San Jose State University

As unmanned aerial vehicles (UAVs) are employed for many applications, UAV identification becomes critical to air traffic control nowadays. Conventional radio-frequency (RF) based UAV identification schemes can correctly recognize a UAV according to the acquired sufficient training RF signal data from a pre-specified training candidate set. However, they may often fail when the model of a test UAV is not included in such a training candidate set and/or the training data are quite limited. To address the aforementioned practical challenges, a novel RF-based open-set few-shot UAV recognition technique is introduced in this work. In our proposed new approach, an RF signal of interest, such as a UAV control signal and a digital data-transmission (DDT) signal, is first sensed and segmented in the UAV frequency band using the corresponding short-time energy-to-spectral entropy ratio (ST-ESER). Then, the cyclic spectrum analysis is applied to the selected RF signal segments to construct the corresponding polyspectra, which are further utilized to establish the cyclic-paw-print (CPP) tensors. Moreover, we design a novel deep-learning (DL) network, namely transformer-attention squeeze-excitation network (TASE-Net), by fusing the transformer-enhanced squeeze-and-excitation (SE) model and the Gaussian mixture model (GMM) into the residual network. The TASE-Net can excel in global feature modeling and unknown class detection simultaneously especially for the open-set and few-shot scenarios. Finally, the constructed CPPs are adopted as the input features of our proposed new TASE-Net to recognize the RF signals (the UAV models). Monte Carlo simulation results demonstrate that our proposed new UAV recognition approach using the TASE-Net can greatly outperform other existing deep-learning methods for open-set few-shot UAV identification.

A multispecies diffuse interface model is formulated in a fluctuating hydrodynamics framework for the purpose of simulating surfactant interfaces at the nanoscale. The model generalizes previous work to ternary mixtures, employing a Cahn–Hilliard free energy density combined with incompressible, isothermal fluctuating hydrodynamics where dissipative fluxes include both deterministic and stochastic terms. The intermolecular parameters in the free energy are chosen such that one species acts as a partially miscible surfactant. From Laplace pressure measurements, we show that in this model the surface tension decreases linearly with surfactant concentration, leading to Marangoni convection for interfaces with concentration gradients. In the capillary wave spectrum for interfaces with and without surfactant, we find that for the former, the spectrum deviates significantly from classical capillary wave theory, presumably due to Gibbs elasticity. In non-equilibrium simulations of the Rayleigh–Plateau instability, deterministic simulations showed that the surfactant delays pinching of a fluid cylinder into droplets. However, stochastic simulations indicate that thermal fluctuations disrupt the surfactant’s stabilizing effect. Similarly, the spreading of a patch of surfactant, driven by Marangoni convection, was found to be partially suppressed by thermal fluctuations.

We assess the relationship between geographic availability of different types of alcohol (i.e., bars, restaurants, and off-premise alcohol outlets) and child maltreatment within a residential neighborhood and in surrounding neighborhood areas over a 15-year period. A better understanding of how specific types of alcohol outlets are associated with child maltreatment, over a longer time-period, could help inform more targeted interventions for specific neighborhoods that differ by the alcohol outlet environment. We conducted an ecological longitudinal study from 2001 to 2015 in Sacramento County, California using 912 Census block groups over a 15-year period. Child abuse and neglect was measured by substantiations, entries into foster care, and alcohol-related entries into foster care. Alcohol outlet data were obtain from the California Alcoholic Beverage Control by type of establishment (bar, restaurant, off-premise alcohol outlet). Data were analyzed using Bayesian conditionally autoregressive space-time models. Bars were positively related to all outcomes in local areas and in adjacent areas for substantiations. Restaurants were related to substantiations and foster care entries in local areas. Off premise outlets were related to foster care entries in adjacent areas and both total and alcohol-related foster care entries in adjacent areas. The type of outlet differed across outcomes. The increase in individuals drinking alcohol at less expensive eateries might be a reason for the current findings. Availability theory would suggest that increased physical availability of alcohol leads to more alcohol which leads to more negative child welfare outcomes.

Children learn language through interactions with others. To document variation in how caregivers engage verbally with their 2‐year‐old children, we sampled six 10‐min segments of dense child‐directed speech from naturalistic daylong audio recordings in an economically‐ and linguistically‐diverse sample of English‐ ( n = 45) and Spanish‐speaking ( n = 45) families. Segments of child‐directed speech occurred during child‐centered (e.g., booksharing, play) and adult‐centered (e.g., cooking) activities, with substantial variation among families. During all activity types, child‐directed speech was associated with one or more linguistic or interactive features that have been shown to facilitate language development, such as lexical diversity, mean length of utterance (MLU), or responsiveness to children. Moderate to strong stability within families was found, suggesting that caregivers in some families engaged with children more than in others, regardless of the activities in which they participated. Patterns were generally similar across language groups. This study extends previous research by using naturalistic daylong recordings to explore how activity type and caregivers' individual tendencies relate to children's early language experiences.

