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Frequent Disturbance to a Foundation Species Disrupts Consumer-Mediated Nutrient Cycling in Giant Kelp Forests
No full textStructure-forming foundation species facilitate consumers by providing habitat and refugia. In return, consumers can benefit foundation species by reducing top-down pressures and increasing the supply of nutrients. Consumer-mediated nutrient dynamics (CND) fuel the growth of autotrophic foundation species and generate more habitat for consumers, forming reciprocal feedbacks. Such feedbacks are threatened when foundation species are lost to disturbances, yet testing these interactions requires long-term studies, which are rare. Here, we experimentally evaluated how disturbance to giant kelp, a marine foundation species, affects (1) CND of the forest animal community and (2) nutrient feedbacks that help sustain forest primary production during extended periods of low nitrate. Our experiment involved removing giant kelp annually during the winter for 10 years at four sites to mimic frequent wave disturbance. We paired temporal changes in the forest community in kelp removal and control plots with estimates of taxon-specific ammonium excretion rates (reef fishes and macroinvertebrates) and nitrogen (N) demand (giant kelp and understory macroalgae) to determine the effects of disturbance on CND as measured by ammonium excretion, N demand by kelp forest macroalgae, and the percentage of nitrogen demand met by ammonium excretion. We found that disturbance to giant kelp decreased ammonium excretion by 66% over the study, mostly due to declines in fishes. Apart from a few fish species that dominated CND, most reef-associated consumers were unaffected by disturbance. Disturbance to giant kelp reduced its N demand by 56% but increased that of the understory by 147% due to its increased abundance in the absence of a kelp canopy. Overall, disturbance had little effect on the fraction of N demand of macroalgae met by consumer excretion due to the offsetting responses of giant kelp, understory macroalgae, and consumers to disturbance. Across both disturbance regimes, on average, consumers supported 11%–12% of the N required by all kelp forest macroalgae and 48% of N demand by the understory macroalgae (which are confined to the benthos where most reef-associated consumers reside). Our findings suggest that CND constitutes a considerable contribution of N required in kelp forests, yet nutrient inputs decrease following reductions in essential habitat perpetuated by frequent disturbances
Fresno’s Scribbles Bike Path: A Master Plan for Active Transportation
Get PDFThe Fresno Scribbles Bike Path Master Plan, which represents a commitment to equitable active transportation and social infrastructure, is designed to enhance the quality of life for Fresno residents by providing safe, comfortable, and accessible biking and walking paths while promoting sustainability and fostering a sense of community. This report details the Intelligent Design Visualization Lab’s (IDVL) ongoing contribution to the Fresno City Scribbles Bike Campus initiative. This multifaceted urban intervention seeks to promote bike and pedestrian safety, enhance environmental resilience, and active transportation infrastructure through design, serving as a form of placemaking. In collaboration with the Fresno State Transportation Institute(FSTI) and in alignment with the Fresno County Regional Active Transportation Plan (FATP), the project focuses on two critical deliverables: the design development of eight educationally themed, district-specific bike shelters and the creation of a heat island mitigation design toolkit (which aims to reduce the impact of “heat islands” in which urban areas become significantly warmer than surrounding rural areas due to human activities and built environment), known as the Heat Island Design Toolkit(HIDT). Both efforts are grounded in health, safety, and well-being principles, as well as democratized design, environmental equity, social justice, and spatial agency. The gateway trailhead bike shelters are designed as contextual and functional complements to the cultural and architectural vernacular (styles) of the eight districts in the greater Fresno area. They are intended to support year-round use of the biking infrastructure by integrating amenities such as shade structures, charging stations, water access, and repair services. Parallel to this, the Heat Island Toolkit investigates and prototypes scalable strategies to reduce surface temperatures at gateway trailheads and along bike pathways. The mitigation strategies include community surveys, site-specific analysis, 3D renderings, and flythroughs of the sites. The master plan proposes a participatory framework that prioritizes distributed decision-making by engaging district residents through both in-person touchpoint stations and asynchronous digital tools for feedback. Findings from early survey data indicate district-level variation is preferred in heat mitigation design strategies, underscoring the need for localized design informed by community engagement. Ultimately, the initiative advances a public social infrastructure model highlighting safety, health and wellness, climate adaptation, and inclusive design processes. By merging technological exploration with grassroots engagement, the IDVL proposes an ecologically responsive, resilient, and socially oriented archetype for active transportation design in California’s Central Valley. These efforts promote bike and pedestrian use and safe, inclusive, equitable, and environmentally sound mobility
Conference Report: 200th Anniversary of Beethoven\u27s 1824 Academy Concerts (Beethoven-Haus, Bonn, 2024)
Get PDFSpartan Daily, April 22, 2025
Get PDFVolume 164, Issue 35https://scholarworks.sjsu.edu/spartan_daily_2025/1034/thumbnail.jp
Latent Active Transportation Methodology
Get PDFUnderstanding and estimating latent demand for active transportation, such as walking and cycling, is essential for designing infrastructure and policies that promote sustainable mobility. Unlike traditional demand models that focus on observed trips, latent demand estimation seeks to quantify the unrealized potential for active travel due to barriers such as inadequate infrastructure, safety concerns, or lack of connectivity. This study develops a comprehensive latent demand model tailored for California, integrating geospatial analysis and multimodal accessibility assessments. The methodology employs a GIS-based corridor analysis approach, utilizing spatial accessibility metrics and distance decay functions to evaluate potential demand. It incorporates employment and population data, school and university enrollments, and park and trail accessibility to estimate the likelihood of walking and cycling trips. To better capture behavioral patterns, the model also classifies cyclists into four categories: strong and fearless, enthused and confident, interested but concerned, and no way, no how. Case studies in Douglas City, El Centro, and downtown San Jose illustrate the model’s application across urban, suburban, and rural contexts. Findings highlight that employment centers and commercial areas significantly contribute to bicycle demand, while schools and recreational spaces influence pedestrian activity. The results emphasize the need for targeted infrastructure improvements to convert latent demand into realized active transportation. This study provides a data-driven framework to guide investments in pedestrian and cycling infrastructure to ensure equitable and effective transportation planning in California
