Clase Modelo STEM: Construcción de un Puente con Materiales ReciclablesNivel: primaria Duración: 2 a 3 clases de 45 minutos Área STEM involucradas: Ciencia: fuerzas, equilibrio, materiales Tecnología: uso de herramientas simples, diseño Ingeniería: planificación y construcción de estructuras Matemáticas: medidas, proporciones, geometría   Objetivo General:Que los estudiantes comprendan conceptos básicos de ingeniería y física aplicándolos en la construcción de un puente funcional usando materiales reciclables.  Preguntas guía (aprendizaje basado en indagación): ¿Qué hace que un puente sea fuerte? ¿Cómo influye el tipo de material en la resistencia? ¿Qué forma es la más estable para soportar peso?   Materiales: Palitos de helado, sorbetes, papel, cartón Cinta, pegamento, tijeras Cinta métrica o regla Pesas pequeñas (pueden ser bolsas de arena o pilas)   Fases de la clase:1. Introducción (15 min)Explicación breve sobre tipos de puentes y conceptos como fuerza, tensión y compresión. Se muestran imágenes o videos breves.2. Diseño (30 min)En grupos, los estudiantes dibujan un diseño de puente que puedan construir. Deben pensar en qué formas usarán (triángulos, columnas, etc.).3. Construcción (45 min)Con los materiales, cada grupo construye su puente según su diseño.4. Prueba y evaluación (30 min)Cada puente se somete a una prueba de carga (cuánto peso soporta). Se anotan los resultados y se reflexiona: ¿Qué funcionó? ¿Qué cambiarían? 5. Reflexión final (15 min)Discusión grupal sobre lo aprendido, cómo se relaciona con la vida real y qué habilidades STEM se usaron.  Evaluación: Participación activa y trabajo en equipo Creatividad y justificación del diseño Funcionamiento del puente Capacidad para reflexionar sobre el proceso  

Hairy vetch (Vicia villosa Roth) is an over-wintering annual legume valued for its ability to provide N through symbiotic N fixation (SNF). Although SNF is a high priority trait for farmers, evaluating SNF in a breeding program is a challenging endeavor. Our objective was to evaluate how SNF varies within and among hairy vetch genotypes, and how SNF covaries with traits directly targeted by breeders (i.e., vigor and flowering time). We evaluated SNF in two hairy vetch breeding nurseries (2017 and 2019) based on the proportion of N derived from SNF (Ndfa) measured using the δ15N natural abundance technique. We also considered plant size (vigor) and flowering stage. In 2017 we sampled plants on the same day, at varying developmental stages. In 2019 we sampled plants at a common developmental stage (early flowering) on different days. The 2017 data could not be confidently interpreted because of confounding effects between developmental stage and genotype. The 2019 data collection controlled for developmental stage effects and showed a positive relationship between vigor and Ndfa that appeared to be driven by flowering time. In other words, genotypes with delayed anthesis produced greater biomass and newly fixed N. All three traits showed discernable variation among genotypes and likely reflect cold late winter/early spring conditions that limited growth and N-fixation of early-flowering plants. Taken together, our study shows that developmental stage and community composition are important considerations for evaluating SNF in annual legume breeding programs.