In his 1975 novel Ecotopia, Ernest Callenbach imagined a near future in which a large section of the American west coast, including the states of Washington, Oregon and the northern part of California, had seceded from the United States in order to build a new society, entirely based on principles of ecological stability and long-term human survival. Callenbach’s fictional vision, in many ways, extended the logic and forms of radical urban design experiments that had already been proceeding in Berkeley and other parts of Northern California. What Callenbach observed in the early 1970s were various attempts to generate, sometimes through direct action, an alternative ecological pattern of living that came to be thought of as ‘ecotopian’ but which, I would argue both preceded and extended beyond and after the particular novel of that name. Investigating these reciprocal and intertwining histories of ecotopian urbanism, this paper crosses back and forth between literary imagination and architectural place-making.

The art of music is profound and includes the fine-tuning of multiple fine-grained aspects. Taking the example of one such attribute, the music tonic, this paper investigates if machine learning can be effectively applied to detect fine-grained features from various representations of music. It describes the methodology and experiments to determine the effectiveness of the audio representations and non-linear machine learning classifiers such as Support Vector Machines, K-Nearest Neighbors, and tree-based methods in capturing and detecting tonic informa-tion. The effectiveness of Mel-frequency cepstral coefficients is compared with Bark-frequency cepstral coefficients and Mel spectrogram data, which are other audio representations. The feature extraction of the Mel-frequency cepstral coef-ficients is performed using two different tools for comparison. A spectral analysis of the music renditions in a chosen dataset using Principal Component Analysis, and other dimensionality reduction techniques, t-distributed stochastic neighbor embedding, auto-encoder, and Uniform Manifold Approximation and Projection provided valuable insights into the representation learning of music in general. Classification using fine-tuned machine learning models demonstrated the fea-sibility of automating the detection and possible prediction of the music tonic. The work also resulted in a dataset with vocal renditions of the second and third authors of this paper that was used for some of the experiments.

Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract. As a result, generating high quality synthetic samples from scarce data sets becomes a crucial method for strengthening detection software. This paper focuses on presenting generation techniques that optimize the embedding space to produce high-quality synthetic samples, even under constrained datasets. The dataset used in this paper consists of 500 Windows malware API call samples that were processed using embedding and Generative AI (Gen AI) techniques to generate synthetic malware. Two novel contributions are highlighted in this paper. (1) The integration of autoencoders with pretrained NLP models (BERT and ELMo) to enhance the quality of embeddings. Autoencoders extract features and learn patterns from the data to generate higher-quality embeddings than those generated using other techniques alone. (2) Cluster-Tangent Diffusion (CT-Diff): a novel application of manifold diffusion. Manifold diffusion improves upon diffusion and other Gen AI techniques by focusing on generating samples along the distribution of the original data using structured noise instead of standard gaussian noise. Collectively these two contributions have consistently outperformed previous techniques. Furthermore, the results demonstrate the feasibility of generating reliable fake samples even in low data scenarios.

In the standard picture of fully developed turbulence, highly intermittent hydrodynamic fields are nonlinearly coupled across scales, where local energy cascades from large scales into dissipative vortices and large density gradients. Microscopically, however, constituent fluid molecules are in constant thermal (Brownian) motion, but the role of molecular fluctuations in large-scale turbulence is largely unknown, and with rare exceptions, it has historically been considered irrelevant at scales larger than the molecular mean free path. Recent theoretical and computational investigations have shown that molecular fluctuations can impact energy cascade at Kolmogorov length scales. Here, we show that molecular fluctuations not only modify energy spectrum at wavelengths larger than the Kolmogorov length in compressible turbulence, but also significantly inhibit spatio-temporal intermittency across the entire dissipation range. Using large-scale direct numerical simulations of computational fluctuating hydrodynamics, we demonstrate that the extreme intermittency characteristic of turbulence models is replaced by nearly Gaussian statistics in the dissipation range. These results demonstrate that the compressible Navier–Stokes equations should be augmented with molecular fluctuations to accurately predict turbulence statistics across the dissipation range. Our findings have significant consequences for turbulence modelling in applications such as astrophysics, reactive flows and hypersonic aerodynamics, where dissipation-range turbulence is approximated by closure models.

