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Estimating Rainwater Harvesting Potential for Mexico City Metropolitan Area Using System Dynamic Modeling
No full textWater scarcity is a growing concern in the Mexico City Metropolitan Area (MCMA) due to increasing population, rising demand, and over-extraction of groundwater. Rainwater harvesting (RWH) presents a potential supplementary water source to reduce reliance on municipal supplies and mitigate groundwater depletion. However, large-scale implementation faces challenges, including disparities in water access, infrastructure limitations, and the impacts of climate change. This study estimates the potential of RWH in MCMA using system dynamics modeling, incorporating population growth projections, storage capacities of 2,500L, 5,000L, and 10,000L per household, and climate change scenarios SSP2-4.5 and SSP5-8.5 to analyze future precipitation variability. Results indicate that as population grows, RWH potential increases due to larger rooftop areas available for rainwater capture. Water savings range from 11% to 29%, with higher efficiency observed during the wet season. However, significant disparities exist among marginalization zones (1–5), with more vulnerable communities facing greater difficulties in implementing RWH systems. Climate change projections reveal variations in future precipitation patterns, emphasizing the need for adaptive and integrated water management strategies.
Climate change projections reveal variations in future precipitation patterns, emphasizing the need for adaptive and integrated water management strategies. While RWH can help reduce municipal water demand and groundwater depletion, its success depends on policy alignment, infrastructure investment, and public engagement. Addressing water quality concerns, maintenance, and public acceptance is key to a large-scale adoption. Results suggest that RWH, when paired with supportive policies and infrastructure, enhances resilience and reduces municipal costs. RWH offers a promising strategy for urban water management in MCMA, its long-term and large-scale success for water-sustainability
Examining the performance of precipitation products in characterizing the Indian summer monsoon rainfall (ISMR) using triple collocation
No full textPrecipitation datasets are crucial for understanding and studying the hydroclimatic processes. The advent of satellite observations and reanalysis has enabled to produce the precipitation observation at considerably fine spatial and temporal scales. However, the applicability and representativeness of these data products are still not definite, specifically over a vast topographic, ecologic, and climatic gradient and data-scarce region like India, where reliable (quality-controlled) and dense ground observations spread homogenously over space are inaccessible. Additionally, Indian Summer Monsoonal Rainfall (ISMR) produces ∼ 75 % of annual rainfall and determines the success of the agrarian Indian society and economy and subsequently, the sustenance of millions of people. In this context, reanalysis and satellite datasets could play a vital role by providing historical and near-real-time accurate precipitation estimates in understanding the climatic variability and forecasting the hydrometeorological extremes such as drought, urban floods. Hence, this study aims to evaluate four state-of-the-art precipitation data products over India by deploying the robust triple collocation (TC) technique during the principal rainy season (June to September). TC is a powerful formulation that allows the quantification of the error and agreement of the independent datasets without having the knowledge of reference observation. We further determine the order of suitability (rank) of examined data products based on the minimum error and maximum agreement estimated from TC. Furthermore, we derive the regional suitability of the datasets based on the Köppen–Gieger climate zones. Results suggest that: (1) reanalysis data products (IMDAA and ERA5-Land) outperform (rank one) other datasets in the wet tropical monsoon regions (west coastal plain and northeastern India), the highest rainfall receiving regions; (2) CHIRPS is the most suitable dataset in the transitional climatic conditions and facilitates the characterization of moderate to low-intensity ISMR events; (3) IMD is found to be suitable in peninsular India, where high gauge density is present
A decision support framework for misstatement identification in financial reporting: A hybrid tree-augmented Bayesian belief approach
No full textOver a six-year period, employees and managers at Wells Fargo created 3.5 million false deposit and credit card accounts resulting in $4.8 billion in fines. Following this incident, there has been a newfound focus on effective internal controls. The purpose of the current study is to improve misstatement identification by formulating a novel hybrid decision support framework to a) accurately predict financial misstatements and frauds, b) build a parsimonious model by employing a comprehensive variable selection procedure without hurting (in contrast, potentially improving) the model\u27s prediction power, c) uncover the conditional inter-dependencies between the predictors via a Bayesian-belief based probabilistic network, and d) provide stakeholders with a firm-specific MWIC risk score. In an extensive real-life experimental setup, we validate our decision support system and find that the Tree-Augmented Bayesian Belief Network (TAN) model provides high misstatement identification accuracy results when the variables are selected through the Genetic Algorithm (GA) that employs Random Forests (RF) as the classification algorithm (AUC of 0.856 by employing only 5 out of 23 potential variables). Financial experts and stakeholders can use the probabilistic scores provided, while their intuition/incentive should collaborate with prediction models to make final decision on the cases where the model is not confident enough (i.e., when the probabilistic scores are close to 50/50). These insights enable stakeholders to improve the early warning systems for MWIC and financial misstatements and therefore potential frauds
Characterization of fungal carbonyl sulfide hydrolase belonging to clade D β-carbonic anhydrase