Background: Despite increased awareness of its clinical consequences, personalized risk stratification in patients with moderate aortic stenosis (AS) remains ambiguous. We studied predictors of progression and clinical outcomes in moderate AS to improve risk stratification and add to the existing literature. Methods: Data collected through the Tempus Next care pathway intelligence platform at our tertiary referral center was retrospectively analyzed. Echocardiographic reports performed from October 2017 – January 2020 were screened for descriptive or quantitative evidence of moderate AS. Follow-up extended until January 2022. Clinical data was extracted following manual chart review. Socioeconomic variables were collected based on zip-code-aggregated United States census data. The endpoints were progression from moderate to severe valvular AS, all-cause mortality, all-cause hospitalization, and heart failure (HF) hospitalization. The final multivariable model was selected using a variable selection algorithm inspired by greedy causal discovery algorithms. Results : A total of 34,450 echocardiograms (N=25,204 patients) were screened during the inclusion period; 367 patients met inclusion criteria and were included in the final analysis. Progression to severe AS was noted in 172 patients (median time to progression 16 months). The final predictive models after variable selection exhibited modest predictive power: progression to severe AS, AUC=0.68; all-cause mortality, 0.797; all-cause hospitalization, 0.629; heart failure hospitalization, 0.744. Variables predictive for the endpoints included comorbidities, echocardiographic variables, and demographics. Conclusion : Our findings support further work and exploration of a paradigm shift in the assessment and management of AS, moving beyond traditional measures to a multiparametric model incorporating a broader spectrum of clinical, echocardiographic, and socioeconomic variables.

Chemical proteomics probes serve as critical tools for investigating small molecule--protein interactions within complex biological systems. Traditionally, they are categorized into covalent probes and photoaffinity probes. These probes have found extensive applications in bioactive molecule target identification, targeted ligand screening, enzyme activity monitoring, protein post-translational modification studies, and cross-omics integration. They facilitate drug target discovery, targeted ligand screening, dynamic evaluation of enzyme activities in disease contexts, protein modification mapping, and bridging proteomics with other omics platforms. Despite their significant utility, challenges remain in probe design optimization, reduction of non-specific interactions, and expansion of targetable proteomic landscapes. Future research efforts are expected to focus on the development of novel probes, the integration of chemical proteomics with structural biology and artificial intelligence, and the advancement of clinical applications. These innovations will deepen our understanding of protein functions and support the advancement of precision medicine. In this review, we summarize the classification and fundamental principles of chemical proteomics probes and provide an in-depth discussion of their diverse applications.

A document by Carlos Maldonado. Click on the document to view its contents.

In the paper, a dynamic event-triggered mechanism based-control strategy is proposed for a class of strict feedback nonlinear systems with input saturation and external disturbances. Then, a novel disturbance observer (DO) is designed to simultaneously estimate the approximation errors of the neural network (NN) and the external disturbances. In particular, the proposed DO does not require neural weight information, which results in higher precision than those of methods involving weight information. Next, an auxiliary system is designed to deal with the input saturation. Furthermore, to reduce the frequency of controller updates, the logarithmic dynamic event-triggered mechanism (LDETM) is designed. By incorporating a control input transmission mechanism into trigger conditions, it effectively mitigates input saturation. Finally, simulations are performed to validate the effectiveness and superiority of the proposed control strategy.

This document presents the Emergent Quantum Field Theory (E-QFT), an extension of standard Quantum Field Theory (QFT) based on a non-factorizable global Hilbert space. We develop the mathematical formalism of this space, conceived as a fundamental quantum field whose local manifestations (particles, interactions, spacetime) emerge from coherent projections. Local projection operators are precisely defined, and the gauge symmetries of the Standard Model (U (1), SU (2), SU (3)) naturally emerge from internal transformations of the global field. Numerical validations (precision ∆ < 10 −6) confirm local equivalence with standard QFT for various quantum processes. An analysis of topological properties, characterized by a Chern class c1 = 2, reveals the origin of phenomena such as quantum entanglement. Decoherence is quantified via a generalized Lindblad equation, showing an intrinsic topological protection (>99 %). Comparisons with recent experimental data display remarkable agreement (<5 % deviation). Finally, testable predictions, notably a topological signature (∆γB ≈ 0.057π), protection against decoherence, and a contribution to the muon anomalous magnetic moment (∆a NF µ ≈ 2.8 × 10 −11), are formulated.

