its performance in terms of efficiency, LR, and final key disagreement rate (KDR). The proposed system operates in two modes: ideal secrecy, achieving secret key rates up to 22% under stringent constraints (KDR < 10⁻⁵, LR < 10⁻¹⁰), and perfect secrecy mode, which approximately halves the key rate. Notably, these security guarantees are achieved autonomously, without reliance on advantage distillation or external CR sources. Theoretical findings are further supported by experimental verification demonstrating the practical viability of the proposed system under realistic conditions. This study introduces, for the first time, an autonomous CR-based SKD system with provable security performance independent of communication channels or external randomness, thus enhancing the practical viability of secure key distribution schemes. Full article

The development of scalable, non-invasive tools to assess tumor responsiveness to structurally active immunoformulations remains a critical unmet need in solid tumor immunotherapy. Here, we introduce a real-time, ex vivo functional system to classify tumor cell lines exposed to a phospholipoproteomic platform, without relying on cytotoxicity, co-culture systems, or molecular profiling. Tumor cells were monitored using IncuCyte^® S3 (Sartorius) real-time imaging under ex vivo neutral conditions. No dendritic cell components or immune co-cultures were used in this mode. All results are derived from direct tumor cell responses to structurally active formulations. Using eight human tumor lines, we captured proliferative behavior, cell death rates, and secretomic profiles to assign each case into stimulatory, inhibitory, or neutral categories. A structured decision-tree logic supported the classification, and a Functional Stratification Index (FSI) was computed to quantify the response magnitude. Inhibitory lines showed early divergence and high IFN-γ/IL-10 ratios; stimulatory ones exhibited a proliferative gain under balanced immune signaling. The results were reproducible across independent batches. This system enables quantitative phenotypic screening under standardized, marker-free conditions and offers an adaptable platform for functional evaluation in immuno-oncology pipelines where traditional cytotoxic endpoints are insufficient. This approach has been codified into the STIP (Structured Traceability and Immunophenotypic Platform), supporting reproducible documentation across tumor models. This platform contributes to upstream validation logic in immuno-oncology workflows and supports early-stage regulatory documentation. Full article

Background/Objectives: Digital food-related videos significantly influence cravings, appetite, and weight outcomes; however, the dynamic neural mechanisms underlying appetite fluctuations during naturalistic viewing remain unclear. This study aimed to identify neural activity patterns associated with moment-to-moment appetite changes during naturalistic food-cue video viewing and to examine their relationships with cravings and weight-related outcomes. Methods: Functional magnetic resonance imaging (fMRI) data were collected from 58 healthy female participants as they viewed naturalistic food-cue videos. Participants concurrently provided continuous ratings of their appetite levels throughout video viewing. Hidden Markov Modeling (HMM), combined with machine learning regression techniques, was employed to identify distinct neural states reflecting dynamic appetite fluctuations. Findings were independently validated using a shorter-duration food-cue video viewing task. Results: Distinct neural states characterized by heightened activation in default mode and frontoparietal networks consistently corresponded with increases in appetite ratings. Importantly, the higher expression of these appetite-related neural states correlated positively with participants’ Body Mass Index (BMI) and post-viewing food cravings. Furthermore, these neural states mediated the relationship between BMI and food craving levels. Longitudinal analyses revealed that the expression levels of appetite-related neural states predicted participants’ BMI trajectories over a subsequent six-month period. Participants experiencing BMI increases exhibited a significantly greater expression of these neural states compared to those whose BMI remained stable. Conclusions: Our findings elucidate how digital food cues dynamically modulate neural processes associated with appetite. These neural markers may serve as early indicators of obesity risk, offering valuable insights into the psychological and neurobiological mechanisms linking everyday media exposure to food cravings and weight management. Full article

The research demonstrates, through simulation and laboratory validation, the development of a low-voltage DC-link (LVDC) back-to-back converter system that enables multi-frequency power transfer. The system operates in two distinct modes, which include a three-phase grid-connected converter transferring fundamental and 5th and 7th harmonic power to a three-phase residential inverter supplying a clean 50 Hz load and another mode that uses a DC–DC buck–boost converter to integrate a battery storage unit for single-phase load supply. The system allows independent control of each harmonic component and maintains a clean sinusoidal voltage at the load side through DC-link isolation. The LVDC link functions as a frequency-selective barrier to suppress non-standard harmonic signals on the load side, effectively isolating the multi-frequency power grid from standard-frequency household loads. The proposed solution fills the gap between the multi-frequency power systems and the single-frequency loads because it allows the transfer of total multi-frequency grid power to the traditional household loads with pure fundamental frequency. Experimental results and simulation outcomes demonstrate that the system achieves high efficiency, robust harmonic isolation, and dynamic adaptability when load conditions change. Full article

Background/Objectives: “Ozempic face” is an aesthetic side effect associated with the use of the antidiabetic agent Ozempic (semaglutide), characterized by a prematurely aged and fatigued facial appearance due to rapid weight loss. Currently, treatment options for this condition are limited. In this study, we present our clinical experience with the BodyTite device, provided by InMode Italy S.r.l. (Rome, Italy). Materials and Methods: We report a case series involving 24 patients (19 women and 5 men, aged 27–65 years), treated with subdermal bipolar radiofrequency (Endotissutal Bipolar Radiofrequency) between 2023 and 2024. All patients underwent a minimum follow-up of 12 months. At the end of the follow-up period, patients rated their satisfaction on a from 0 to 10 scale, and an independent expert assessed the stability of clinical outcomes. Results: The majority of patients reported high satisfaction levels (≥8), which correlated with the independent expert’s evaluation of treatment efficacy and result stability. The only observed adverse event was transient cutaneous erythema. Conclusions: “Ozempic face” is an increasingly common side effect associated with newer classes of antidiabetic medications. Although these drugs offer significant metabolic benefits, the accompanying facial volume loss and aging are often poorly tolerated by patients. Our findings suggest that subdermal bipolar radiofrequency represents a safe, low-risk, and cost-effective therapeutic option for the aesthetic management of Ozempic face. Full article

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article