Get PDFCarbonyl sulfide hydrolase (COSase) is a unique enzyme that exhibits high activity towards carbonyl sulfide (COS) but low carbonic anhydrase (CA) activity, despite belonging to the CA family. COSase was initially identified in a sulfur-oxidizing bacterium and later discovered in the ascomycete Trichoderma harzianum strain THIF08. The COSase from T. harzianum has been recognized as a key enzyme in the assimilation of gaseous COS, and homologous genes are widely present not only in Ascomycota but also in Basidiomycota. Here, we characterized the COSases from the basidiomycete Gloeophyllum trabeum NBRC 6430 and T. harzianum to obtain detailed characteristics of fungal COSase. This study contributes to a better understanding of COS metabolism in fungi
Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
Get PDFMapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled datasets, which are typically limited to specific years or regions. This restricts the ability to create annual maps needed for agricultural monitoring, as changes in farming practices and environmental conditions cause domain shifts between years and locations. To address this, we focused on improving model generalization without relying on yearly labels through a holistic approach that integrates several techniques, including an area-based loss function, Tversky-focal loss (TFL), data augmentation, and the use of regularization techniques like dropout. Photometric augmentations helped encode invariance to brightness changes but also increased the incidence of false positives. The best results were achieved by combining photometric augmentation, TFL, and Monte Carlo dropout, although dropout alone led to more false negatives. Input normalization also played a key role, with the best results obtained when normalization statistics were calculated locally (per chip) across all bands. Our U-Net-based workflow successfully generated multi-year crop maps over large areas, outperforming the base model without photometric augmentation or MC-dropout by 17 IoU points
“I can\u27t fit into these boxes, it doesn\u27t work”: Multiracial men\u27s experiences and expressions of masculinity.
No full textLittle is known about how masculinity is understood, embodied, and performed by Multiracial men as compared to monoracial men. The findings of the present qualitative study, based on semistructured interviews with 14 Multiracial men residing in the United States, demonstrate a diversity of experiences as Multiracial men navigate intersecting and restrictive gender and racial ideologies, including hegemonic masculinity and racial essentialism. The study documents proximal (e.g., romantic partners, colleagues, and bullies) and distal (e.g., media representation) influences on Multiracial men\u27s self-concepts and provides novel insights into the challenges and strengths possessed by this historically understudied population. Notable challenges faced by the men in this sample include integrating conflicting race-based expectations for how to be a man and being fetishized and exoticized across different domains. Notwithstanding, participants reflected on their strengths of gregariousness, empathy, and having the ability to code-switch. This study subverts essentialist understandings of gender and race and offers potential clinical and research implications and recommendations. (PsycInfo Database Record (c) 2025 APA, all rights reserved) © 2025 American Psychological Associatio
A discussion of positioning theory: An interview with Michael Bamberg and Luk Van Langenhove
Get PDFSince 2015 a Positioning Theory conference has been held biannually in either Europe or the US. These conferences have become a significant event for scholars and practitioners interested in the development and application of PT providing thereby a platform for discussing the latest research, sharing insights, and fostering collaborations. In the fourth edition of the conference, organised in Finland in July 2024, Professor Van Langenhove and Professor Bamberg both provided keynotes and Pasi Hirvonen and Bo Allesøe Christensen seized the opportunity of engaging both in an interview discussing their insights, contributions and personal thoughts about PT and its history. © The Author(s) 2025
Baseball team group photo [4], 1984
No full textThe 1983-1984 Clark University baseball team group photo.
All photographs in this collection were digitized between 2022 and 2023. The photographs in this collection are part of the Photographs and Media record group of Clark University’s Archives & Special Collections.https://commons.clarku.edu/baseball/1038/thumbnail.jp
Baseball player in the middle of an at-bat [2], circa 1950s-1960s
No full textA Clark University baseball player in the middle of an at-bat, circa 1950s-1960s.
All photographs in this collection were digitized between 2022 and 2023. The photographs in this collection are part of the Photographs and Media record group of Clark University’s Archives & Special Collections.https://commons.clarku.edu/baseball/1014/thumbnail.jp
Science is Objective, Isn\u27t It?: Countering the Effects of Structural Racism in Citation Practices
No full textThis chapter challenges the color-blind discourse that science should be objective versus the reality that social constructs like race affect knowledge generation. The antiracist action plan involved activities in a neuroscience course that guided students to explore how systemic and individual biases affect citation practices. In class, students discussed different readings that considered the racial and ethnic imbalances in citations in neuroscience, why citation equity matters, and how to broaden our exposure to scientists with minoritized identities. Then, students generated their own action plans for generating more inclusive citation lists. They also wrote papers reflecting on their key takeaways from the course activities on race and citation equity. Based on their written responses and action plans, students learned about the importance of citations and how racism affects citations and citation practices. Although this was a set of activities done in one science class, it provides a starting point for faculty in and outside of STEM to address systemic bias in their own disciplines, specifically in citation practices. © 2026 selection and editorial matter, Jie Y. Park and Laurie Ross; individual chapters, the contributors