Enhancing the photoelectrochemical (PEC) performance of CuWO₄ photoanodes has typically relied on doping or co-catalyst strategies to improve charge carrier dynamics. In this work, we present an alternative approach in which Fe(III) acts as a self-assembly mediator during hydrothermal synthesis, enabling the formation of a core–shell heterostructure composed of a crystalline CuWO₄ core, a partially amorphous CuO/WO₃ shell, and embedded metallic Cu nanoinclusions. Rather than functioning as a dopant or co-catalyst, Fe(III) is completely removed during post-synthetic treatment, mediating a redox-guided phase reorganization without being incorporated into the final material. This architecture establishes local heterojunctions that facilitate charge separation, suppress recombination, and enhance oxygen evolution reaction (OER) activity. A 30-fold increase in photocurrent is observed relative to pristine CuWO₄, as confirmed by structural, spectroscopic, and electrochemical analyses. Complementary photocatalytic dye degradation experiments reveal that Fe-activated particles act as highly efficient ROS-generating catalysts in suspension, demonstrating functionality beyond thin-film devices. These findings offer a new paradigm for oxide photoanode design, leveraging Fe(III)-induced self-assembly to engineer multifunctional heterostructures without relying on conventional doping.

The Los Angeles forest fire of January 2025 represents a significant environmental disaster, prompting immediate remote sensing analysis to assess its impacts. This study utilized freely available Landsat-8 satellite imagery from the USGS database and ArcGIS software to estimate key environmental parameters. Land Surface Temperature (LST) analysis revealed a temperature range from 7.62°C to 51.7°C during the fire event, with notable smoke coverage extending over 3,758.80 km² of land and 1,837.52 km² of coastal areas. Interestingly, LST values during the fire were lower than pre-fire measurements (54°C compared to 76°C), attributed to smoke-induced solar radiation scattering, atmospheric heat masking, and surface albedo changes. Normalized Difference Vegetation Index (NDVI) analysis showed a drastic decline in vegetation health, with values ranging from 0.9994 to -0.1186, reflecting widespread loss of chlorophyll concentration due to the fire. These findings provide crucial preliminary insights into the environmental consequences of the event. Further analysis will be conducted once post-fire satellite data is made available, to comprehensively assess vegetation loss, land cover changes, and property damage for assess the homelessness.

This study presents an empirical analysis of the dependence of tribal communities in the Sirumalai Hills on natural resources, exploring the challenges posed by deforestation, land-use changes, and the state of essential infrastructure. Data from 283 structured questionnaires reveal a significant reliance on forest-based agriculture, timber, medicinal plants, and wild resources. However, deforestation, land degradation, and changing climatic conditions have notably disrupted their livelihoods. In addition, the absence of critical infrastructure such as access to water, transportation, electricity, healthcare, and education further compounds the socio-economic vulnerabilities of these communities. The findings underscore the urgent need for integrated policy interventions that prioritize environmental conservation, sustainable resource management, and rural infrastructure development. Statistical analysis reveals a strong peak at p<0.001 for agricultural practices, signifying a true effect. A smaller peak at p=0.028 for firewood use suggests a real, though less pronounced, trend. In contrast, higher p-values (>0.05) for hunting and medicinal plant use indicate no significant relationship. These results contribute to understanding the complex interplay between environmental and socio-economic factors affecting tribal livelihoods in the region.

Transformer architectures have driven advances in natural language processing, powering systems from question answering to creative text generation. Yet as model sizes have grown from hundreds of millions to hundreds of billions of parameters, the computational cost of these systems has become prohibitive, placing advanced language technology out of reach for many researchers. In this work, we introduce QuantumGPTMini, a hybrid quantum-classical transformer with just over one million classical parameters that leverages lightweight quantum circuits to perform its core attention and feed-forward computations. By encoding token embeddings both in conventional vectors and in the amplitudes of small variational quantum circuits, our model replaces the conventional softmax attention mechanism with an entanglement-driven alternative. When trained on the Wikipedia-2 corpus using PennyLane's lightning.qubit simulator on NVIDIA A100 GPUs, QuantumGPTMini matched the perplexity of a GPT-2 Small baseline while demonstrating superior pattern recognition and symbolic reasoning behaviors. We have made this system publicly accessible through a FastAPI endpoint and a Next.js web interface. While our initial results underscore the potential of quantum-augmented language models, they also highlight challenges in moving beyond simulation, including circuit depth limitations, decoherence, and the high overhead of parameter-shift gradient estimation.

