West Valley High School

This study presents DAISY (Disaster Awareness and Information Serving You), an intelligent chatbot designed to enhance public access to real-time natural disaster information. DAISY integrates Large Language Models (LLMs), GPT-3.5-turbo and Zephyr-7B

Current wavelength division multiplexing (WDM) transmission systems based on silica fibers rely on the single-channel transmission rate and the number of wavelength channels for communication capacity. While single-channel rates have reached Tb/s levels, further enhancement is severely limited by electronic bottlenecks. To boost system capacity, an effective approach is to increase the number of wavelength channels. The low-loss window of silica fibers, spanning 1250-1800 nm with a loss below 0.4 dB, represents a substantial untapped resource. To fully utilize the full-band bandwidth of silica fibers, it is imperative to develop a full-band fiber amplifier covering this wavelength range. This paper proposes bismuth-doped multicomponent fiber concatenation and parallel connection schemes, establishes energy level structures, rate equations, and power propagation equations for pump, signal, and amplified spontaneous emission light in bismuth-doped silica and germano-silica matrices, calculates the full-band gain spectrum, and optimizes its flatness using a genetic algorithm. Results show that the gain magnitude and flatness of the two amplifier structures depend on pump wavelength, pump power, fiber length, and doping concentration, with the parallel structure offering greater flexibility in controlling the full-band gain spectrum.

Abstract In a complex dynamical system, noise, feedback, and external forces shape behavior that can range from regularity to high-dimensional chaos. Multiple feedback sources can significantly alter its dynamics, potentially even suppressing the system's output. This study investigates the impact of competing feedback sources on a stochastic complex dynamical system using a photonic neuron—a diode laser with external optical feedback. By varying the feedback intensities from two external reflectors, we explore how dual feedback influences the system's behavior. Using ordinal analysis and advanced measures of complexity, we quantify the system's dynamics and uncover underlying symmetries. Our findings reveal that the interaction between the two feedback sources induces a more intense deterministic behavior, distinct from the dynamics produced by each feedback source individually. Additionally, clear temporal symmetries emerge across all dynamical regimes. By employing a novel entropy-vector representation, we are able to identify a unique signature that characterizes the system's dynamics.

Abstract Understanding the phenology of migration is fundamental to management of migratory gamebirds, in part because of the role migratory timing plays in setting harvest regulations. Migratory timing is particularly important for determining appropriate dates for hunting seasons, which may be selected to coincide with major periods of migration, according to local management objectives. We used global positioning system (GPS)‐transmitters to track American woodcock (Scolopax minor), characterize the timing of woodcock migration, and identify sources of variation in timing relative to current hunting season structures in eastern North America. We captured 304 woodcock in 3 Canadian provinces and 12 states from 2017 to 2020, primarily within the Eastern Woodcock Management Region. Using locations collected every 1.7 days on average, we assessed whether initiation, termination, or stopover timing of woodcock migration during fall and early spring varied geographically, differed among age and sex classes, or was influenced by individual body condition. During fall, woodcock migrating from summer use areas farther north and west (e.g., Ontario, Quebec, Canada) initiated and terminated migration earlier than woodcock migrating from areas farther south and east (e.g., Rhode Island, USA). Adult woodcock made multiday stopovers that were 3 days longer on average than juveniles and females made more stopovers on average (8.0 stopovers) compared to males (6.1 stopovers). During the onset of spring migration, woodcock that wintered farther west initiated migration before birds that spent the winter farther east, and males initiated migration on average 6 days earlier than females. Under the current 45‐day harvest regulatory framework in the United States, hunting seasons in northern breeding and southern wintering areas are generally consistent with migration phenology. At more intermediate latitudes, however, periods of migration are generally longer than 45 days, resulting in many circumstances where migrating woodcock are present during periods when hunting seasons are closed. Managers in mid‐latitude states could consider opening hunting seasons later, allowing hunters to harvest more migrant woodcock.

Emerging evidence has revealed that dysregulation of the hormone ghrelin and its receptor, growth hormone secretagogue receptor (GHSR), contributes to the pathogenesis of Alzheimer's disease (AD). Specifically, defective GHSR function and resultant hippocampal ghrelin resistance are linked to hippocampal synaptic injury in AD paradigms. Also, AD patients exhibit elevated ghrelin activation. However, the detailed molecular mechanisms of hippocampal GHSR dysfunction and the relevance of ghrelin elevation to hippocampal ghrelin resistance in AD-relevant pathological settings are not fully understood. In the current study, we employed a recently established mouse line of AD risk [humanized amyloid beta knockin (hAβ KI mice), also referred to as a mouse model of late-onset AD in previous literature] to further define the role of ghrelin system dysregulation in the development of AD. We employed multidisciplinary techniques to determine the change of plasma ghrelin and the functional status of GHSR in hAβ KI mice as well as primary neuron cultures. We observed concurrent plasma ghrelin elevation and hippocampal GHSR desensitization with disease progression. Further examination excluded the possibility that ghrelin elevation is a compensatory change in response to GHSR dysfunction. In contrast, further in vitro and in vivo results show that agonist-mediated overstimulation potentiates GHSR desensitization through enhanced GHSR internalization. These findings suggest that circulating ghrelin elevation is a pathological event underlying hippocampal GHSR dysfunction, culminating in hippocampal ghrelin resistance and resultant synaptic injury in late-onset AD-related settings.