The subpolar North Atlantic (SPNA) is one of the few regions where the deep ocean is in direct contact with the atmosphere, making it a key location for interior ocean ventilation through gas exchange. We use a novel observation‐based data product to analyze large‐scale patterns of the air‐sea flux of oxygen, finding a mean annual flux of 48.1 ± $\pm$ 14.6 Tmol year−1 ${\text{year}}^{-1}$ from the atmosphere into the ocean integrated over the SPNA (45° $45{}^{\circ}$N–65° $65{}^{\circ}$N). An analysis of a fully‐closed oxygen budget from the data‐assimilative ECCO‐Darwin ocean biogeochemistry model suggests that the net uptake is counteracted by oxygen removal through ocean circulation and mixing. Over an annual cycle, a SPNA oxygen uptake of 63.6 ± $\pm$ 13.8 Tmol at densities greater than 26.7 kg m−3 ${\mathrm{m}}^{-3}$ drives a wintertime oxygen increase in corresponding mode and deep water layers. 87% of this net annual uptake occurs in the density range of subpolar mode water (SPMW), 26.7 kg m−3≤σθ< ${\mathrm{m}}^{-3}\le \hspace*{.5em}{\sigma }_{\theta }<$ 27.63 kg m−3 ${\mathrm{m}}^{-3}$, in the upper branch of the Atlantic Meridional Overturning Circulation (AMOC). Our results demonstrate that oxygen is injected during mode water formation throughout the subpolar gyre's cyclonic pathway from the North Atlantic Current toward the Labrador Sea. Along this path, SPMW becomes progressively denser and more oxygenated, and is ultimately transformed into Labrador Sea Water which exports the accumulated oxygen to the global ocean in the lower branch of the AMOC.

Typhoidal tularemia is a rare form of Francisella tularensis infection with nonspecific systemic symptoms that pose diagnostic challenges. Microbial cell-free DNA (mcfDNA) sequencing offers promise for detecting difficult-to-diagnose pathogens, including tularemia. An 80-year-old woman presented with fever and acute encephalopathy, with in-hospital evaluation unrevealing of an identifiable source or pathogen. She was discharged with clinical improvement on empiric levofloxacin. Following her hospitalization, mcfDNA sequencing results detected Francisella tularensis, leading to diagnosis of typhoidal tularemia. This case demonstrated mcfDNA’s utility for diagnosing rare zoonotic infections that would likely remain undiagnosed using conventional methods. The authors propose molecular testing should be considered for updated tularemia case definitions for public health surveillance. Clinicians should maintain suspicion for zoonotic diseases in patients with undifferentiated fever in California.

Incarceration is a recognized risk factor for homelessness. However, most research focuses on the relationship between homelessness and prison incarceration. Jail incarceration is more common compared to prison incarceration, but little data exists on its impact on housing. The objective of this study is to examine the occurrence of housing loss after jail incarceration among individuals without prior evidence of homelessness and the associated risk of reincarceration. In this retrospective cross-sectional study, we identified adults without evidence of homelessness who became unhoused within 6 months of jail incarceration. We compare pre-incarceration emergent and urgent health and social services utilization among housed and unhoused individuals, as well as the risk of reincarceration. Data are from the San Francisco (SF) Department of Public Health Coordinated Care Management System linked with SF City and County criminal justice data during fiscal years 2015–2018. We find that a quarter (25.1%) of individuals lost housing after jail incarceration, with a median incarceration length of 4 days in both the housed and unhoused groups. Compared to those without evidence of housing loss, more unhoused individuals had pre-incarceration substance use and mental health diagnoses and related service utilization. Unhoused individuals had 1.9 greater odds of reincarceration. In conclusion, we find that a significant number of individuals had evidence of housing loss after even a short jail incarceration; behavioral health diagnoses were more common among this group. Housing loss was associated with subsequent reincarceration. Given our findings, jail re-entry programs would benefit from incorporating housing assistance and housing loss mitigation strategies.

Older adults are often underdiagnosed and undertreated for substance use disorders due to difficulty recognizing signs of substance use disorders in this population, as well as difficulty applying the DSM criteria to older adults. The current paper focuses on the prevalence of alcohol and cannabis use among older adults, as well as best practices for assessment and intervention. Through the integration of a case presentation, the current paper highlights common signs of substance use among older adults, clinical and ethical challenges in substance use disorder diagnosis and treatment among older adults, appropriate substance use assessments for older adults, and a variety of evidence-based treatment approaches that can be used with this population.