As cyber threats continue to grow in complexity and frequency, integrating security into software development life cycle (SDLC) has become a critical need. However, Small and Medium Enterprises (SMEs) often face significant challenges in adopting Secure SDLC practices. The literature review explores key barriers such as limited budgets, lack of in-house security expertise, time constraints, poor integration of security tools, and remediating detected vulnerabilities without affecting the business. The findings highlight that SMEs tend to prioritize rapid development, cost-efficiency, rapid business growth over robust security, leaving their applications vulnerable. Addressing these challenges requires simplified, cost-effective, business enabler security solution, better awareness, and targeted training programs to support SMEs in building secure software from the ground up.

The pursuit of lightweight, structurally sound, and cost-effective designs is paramount in contemporary engineering, particularly in the development of advanced enclosed mast/sensor systems (AEM/S). A significant challenge in this field is the limited availability of comprehensive data and systematic classification of studies pertaining to these specific mast configurations. This research addresses this gap by initially developing a model of a steel and anti-radar composite mast, subsequently employing Abaqus/CFD and Abaqus/FEM to conduct aerodynamic and finite element analyses, respectively. This initial phase aims to characterize the structural behavior of this mast type. In the second stage, a neural network-based method, coupled with a genetic optimization algorithm, is implemented to determine the optimal dimensions for the mast and its associated casing. This optimization process culminated in a composite mast design, standing 22 meters tall with a 9.59-degree slope, achieving a substantial 50% weight reduction compared to the original steel mast design (reducing the mass from 118 to 27.51 tons). Finite element analysis (FEA) was utilized to assess the mechanical behavior of the initial and optimized designs. The results demonstrate that the optimized design exhibits a more evenly distributed stress profile, with reduced stress concentrations in the lower sections and increased stress levels in the upper antenna region. This stress redistribution suggests improved material utilization and enhanced structural integrity. By minimizing the potential for localized failure, the optimized design demonstrates the feasibility of achieving significant weight reductions without compromising structural performance. These findings underscore the effectiveness of artificial neural network (ANN)-based optimization in creating lightweight and efficient composite structures, providing valuable insights for designing AEM/S systems and other tall structures subjected to dynamic loads. The study highlights the potential of advanced optimization techniques and composite materials in achieving sustainable and high-performance engineering solutions.

This paper proposes a sub-optimum Kuhn-Munkres-based resource allocation algorithm to maximize the number of connected links and total throughput served by ultra-dense networks consisting of densely distributed co-channel access points and user equipment. In the proposed seven-step algorithm, users are first assigned to access points supporting higher data rates considering the interference of all access points. Then, only the interference of the selected access points is considered and users connected to these access points that meet the minimum throughput threshold level are found. Afterward, considering the interference of the access points assigned in the first run and the remaining access points selected in the next runs, new users are connected to the remaining selected access points. Simulations in MATLAB for a service area of 250 meters by 250 meters including randomly distributed 250 access points and different numbers of 25 to 250 users, show more connected users and total throughput, and much less processing time of the proposed algorithm than those for Genetic, particle swarm optimization, cuckoo search, and grey wolf optimization algorithms. By changing the number of users from 10% to 100% of the number of access points, the proposed algorithm increases the number of connected users by 10% to 48%, 47% to 96%, 57% to 109%, and 22% to 58%, and the total throughput by 20% to 52%, 44% to 86%, 50% to 105%, and 22% to 69% compared to the four mentioned algorithms. Due to the lower complexity order, it experiences at least 99% less processing time.

\papertype Original Article  The pace of global change presents challenges for adequately assessing outcomes of agricultural management, hindering decision-making by producers, researchers, and consumers. The Long-Term Agroecosystem Research Network (LTAR) is in a unique position to advance monitoring to inform decision making. Here we describe how the network selected performance indicators designed to measure the tradeoffs from various farming and ranching approaches. Indicator selection was motivated by the need for common indicators that apply to the diversity of LTAR sites, but they are intended for widespread use by producers and other managers via the Agricultural Performance Indicator and Context Knowledge System (AgPICKS). An initial set of domains, attributes, and indicators was developed via synthesis of structured conversations at national LTAR meetings. Early use revealed the need for a systematically inclusive process toward improvement. We designed and implemented an iterative decision-making protocol to reach a consensus for a new version. The indicator framework differs from others in its attention to production and social outcomes and its grounding in networked agricultural science. Next steps entail developing web tools and personnel for AgPICKS that use LTAR’s data and knowledge ecosystem to guide users in setting benchmarks of the desired conditions for their prioritized indicators, collect data, and visualize data to assess how well their management meets their benchmarks, toward the accurate measurement of management outcomes in a changing world.

The consensus that Autism is from an intrauterine infection has been growing, bolstered by Patterson's and Fatemi's studies. However, the question remains: which infection? In this review, a prime, conceivable candidate is presented, supported by scientific literature, old and new. Until 1980 autism is still called "childhood schizophrenia" and in some parts of the world, it still is. But there is an extensive body of literature which ties schizophrenia to the infectious focus of this paper. This was only brought more sharply into focus when Rzhetsky, in 2007, used a proof-of-concept biostatistical analysis of 1.5 million patient records, finding significant genetic overlap in humans with autism, schizophrenia........and tuberculosis. And in March of 2017, Ahmedabad-based Dr. Ketan Patel, who has more than 20 years' experience in treating and researching autism in children, said that as many as 45% of the autistic children in the world are found, upon proper scrutiny, to have a history of tuberculosis on either their family's maternal or paternal side. Ever since Nadya Markova's study, also in 2017, it can no longer be questioned that the all too common Cell-Wall-Deficient [CWD] forms of mycobacteria such as TB can easily penetrate the umbilical cord and infect the fetus from the maternal blood stream, even in the case of seemingly normal healthy deliveries. Tracing the history of autism from John Langdon Down's children, a subset of which were autistic, to the present, this paper also explains how these stealth pathogen hypothesized to be behind Autism have evaded modern diagnostics.

Polymicrobial Pasteurella multocida-Anaerobic Coinfection Following a Cat Bite: Limb Salvage through Metagenomic Next-Generation Sequencing-Guided Diagnosis and Multidisciplinary ManagementLu Wang1*, Yuanqing Qu1 , Yuan Liu1 Xin Zhou2Pengjie Xu3Affiliation: Department of Laboratory Medicine, General Hospital of the Western Theater Command, Chengdu, Sichuan, 610083, China.The Department of Clinical Laboratory at Hengyang Medical School, University of South China, is situated in Hengyang, Hunan, 421002, China, and is associated with Nanhua Hospital.3.Emergency Department, the General Hospital of the Western Theater Command, Chengdu, Sichuan, 610083, China.Corresponding Author: Department of Laboratory Medicine, General Hospital of the Western Theater Command, Chengdu, Sichuan, 610083, China.* Correspondence: Lu WangEmail:wl2000ok@163.comLu Wang and Yuanqing Qu contributed equally to this work.