Phantom Limb Pain (PLP), an inappropriate sensation in a missing limb, is a post-amputation phenomenon that occurs in up to 80% of amputees. The specific sensations vary from person to person, but some common reported sensations include warmth or coldness, itching, tingling, and electric shock. Some patients also perceive a specific position or movement of the phantom limb. Most amputees experience PLP with varying degrees of intensity, frequency, and duration. About 5-10% of amputees continue to have severe PLP for many years after amputation. As technology and scientific understanding have evolved, phantom limb pain has become better understood over the centuries, from its first mention from Ambroise Pare in the 1600s to the usage of a variety of medication, medical therapy, and surgery options available today. Although the field of medicine does not yet have a curative treatment for PLP, treatment modalities have advanced from the use of wooden pegs to the latest and advanced prosthetics. By summarizing the growing understanding and evolution of treatment modalities for the medical condition from its first mention in the 16th century to the current 21st century, we can begin to appreciate the years of study and collaboration to current knowledge of phantom limb pain.

Inducible clustered, regularly interspaced, short, palindromic repeats with the CRISPR-associated protein 9 (CRISPR/Cas9) system are a legitimate avenue for treating Parkinson’s and Huntington’s diseases. The CRISPR/Cas9 system, introduced in 2012, is a breakthrough gene-editing mechanism that can be used to modify genomes. The CRISPR/Cas9 system has been used to treat neurodegenerative diseases such as Parkinson’s and Huntington’s. Until now, a majority of CRISPR/Cas9 approaches have been geared towards treating the symptoms of these diseases rather than the causes themselves. Drug inducible CRISPR/Cas9 systems provide more avenues for novel treatments of Parkinson's and Huntington’s diseases that target the genetic causes of these diseases rather than the symptoms. Although there are many limitations to CRISPR, such as delivery methods and target specificity, many improvements are being implemented to increase the efficiency and efficacy of the CRISPR/Cas9 system.

This paper explores the temporal hierarchy of API sequence calls using Deep Recurrent Neural Networks. RNN’s have the capability to easily capture the nature of time series processing due to their sequential structure, whereas generic neural networks cannot find an underlying relationship between data that is separated by timesteps. The primary goal of the paper is to describe an approach to analyzing malware attacks on networks while accounting for each timestep of data and gaining more flexibility in the size of the detection request using the features of the Long Short-Term Memory (LSTM) architecture. We will also touch on the more recent Gated Recurrent Units (GRU) network architecture. We will discuss the tested network configurations and the most optimal model. Lastly, the paper will suggest some applications of this model and how it could be integrated into standard operating systems.

Intimate partner violence (IPV) is becoming a global issue and gaining increasing attention for its detrimental consequences.This paper combines research related to intimate partner violence and depression to systematically analyze their associations.We examined physical, psychological, sexual, and economic abuse in the intimate relationships, and found that physical abuse (and sexual abuse) may induce fear, jeopardize senses of safety or disrupt one's self-identity, leading to depression; psychological abuse demonstrates stronger predictive power on victim's depression and shows more prevailing effect in the long-term; economic abuse happens when the victim's financial freedom is being limited, which increases insecurity and forced reliance on the abuser.Prevention and intervention methods are discussed to determine their effectiveness against IPV causing depressive symptoms.This study contributed a systemic review focusing on IPV, particularly its effect on depression, to the extant literature, and identified further research gaps in long-term effective intervention methods.

ABSTRACT Acute kidney injury (AKI) is a sudden onset of kidney damage. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. In this research, a novel hybrid deep learning architecture for AKI prediction was created using de-identified numeric patient data and ultrasound kidney images. Using data features including serum creatinine among others, two numeric models using MIMIC-III and paired hospital data were developed, and with the hospital ultrasounds, an image-only model was developed. Convolutional neural networks (CNN) were used, including VGG and Resnet, and they were made into a hybrid model by concatenating feature maps of both types of models to create a new input. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, the first time an AKI machine learning model surpassed an AUROC of 0.9. The model also achieved an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU. The implementation of this research has great potential to be applied to different medical predictive applications.