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Abstract Previous research has shown that female and Hispanic students who are underrepresented in science, technology, engineering and mathematics (STEM) face more educational barriers than their non-Hispanic, male peers. However, little research has been conducted on the effects of intersectional identities in the STEM space. In an effort to bridge the gap in underrepresented students' experience, the PSEG Institute for Sustainability Studies organizes a paid, interdisciplinary, team-based, experiential learning and internship program called the Green Teams that occurs during 10 weeks of the summer. The Green Teams Program strives to provide undergraduate students from all backgrounds–academically, economically, and demographically–an opportunity to develop their abilities in STEM fields and prepare them to enter the professional world. Based upon a survey given post-internship, self-reported learning gains for all students were analyzed to determine if the program had a significantly greater impact on students who are from groups traditionally underrepresented in STEM in their STEM-related learning gains and their confidence in STEM disciplines. Through t-tests, a Principal Component Analysis (PCA), and a 2-way factorial Analysis of Variance (ANOVA), Hispanic and female participants were found to report significantly higher learning gains than their counterparts in multiple STEM areas from increased tolerance for obstacles to gains in self confidence. The results of the study suggest Hispanic and female students benefit from paid work experiences in STEM with diverse peers and intentional, supportive mentoring. This research on the Green Teams Program provides insight into how this approach positively impacts STEM education of individuals from traditionally underrepresented groups in STEM. The findings may help to further guide the development of the Green Teams Program and the adoption of paid, interdisciplinary, team-based, experiential learning and internship experiences in additional academic STEM settings. 

The Target Absorbers for Neutrals (TANs) represent one of the most radioactive regions in the Large Hadron Collider. Seven 40cm long fused silica rods with different dopant specifications, manufactured by Heraeus, were irradiated in one of the TANs located around the ATLAS experiment by the Beam RAte of Neutrals (BRAN) detector group. This campaign took place during Run 2 data taking, which occurred between 2016 and 2018. This paper reports a complete characterization of optical transmission per unit length of irradiated fused silica materials as a function of wavelength (240 nm–1500 nm), dose (up to 18 MGy), and level of OH and H2 dopants introduced in the manufacturing process. The dose delivered to the rods was estimated using Monte Carlo simulations performed by the CERN FLUKA team. 

The co-existence of rats and humans in urban environments has long been a cause for concern regarding human health because of the potential for rats to harbor and transmit disease-causing pathogens. Here, we analyze whole-genome sequence (WGS) data from 41 Escherichia coli isolates collected from rat feces from 12 locations within the city of Chicago, IL, United States to determine the potential for rats to serve as a reservoir for pathogenic E. coli and describe its population structure. We identified 25 different serotypes, none of which were isolated from strains containing significant virulence markers indicating the presence of Shiga toxin-producing and other disease-causing E . coli . Nor did the E. coli isolates harbor any particularly rare stress tolerant or antimicrobial resistance genes. We then compared the isolates against a public database of approximately 100,000 E. coli and Shigella isolates of primarily food, food facility, or clinical origin. We found that only one isolate was genetically similar to genome sequences in the database. Phylogenetic analyses showed that isolates cluster by serotype, and there was little geographic structure (e.g., isolation by distance) among isolates. However, a greater signal of isolation by distance was observed when we compared genetic and geographic distances among isolates of the same serotype. This suggests that E. coli serotypes are independent lineages and recombination between serotypes is rare. 

Abstract Frozen winters define life at high latitudes and altitudes. However, recent, rapid changes in winter conditions have highlighted our relatively poor understanding of ecosystem function in winter relative to other seasons. Winter ecological processes can affect reproduction, growth, survival, and fitness, whereas processes that occur during other seasons, such as summer production, mediate how organisms fare in winter. As interest grows in winter ecology, there is a need to clearly provide a thought-provoking framework for defining winter and the pathways through which it affects organisms. In the present article, we present nine maxims (concise expressions of a fundamentally held principle or truth) for winter ecology, drawing from the perspectives of scientists with diverse expertise. We describe winter as being frozen, cold, dark, snowy, less productive, variable, and deadly. Therefore, the implications of winter impacts on wildlife are striking for resource managers and conservation practitioners. Our final, overarching maxim, “winter is changing,” is a call to action to address the need for immediate study of the ecological implications of rapidly changing winters. 

A search is presented for an extended Higgs sector with two new particles, $X$and $\varphi$, in the process $X\to \varphi \varphi \to \left(\gamma \gamma \right)\left(\gamma \gamma \right)$. Novel neural networks classify events with diphotons that are merged and determine the diphoton masses. The search uses LHC proton-proton collision data at $\sqrt{s}=13\mathrm{TeV}$collected with the CMS detector, corresponding to an integrated luminosity of $138{\mathrm{fb}}^{-1}$. No evidence of such resonances is seen. Upper limits are set on the production cross section for $m_X$between 300 and 3000 GeV and $m_{\varphi }/m_X$between 0.5% and 2.5%, representing the most sensitive search in this channel. © 2025 CERN, for the CMS Collaboration2025CERN 

A<sc>bstract</sc> A measurement is performed of Higgs bosons produced with high transverse momentum (p_T) via vector boson or gluon fusion in proton-proton collisions. The result is based on a data set with a center-of-mass energy of 13 TeV collected in 2016–2018 with the CMS detector at the LHC and corresponds to an integrated luminosity of 138 fb⁻¹. The decay of a high-p_THiggs boson to a boosted bottom quark-antiquark pair is selected using large-radius jets and employing jet substructure and heavy-flavor taggers based on machine learning techniques. Independent regions targeting the vector boson and gluon fusion mechanisms are defined based on the topology of two quark-initiated jets with large pseudorapidity separation. The signal strengths for both processes are extracted simultaneously by performing a maximum likelihood fit to data in the large-radius jet mass distribution. The observed signal strengths relative to the standard model expectation are$$ {4.9}_{-1.6}^{+1.9} $$ ${4.9}_{-1.6}^{+1.9}$and$$ {1.6}_{-1.5}^{+1.7} $$ ${1.6}_{-1.5}^{+1.7}$for the vector boson and gluon fusion mechanisms, respectively. A differential cross section measurement is also reported in the simplified template cross section framework. 

Abstract Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.