Many bird species are monitored using auditory point count surveys during the breeding season. Advances in passive acoustic technology have enabled the use of autonomous recording units (ARUs) alongside point-count surveys, improving survey methodologies. However, automated song/call identification and manual review of recordings are required to assess accuracy of data collected from ARUs. We evaluated the feasibility and accuracy of PNW-Cnet, an AI-based detection application, for identifying the hooting song of male Sooty Grouse (Dendragapus fuliginosus). From 2020–2023, we deployed ARUs at 149 locations in western Oregon, USA near known hooting males. We used PNW-Cnet to identify hoots and the accuracy of the detector was calculated by manually verifying 10,000 detections. Accuracy was near perfect relative to false detections. Once hoots were identified, we used generalized additive models with random effects to examine seasonal (across the breeding season) and daily (relative to time since sunrise) hooting patterns. Model results indicated hooting rates peaked in late-April, providing guidance on the optimal timing of point count surveys based on the estimated number of hoots that would be heard per survey. Daily patterns revealed a rapid increase in hooting 30 minutes before sunrise, then leveling at a relatively constant hooting rate up to at least six hours after sunrise (latest time our ARUs were recording). Thus, our results suggest males continue to vocalize throughout the entire morning allowing effective surveys to be conducted beyond the early morning. By integrating ARUs and an AI-based detection application, we gained detailed information about hooting patterns that will allow improvements to future data collection, increasing survey efficiency, and ultimately leading to a more efficient population monitoring.

Python's dynamic typing model provides flexibility and rapid development capabilities, but it also introduces significant risks in safety-critical and high-assurance systems where type correctness is essential. This paper examines the current limitations of Python's type system, highlighting challenges related to runtime type errors, the broadness of existing type annotations, and the lack of strong enforcement mechanisms. Although recent advancements, such as gradual typing, offers opportunities for improved static analysis, it remains insufficient to guarantee complete type safety. The paper argues for an enhanced methodology that includes extending the type system through constraint-based types, rigorous validation of dynamically sourced values, and the systematic development of tailored types for critical applications. By integrating stricter static analysis and structured runtime validation practices, Python can be adapted to better meet the demands of high-assurance software development.

Scavenging is a foraging strategy widely used across the animal kingdom and apex predators provide a large amount of energy in a food web. In the harsh environmental conditions of the Arctic, apex predators such as polar bears (Ursus maritimus) can provide scavenging opportunities for many species. Carrion can act as a buffer when food resources are low, and some terrestrial species use the marine environment for cross-ecosystem resource subsidies. We present an overview of scavenging as a foraging strategy in the Arctic marine environment and examine the contribution of prey provided by polar bears to the Arctic scavenging assemblage. As obligate predators of seals, polar bears contribute a substantial amount of carrion to the marine ecosystem, particularly to the sea ice surface where it is accessible for seasonal scavenging opportunities. We estimated that each adult polar bear kills an average of 1,001 kg of marine mammal biomass annually, and given preferential feeding of blubber and abandonment of carcasses, we estimate that 30% of the biomass is left as usable carrion. Consequently, polar bears provision approximately 7.0 x 106 kg/year of usable carrion biomass for scavengers across their range, equivalent to 1.55 x 108 MJ of energy. Eleven vertebrate species are known to scavenge polar bear kills, and an additional 7 are potential scavengers. While foraging associations with polar bear kills for some species are better understood, others are scarce or undocumented. We provide an overview of what is known about the role of polar bears as carrion providers, the network of scavenging species on the sea ice, and the possible consequences of trophic downgrading in this ecosystem and recipient ecosystems.

Soil pollution is usually associated with biodiversity loss. However, soils enriched with zinc, lead and cadmium can support unique vegetation such as calamine grasslands, which are priority habitats for nature conservation programmes. This study investigates a declining calamine grassland in Northern France, which developed in the 1960s in the immediate vicinity of a former smelting plant. It was initially dominated by two locally rare and absolute metallophyte dicot species, Armeria maritima and metal-hyperaccumulating Arabidopsis halleri, in association with Agrostis capillaris. From then on, the grassland gradually declined and developed into a meadow, largely dominated by a tussock-forming grass: Arrhenatherum elatius. Possible explanations include landscaping, a decrease in soil metal concentrations or the replacement of pioneer species by later successional species. To better understand the causes of the decline and to discuss restoration strategies, we carried out an ecological study including: (1) compilation of available data to reconstruct the history of the site, (2) chemical analysis of soil element concentrations, (3) functional analysis of potential plant-plant interactions, and (4) a 7-year restoration experiment including vegetation monitoring. The results suggest that in the absence of significant anthropogenic disturbance, while soil metal concentrations remain elevated, grassland decline is best explained by plant successional dynamics. Accordingly, repeated disturbances, such as the removal of A. elatius tussocks by annual mowing of the vegetation with organic matter removal, not only stopped the expansion of A. elatius but also allowed a partial recovery of the calamine grassland.