International Perspectives on Research Data Management Services in Academic Libraries
Get PDFThe information landscape in academic libraries is undergoing a massive fundamental shift, from scarcity to abundance aided by changes in technology. One such change is the development of research data management (RDM) and their related services. Research data management services (RDMS) allow libraries to aid in “creating, finding, organizing, storing, sharing and preserving data within any research process” (Cox & Verbaan, 2018). Our research seeks to understand where RDMS is currently at on the global stage. Are international institutions currently developing RDM services? What is needed for successful implementation? And are there any considerable challenges? Through a comprehensive literature review, we propose that nine main areas related to RDMS affect implementation: skills of librarians and researchers; engagement; communication between individuals and the establishments dealing with data; incentives; technology use and infrastructure; data security; organizational support or proper structuring of services; collaboration; and resources and funding. Understanding these areas will allow academic institutions to evaluate their own programs and aid in implement of RDMS
The Evolution of Video Game Collectibles and Marketplaces
Get PDFDigital Collectibles such as weapon skins, avatar skins, emotes, and in-game art were created as a playful surprise for the video game experience. Many years later, it has produced a multibillion-dollar economy in the gaming industry and is also responsible for the high demand for intriguing in-game collectibles. Although these cosmetic features were initially meant for looks, they have evolved into something of actual value that has completely changed the landscape of games and how they are played. As a result, this has created an intricate environment where players are able to buy, trade, and sell in-game collectibles in both sanctioned and unsanctioned marketplaces
Synthetic Malware Generation using Generative AI
No full textMalware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein GANs to generate synthetic embeddings, specifically testing on eight held-out families not observed during GAN training. To validate utility, we create ten augmented training sets at 100%–10% synthetic ratios and compare four classifiers to 100% real data baselines.
Our experiments show that embeddings generated by GANs match the perfor- mance of models trained on real training. Most importantly, the samples generated by GANs generalize across families without overfitting, completing gaps within the data. In our future work, we will test stronger embeddings (e.g., GloVe, Word2Vec, ELMo) and newer adversarial frameworks like WGAN-GP to further enhance malware detection robustness
Why We Need to Fill in Our Mixed-Use Communities’ Business Vacancies and How We Can Do That
Get PDFThis planning report provides an in-depth exploration as to why so many mixed use communities, including transit-oriented developments, in metropolitan regions across the country are facing challenges with filling in their retail business vacancies at the ground floor. It also provides a current snapshot of best practices to remedy this widespread barrier to cities functioning more effectively and sustainably. This report provides a combination of findings from a literature review which identifies the most common reasons and remedies for business vacancies, and a research design which identifies which urban planning or urban design characteristics correlate most frequently with case sites that have had more success with leasing their retail spaces. The completion of this report involved six case sites in total from the southern portion of the San Francisco Bay Area: three of which were case sites with more than half of their spaces currently being leased, and the other three were sites with less than half of their spaces filled. Closely correlating with literature review findings, research design findings reveal that successful case sites consistently (1) had more welcoming street design amenities, (2) were near already existing bubbles of retail activation, (3) leased to businesses which need to be accessed in-person, (4) had more furnishings in their spaces remaining for lease, and (5) were located near higher numbers of educational facilities and transit stops
Gen Ai for Malicious Network Data
No full textThough botnet attacks are on the rise, they also have become sophisticated and difficult to detect. Such a rising threat demands more and more sophisticated cybersecurity that leverages machine learning technology. Nevertheless, one of the biggest bottlenecks remains the unavailability of large and well-balanced datasets, particularly for malicious traffic, which hampers the efficacy of detection models. In an attempt to address this issue, our research utilizes Generative Adversarial Networks (GANs) to produce synthetic samples of botnet traffic from the CTU-13 dataset. While the majority of generative models have been targeting image data, we use GANs for a new application: generating flow-based network traffic records that mimic actual botnet activity. These generated records are intended to mimic actual attacks as closely as possible, thereby addressing the scarcity of data and enhancing the classifier’s training. Different machine learning algorithms like Random Forests, Decision Trees, and Deep Neural Networks are trained on both real and synthetic data, demonstrating considerable improvements in identifying infrequent botnet patterns. Motivated by some of the recent results on the application of GANs to intrusion detection and malware, our paper highlights the capabilities of synthetic data to improve the accuracy and generalization of security models. First, results suggest that not only do these artificial samples look realistic, but they also fool regular detection tools, proving that this technique may help in developing more robust and persistent botnet detection systems