Recent years have witnessed significant growth in corporate sustainability reporting. Yet existing research provides mixed evidence on the information content of these reports for investors. We examine the stock market reaction to the announcement of a sample of US corporate sustainability reports incorporating Sustainability Accounting Standards Board metrics that are intended to provide financially material information to investors. Using standard measures of information content, we cannot find compelling evidence that these reports provide a significant amount of new information to investors. Further analysis of a subset of common metrics indicates that they are either financially immaterial or preempted by traditional financial disclosures. Finally, we show that most firms target their sustainability reports at a broad set of sustainability-oriented stakeholders rather than a narrow set of financially oriented investors.

The bleaching properties of locally acid-activated Anfoega kaolin clays have been studied to investigate their applicability as a substitute for the expensive imported acid-activated bleaching clays used in vegetable oil refinery industries in Ghana. The clay was characterized by X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectrophotometer, and scanning electron microscopy (SEM). The bleaching properties of the clay were investigated by varying the clay dosage, acid concentration, and bleaching temperature. Activation of the Anfoega kaolin clay at 100 °C and 2.5 h with constant stirring was found to be optimum conditions of temperature and contact time, respectively. The clay/acid ratio was found not to significantly affect the clay properties. Palm oil was used to investigate the bleaching performance of the activated clay samples. When the oil was bleached at 90 °C for 30 min using 10% wt/vol of oil, clay activated with 2 mol/L H 2 SO 4 , the bleaching performance obtained was up to 94.54%. Response surface plot methodology revealed that the optimal bleaching conditions were achieved with a clay dosage of 10 g, a temperature range of 70 to 120 °C, and a bleaching duration of 60 min resulting in a bleaching efficiency of 81%.

Community policing is one of the most well-known and influential police reform movements over the past half century. Most of the literature on community policing and its effects has emerged from a handful of large, industrialized democracies. Less is known about the nature and implementation of community policing in other contexts. Here, we explore a community policing initiative called Hearts and Minds that was developed and implemented in Trinidad and Tobago, a small two-island Caribbean nation that has experienced a significant problem with gang and gun violence over the past two decades. Based on data from interviews with Hearts and Minds officers, we examine efforts by Hearts and Minds officers to build trust and improve public perceptions of police legitimacy. We discuss the implications of this research for theory, research, and practice.

Understanding the sequence of embryonic and larval development and the factors necessary to induce reproduction in captivity are critical for developing new species for commercial or conservation aquaculture. In this study, we describe the adult reproductive behaviours and development of eggs, embryos and early larvae of captive monkeyface pricklebacks, Cebidicthys violaceus, compared to previously documented wild observations. Eggs were laid in cohesive clutches and guarded by the male parent until hatching began 23 days post fertilization at 13°C. Fertilized eggs were spherical, approximately 1.5 mm in diameter, covered in an opaque chorion, and contained six adhesive pads around the outside. We characterized the rate of depletion of yolk and the oil globule and growth of the embryo from fertilization until hatching. Notable embryonic stages were documented, including the timing of the first heartbeat, and the development of otoliths, intestinal tract, eye pigmentation, mouth, fins and the circulatory system. Larval length at hatching was about 7.4 mm, and larvae were immediately mobile and feeding on live food. Larvae were cultured and observed up to 18 days post hatch.

Carbon–climate (CC) feedbacks — arising from response of land and ocean carbon sinks to elevated CO2 and climate warming — are critical yet highly uncertain drivers of Earth’s climate trajectory. Processes such as permafrost thaw, tropical forest dieback, reduced ocean uptake, and intensifying climate extremes are already weakening natural carbon sinks, with potential to amplify warming and accelerate crossing of tipping points. Detecting and quantifying these dynamics requires sustained, high-resolution, spatially-comprehensive Earth Observations. Drawing on outcomes from a 2024 community workshop, we identify three core priorities for advancing CC feedback monitoring: (1) frequent, long-term measurements of carbon fluxes and stocks at sub-continental scales; (2) improved flux detectability using greenhouse gas partial columns; and (3) targeted monitoring of poorly-observed vulnerable regions, such as the tropics, high latitudes, and the Southern Ocean. We argue that a unified, multi-platform observing system, built on lower-cost, proven technologies and orbits and focused on high-risk observation gaps, would significantly reduce uncertainties in Earth System Models, provide early warnings of feedbacks and tipping points, inform climate mitigation strategies, and enhance transparency in carbon monitoring.

Highlights What are the main findings? This article presents an image processing method to semi-automatically track wildfire progression. The algorithm was successfully applied to aerial infrared imagery acquired during tactical fire management operations. What are the implications of the main finding? These results illustrate how tactical data can be used in fire behavior studies. The proposed method may facilitate real-time analysis of tactical information during wildfire emergencies. Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts.