Michigan State University

This article investigates the impact of the semiconformal curvature tensor's symmetry on the base and fiber manifolds of a warped product manifold. It establishes that the fiber manifold of a warped product manifold has a constant sectional curvature, whereas the base manifold is semiconformally symmetric. Furthermore, the article derives the specific forms of the semiconformal curvature tensor for both the base and fiber manifolds. Also, it is demonstrated that a semiconformally symmetric (flat) GRW space-time is a perfect fluid space-time and exhibits an irrotational velocity vector field.

The excessively high temperature poses a significant risk to battery health, accelerating degradation and causing damage. Despite the recognized importance of battery thermal management (BTM), numerous studies in this domain often overlook the distinct timescales associated with vehicle and battery thermal dynamics. This oversight can compromise the efficacy and cost-effectiveness of BTM strategies in efficiently controlling battery temperature. This study proposes a novel predictive battery thermal and energy management ( p -BTEM) strategy for connected and automated electric vehicles. The p -BTEM leverages a cloud-enabled predictive control framework to synthesize the look-ahead constant and time-varying factors, e.g., vehicle, road, and traffic information. This synthesis aims to achieve global optimization of battery temperature in the Cloud while enabling local adaptations for vehicle acceleration and compressor power on the Vehicle End. This approach ensures proactive and economical regulation of battery temperature, especially in high temperature conditions, thereby maintaining the battery within optimal temperature ranges and reducing energy consumption in dynamic traffic scenarios. To assess the effectiveness of the p -BTEM, representative route simulations are conducted utilizing real-world data. The results reveal the exceptional performance of the p -BTEM in reducing battery cooling energy when compared to two benchmark strategies, with a minimum improvement of 8.58% and 10.31%, respectively. Moreover, the sensitivity analysis is performed to elaborate on the p -BTEM under the influence of traffic, communication, and algorithmic factors.

We report on the structural, thermal, linear, and ultrafast third-order nonlinear optical (NLO) properties of two novel anthracene chalcones: (2E)-1-(anthracen-9-yl)-3-(5-methylthiophen-2-yl)prop-2-en-1-one (5ML2SANC) and (2E)-1-(9-anthryl)-3-(2,4,5-trimethoxyphenyl)prop-2-en-1-one (245TMANC). The chalcones were synthesized by Claisen-Schmidt condensation reaction, and the single crystals were grown by the solvent evaporation method. The molecular structure was confirmed by FTIR and NMR spectroscopy, while the crystal structure was determined using the single crystal XRD. Both crystals belong to centrosymmetric monoclinic crystal system with space group P21/n. The Hirshfeld surface was analyzed to understand intermolecular interactions, and the band structures - including HOMO-LUMO levels, excited state energies, GCRDs and MEPs-were studied using DFT. The ultrafast third-order NLO properties were investigated by Z-scan and degenerate four-wave mixing (DFWM) techniques using Ti: Sapphire amplifier laser delivering ~50 fs pulses at 800 nm (1 kHz, ~4 mJ, 2 W). Two-photon absorption, positive nonlinear refraction, optical limiting and optical switching behaviors were observed by Z-scan measurements. The time-resolved DFWM show that the decay time of 5ML2SANC is ~127 fs, while for 245TMANC it is ~142 fs. The second hyperpolarizability (γ) measured by Z-scan, DFWM and the estimations from the DFT theory are found to be in good agreement (~10􀀀 34 esu). The ultrafast optical response, significant NLO properties and thermal stability of the synthesized chalcones demonstrate their potential suitability in optical limiting and switching applications.

Correction for ‘Reinvestigation of Passerini and Ugi scaffolds as multistep apoptotic inducers via dual modulation of caspase 3/7 and P53-MDM2 signaling for halting breast cancer’ by Mohammed Salah Ayoup et al., RSC Adv., 2023, 13, 27722–27737, https://doi.org/10.1039/d3ra04029a.

Data‐enabled predictive control (DeePC) is a data‐driven control algorithm that utilizes data matrices to form a non‐parametric representation of the underlying system, predicting future behaviors and generating optimal control actions. DeePC typically requires solving an online optimization problem, the complexity of which is heavily influenced by the amount of data used, potentially leading to expensive online computation. In this article, we leverage deep learning to propose a highly computationally efficient DeePC approach for general nonlinear processes, referred to as Deep DeePC. Specifically, a deep neural network is employed to learn the DeePC vector operator, which is an essential component of the non‐parametric representation of DeePC. This neural network is trained offline using historical open‐loop input and output data of the nonlinear process. With the trained neural network, the Deep DeePC framework is formed for online control implementation. At each sampling instant, this neural network directly outputs the DeePC operator, eliminating the need for online optimization as conventional DeePC. The optimal control action is obtained based on the DeePC operator updated by the trained neural network. To address constrained scenarios, a constraint handling scheme is further proposed and integrated with the Deep DeePC to handle hard constraints during online implementation. The efficacy and superiority of the proposed Deep DeePC approach are demonstrated using two benchmark process examples.

Farmers markets and public health organisations aim to improve access to fresh produce for low-income consumers. While recent efforts to expand the use of food assistance benefits at farmers markets support this goal, persistent barriers related to transportation, convenience, price, exclusivity, and administrative burden still limit low-income participation at these venues. Mobile farmers markets, which bring produce directly to the customer, aim to address these barriers. But research on the effectiveness of mobile markets, especially from the customer perspective, is limited. Our project, a partnership with a local non-profit, explores if and how a mobile farmers market in the Rust Belt Midwest reduces barriers to farmers market access for low-income and minority consumers. Our data demonstrates a strong tie between market activities and customer wellbeing. Staff and customer interviews and participant observation show that the mobile market effectively alleviates many entrenched barriers to farmers market access for low-income and minority customers. Like many venues, though, this market also still struggles with the logistics of administering food assistance benefits. Our findings illuminate strategies for improving fresh food access for low-income and minority consumers that are relevant for other programmes and contexts. ARTICLE HISTORY

Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine-grained formulation for IFDL representation learning. Specifically, we first represent forgery attributes of a manipulated image with multiple labels at different levels. Then, we perform fine-grained classification at these levels using the hierarchical dependency between them. As a result, the algorithm is encouraged to learn both comprehensive features and the inherent hierarchical nature of different forgery attributes, thereby improving the IFDL representation. In this work, we propose a Language-guided Hierarchical Fine-grained IFDL, denoted as HiFi-Net++. Specifically, HiFi-Net++ contains four components: multi-branch feature extractor, language-guided forgery localization enhancer, as well as classification and localization modules. Each branch of the multi-branch feature extractor learns to classify forgery attributes at one level, while localization and classification modules segment the pixel-level forgery region and detect image-level forgery, respectively. In addition, the language-guided forgery localization enhancer (LFLE), containing image and text encoders learned by contrastive language-image pre-training (CLIP), is used to further enrich the IFDL representation. LFLE takes specifically designed texts and the given image as multi-modal inputs and then generates the visual embedding and manipulation score maps, which are used to further improve HiFi-Net++ manipulation localization performance. Lastly, we construct a hierarchical fine-grained dataset to facilitate our study. We demonstrate the effectiveness of our method on 8 different benchmarks for both tasks of IFDL and forgery attribute classification. Our source code and dataset can be found: github.com/CHELSEA234/HiFi-IFDL.

We present the application of the ACOT and S-ACOT general mass variable flavor number schemes to proton-proton collisions with particular attention to the production of final states with at least one heavy quark. Subtraction and residual heavy-quark parton distribution functions are introduced to facilitate the implementation of this scheme at higher orders in perturbative QCD. The calculation of Z-boson hadronic production with at least one b jet beyond the lowest order in QCD is considered for illustration purposes.

Most states now allow legal medical or recreational cannabis sales. Researchers believed this shift would reduce the cannabis black-market. However, studies suggest that black-markets have not reduced as expected. One unexplored possibility could be that cannabis dispensary purchasers possess differing characteristics from black-market purchasers, such as criminal involvement. This study uses data from the 2022 National Survey on Drug Use and Health to investigate whether individuals who purchase cannabis from dispensaries are less likely to be involved in the criminal justice system and self-reported offending. Results suggest that cannabis dispensary purchase is negatively associated with self-reported offending, but positively associated with past year parole. The findings highlight the possibility that these may represent two distinct groups of cannabis users.

Virtual meetings, facilitated through videoconferencing or virtual reality, have become a common form of workplace communication. Despite the advantages these meetings offer, enabling collaboration among workers in dispersed locations, the phenomenon of virtual meeting fatigue, commonly referred to as Zoom fatigue, has emerged as a significant concern. This study explores whether facial appearance dissatisfaction, a known contributor to Zoom fatigue, leads to reduced engagement in virtual meeting interactions by mediating the role of Zoom fatigue. Furthermore, this study examines the impact of facial dissatisfaction and Zoom fatigue on virtual meeting engagement cross-culturally, within the contexts of South Korea and the United States. The findings indicate that in the United States, facial dissatisfaction led to a lower level of virtual meeting engagement through the mediating role of Zoom fatigue, while in South Korea, facial dissatisfaction negatively impacted virtual meeting engagement regardless of Zoom fatigue levels. Additionally, we reveal that Zoom fatigue and facial dissatisfaction are more pronounced among U.S. women than U.S. men, but differences were not observed between South Korean women and men. These results underscore the influence of societal competitiveness, as seen in South Korea, on impression management concerns in technology-mediated work environments. We highlight the importance of developing virtual meeting features to mitigate facial dissatisfaction and Zoom fatigue, thereby enhancing engagement in virtual interactions.

The 2024 International Rett Syndrome Foundation (IRSF) Rett Syndrome Scientific Meeting, held in Westminster, Colorado, gathered over 200 researchers and clinicians to discuss advancements in understanding and treating Rett syndrome (RTT). Key topics included MeCP2 biology, neuronal circuitry, therapeutic development, and clinical trial outcomes. The meeting reinforced the importance of collaborative research in unraveling RTT’s complex pathology and advancing treatment approaches. With promising therapeutic candidates in development, the conference underscored a growing hope for effective treatments, offering a path toward improving the quality of life for individuals with Rett syndrome and their families.

Small bodies are capable of delivering essential prerequisites for the development of life, such as volatiles and organics, to the terrestrial planets. For example, empirical evidence suggests that water was delivered to the Earth by hydrated planetesimals from distant regions of the Solar System. Recently, several morphologically inactive near-Earth objects were reported to experience significant nongravitational accelerations inconsistent with radiation-based effects, and possibly explained by volatile-driven outgassing. However, these “dark comets” display no evidence of comae in archival images, which are the defining feature of cometary activity. Here, we report detections of nongravitational accelerations on seven additional objects classified as inactive (doubling the population) that could also be explainable by asymmetric mass loss. A detailed search of archival survey and targeted data rendered no detection of dust activity in any of these objects in individual or stacked images. We calculate dust production limits of ∼ 10, 0.1 , and 0.1 kg s − 1 for 1998 FR 11 , 2001 ME 1 , and 2003 RM with these data, indicating little or no dust surrounding the objects during the observations. This set of dark comets reveals the delineation between two distinct populations: larger, “outer” dark comets on eccentric orbits that are end members of a continuum in activity level of comets, and smaller, “inner” dark comets on near-circular orbits that could signify a different different population. These objects may trace various stages in the life cycle of a previously undetected, but potentially numerous, volatile-rich population that may have provided essential material to the Earth.