According to the ‘nest protection hypothesis’, some passerines incorporate fresh aromatic plants into their nests to reduce pathogens that can negatively affect nestlings. We experimentally evaluated the effect of five aromatic plant species on the nest bacterial microbiota of Corsican blue tits (Cyanistes caeruleus). The experimental addition of aromatic plants decreased bacterial diversity in nests collected post-hatching. We also detected a weak effect of aromatic plants on nest bacterial composition. In the observational approach, we tested the effect of these plants on the bacterial microbiota diversity and composition of eggshells and nests, and on nestling condition and behavior. Bacterial diversity decreased with the quantity of aromatic plants in nests containing nestlings and on eggshells during incubation, but only in one of the three studied populations. Again, there was a weak effect of aromatic plants on bacterial composition in nests and no effect on eggshell bacterial composition. Finally, nests with a high quantity of aromatic plants tended to have bigger and taller nestlings in two out of three populations. In the other population, the quantity of aromatic plants was associated negatively with speed of feather development and positively with handling aggression. Our results support the ‘nest protection hypothesis’, while highlighting differences in the effect of aromatic plants among populations. To our knowledge, our study is the first to reveal a correlation between the presence of aromatic plants and the bacterial diversity of nests and eggshells in a natural population, and to demonstrate experimentally that it results from a direct effect of five aromatic plant species on bacterial diversity in nest material.

\papertype Original Article While large grazers are well known to alter fire regimes, small herbivore effects on fire have received comparatively little attention. The gopher tortoise (Gopherus polyphemus) is a small herbivore that acts as an ecosystem engineer in upland, fire-dependent ecosystems of the southeastern U.S. They dig burrows that locally decrease fire intensity, and these burrows provide a refuge for many animals during fires. Importantly, their burrowing and foraging activities have the potential to modify fire regimes via several mechanisms. Gopher tortoises may actively decrease fire intensity near their mounds, either by reducing plant biomass and/or by altering the flammability of the adjacent plant community. Alternatively, tortoises may preferentially burrow at microsites with relatively nonflammable vegetation and low fuel loads. To test these hypotheses, we leveraged data from intensive monitoring of gopher tortoises at Archbold Biological Station in south-central Florida, USA. We selected 30 existing burrows varying in activity status (active, inactive, abandoned) as well as nearby, non-mound control points. We characterized plant biomass and community composition within 15 m of mounds and non-mound points and quantified 11 fire-related traits for 23 common plant species. Mounds of both active and inactive tortoise burrows had lower plant and litter cover than abandoned mounds and the surrounding vegetation matrix, but these differences in vegetation and litter did not extend beyond the mound itself nor persist following burrow abandonment. Tortoise effects on community-level flammability were minor and unlikely to modify fire intensity. Overall, the highly localized soil disturbance associated with burrowing is likely the primary means by which gopher tortoises may alter sandhill fire regimes. Critically, our study highlights how small animals can shape fire behavior via direct reduction of fuel loads.

The integration of artificial intelligence (AI) into cloud-based project management systems has revolutionized workforce allocation by enhancing efficiency, scalability, and decision-making. This study explores AI-driven approaches for optimizing workforce distribution in dynamic project environments, leveraging predictive analytics, machine learning, and real-time data processing. By automating task assignments, skill matching, and workload balancing, AI minimizes resource wastage and improves project outcomes. The paper evaluates key AI techniquesincluding reinforcement learning and natural language processing-within cloud platforms to demonstrate their impact on productivity and cost reduction. Case studies from industry implementations highlight the benefits and challenges of AI adoption in workforce management. The findings suggest that AI-driven allocation not only accelerates project timelines but also adapts to evolving business demands, fostering agile and data-informed workforce strategies.