Vegetation plays a crucial role in coastal dune building. Species‐specific plant characteristics can modulate sediment transport and dune shape, but this factor is absent in most dune building numerical models. Here, we develop a new approach to implement species‐specific vegetation characteristics into a process‐based aeolian sediment transport model. Using a three‐step approach, we incorporated the morphological differences of three dune grass species dominant in the US Pacific Northwest coast (European beachgrass Ammophila arenaria, American beachgrass A. breviligulata, and American dune grass Leymus mollis) into the model AeoLiS. First, we projected the tiller frontal area of each grass species onto a high resolution grid and then re‐scaled the grid to account for the associated vegetation cover for each species. Next, we calibrated the bed shear stress in the numerical model to replicate the actual sand capture efficiency of each species, as measured in a previously published wind tunnel experiment. Simulations were then performed to model sand bedform development within the grass canopies with the same shoot densities for all species and with more realistic average field densities. The species‐specific model shows a significant improvement over the standard model by (a) accurately simulating the sand capture efficiency from the wind tunnel experiment for the grass species and (b) simulating bedform morphology representative of each species' characteristic bedform morphology using realistic field vegetation density. This novel approach to dune modeling will improve spatial and temporal predictions of dune morphologic development and coastal vulnerability under local vegetation conditions and variations in sand delivery.

Heightened anti-Arab/Middle Eastern and North African (MENA) xenophobia in the United States (US) coupled with the addition of a MENA category on the next US Census call into attention the health needs of this minoritized population. Targeted research is needed to better understand the factors that influence Arab/MENA American participation in US-based health research and health care. A novel qualitative interview guide was constructed to better understand the health research experiences, health care experiences and needs of Arab/MENA patients nationally. Patients were recruited through the Arab American Health Network Alliance (AAHNA) community connections. Semi-structured interviews were conducted virtually in English and Arabic, and qualitative data was interpreted through iterative thematic analysis using inductive reasoning. A total of seventeen interviews (n = 17) were completed (14 in English, 3 in Arabic). Notably, the majority identified as female (82%) and have resided in the US for 18 years or longer (53%). Three main themes were identified (1) Individual-level Comfortability and Access to Research Participation, (2) Advancing Community Health Outcomes and Participation, and (3) Structural Barriers as Drivers of Health Disparities. The health research and health care experiences explored in this project have the potential of informing future inquiries on Arab/MENA American health. For instance, we suggest building community trust, providing equitable compensation and support, increasing health workforce diversity, and advocating for affordable health care, all to improve Arab/MENA patient participation in health research.

Politicians are increasingly subjected to violence, both online and offline. Recent studies highlight a gendered pattern to this violence. But, as societies diversify and minorities increasingly hold political office, we have yet to assess whether members of these groups face disproportionate levels of violence. Our research investigates levels and types of violence against immigrant background politicians in Sweden, where over one-third is either foreign-born or has a foreign-born parent, using a unique three-wave survey ( N=23,000 ) on Swedish elected officials. Across every form of violence examined, politicians with immigrant backgrounds report experiencing significantly more physical and psychological violence than their counterparts. These experiences are not without political consequence: immigrant background politicians, and among them especially women, are significantly more likely than their counterparts to consider exiting politics due to harassment. Together, these findings suggest that violence may be driving this already underrepresented group of immigrant background politicians out of office.

Analyzing human genomic data from biobanks and large-scale genetic evaluations often requires fitting models with a sample size exceeding the number of DNA markers used (n > p). For instance, developing Polygenic Scores (PGS) for humans and genomic prediction for genetic evaluations of agricultural species may require fitting models involving a few thousand SNPs using data with hundreds of thousands of samples. In such cases, computations based on sufficient statistics are more efficient than those based on individual genotype-phenotype data. Additionally, software that admits sufficient statistics as inputs can be used to analyze data from multiple sources jointly without the need to share individual genotype-phenotype data. Therefore, we developed functionality within the BGLR R-package that generates posterior samples for Bayesian shrinkage and variable selection models from sufficient statistics. In this article, we present an overview of the new methods incorporated in the BGLR R-package, demonstrate the use of the new software through simple examples, provide several computational benchmarks, and present a real-data example using data from the UK-Biobank, All of Us, and the HCHS/SOL cohort demonstrating how a joint analysis from multiple cohorts can be implemented without sharing individual genotype-phenotype data, and how a combined analysis can improve the prediction accuracy of PGS for Hispanics--a group severely underrepresented in GWAS data.