Middle East Studies Association of North America

Directional permeability variations, which govern directional fluid flow in porous media with anisotropy, are important to accurately predict flow behavior, reactive transport, and fluid–solid interactions for various processes such as enhanced geothermal systems, energy storage devices, and biological systems. However, the intricate architecture of porous media makes it difficult to predict directional permeabilities. Here, we present a novel machine learning (ML) framework, AnisONet, built upon an integration of a convolutional neural network, Swin transformer, and the deep operator network architecture, designed to predict anisotropic permeability and upscale predictions to larger spatial domains. First, AnisONet was evaluated with three classes of two-dimensional (2D) porous media, including synthetic circular and elliptical grains and natural sandstone grains from micro-computed tomography images. A lattice Boltzmann model (LBM) was used to calculate directional permeabilities at every 10° angle, producing 19 data points per image of porous media. AnisONet is then trained to predict permeability as a function of rotation angle. AnisONet showed strong predictive capability of directional permeability. Second, we tested our model for five upscaling cases with a large image size in the finite-element method (FEM) for 2D Darcy flow with various permeability tensor construction methods. Overall, upscaled permeability tensors in FEM simulations produce a reasonably good match with LBM results, highlighting the importance of selecting appropriate tensor formation strategies for accurate permeability upscaling. AnisONet, as a directional permeability estimator, could be further developed for more complex geometries, with the potential to develop a foundational ML model for various applications in porous media.

ABSTRACT Compliance with health safety protocols is important for protecting public health, particularly in agricultural sectors where disease outbreaks can disrupt production and market access. Despite its economic significance, we know little about what drives protocol compliance. We examine the relationship between individuals' objective and self‐assessed knowledge and their compliance with safety measures in the context of COVID‐19. We find that greater opposition to COVID‐19 safety protocols is associated with lower objective knowledge and inflated self‐assessed knowledge, indicating overconfidence. Addressing cognitive biases could make public health interventions more effective, with important implications for agricultural settings where resilience depends on consistent protocol adherence.

The detection of founder pathogenic variants, those observed in high frequency only in a group of individuals with increased inter-relatedness, can help improve delivery of health care for that community. We identified 16 groups with shared ancestry, based on genomic segments that are shared through identity by descent (IBD), in New York City using the genomic data of 25,366 residents from the All Of Us Research Program and the Mount Sinai BioMe biobank. From these groups we defined 7 as founder populations, mostly communities currently under-represented in medical genomics research, such as Puerto Rican and Garifuna. The enrichment analysis of ClinVar pathogenic or likely pathogenic (P/LP) variants in each group identified 201 of these damaging variants across the seven founder populations. We confirmed disease-causing variants previously reported to occur at increased frequencies in Ashkenazi Jewish and Puerto Rican genetic ancestry groups, but most of the damaging variants identified have not been previously associated with any such founder populations, and most of these founder populations have not been described to have increased prevalence of the associated rare disease. Twenty-two of 47 variants meeting Tier 2 prenatal screening criteria (1/100 carrier frequency within these founder groups) have never previously been reported. We show how population structure studies can provide insights into rare diseases disproportionately affecting under-represented founder populations, delivering a health care benefit but also a potential source of stigmatization of these communities, who should be part of the decision-making about implementation into health care delivery.

Abstract The particle אשר has long been treated as a relative pronoun instead of as a particle of relation. Lexicons and grammars label אשר as a relative pronoun, and consequently the reader is preconditioned to treat each occurrence as such. Yet the contextual usage of the particle serves a broad set of functions. At its core the particle establishes connectivity between clauses and phrases while informing the manner and tenor of relation and restriction. This in turn influences the punctuation (if any) that is best suited for the translation. The particle is therefore hypersensitive to the literary content, the narrative progression, and the conceptual milieus of the biblical author(s). In this article, I examine the two occurrences of אשר in Lev 10:1–3 (with new translations of Lev 10:1 and 3) as a case study of its relational and jurisprudential significance. The study reveals that the particle serves a key role in such contexts by denoting contrast, reason, coextensivity, sequentiality, temporality, and referential unity.

It is said that Richard Feynman had the words "What I cannot create, I do not understand," written on his blackboard at the time of his death.

Abstract In the United States, truck rates for perishable food, the per‐mile rate charged for trucking services to move perishable food from farms to stores, rose substantially in the post‐COVID‐19 pandemic era. We argue that rising truck rates is a signal of a broader shortage of truckers, but the connection between labor shortages, rising truck rates, and a lack of trucking services has yet to be established empirically. In this paper, we develop an empirical examination based on an equilibrium job search, matching, and bargaining framework in which we estimate the role of labor shortages in accelerating driver‐wage growth, and truck rates for agricultural products. We estimate the model by combining US Bureau of Census Current Population Survey data on truck driver wages with USDA‐Agricultural Marketing Service Service data on truck rates to establish the linkage between trucker supply and the demand for trucking services. We find that the COVID‐19 pandemic was responsible for a rise in for‐hire trucker wages of some , a rise in average truck rates of nearly and that the gap between trucker‐job openings and successful matches explains a significant, but small, rise in truck rates.

Abstract Stereolithography has been used to create ceramic parts with complex geometry that is difficult to achieve with conventional fabrication techniques. This study used stereolithography to print silica honeycomb structures with a commercial Formlabs Form2 printer. The printed samples were sintered at different temperatures, and the print shape was retained up to 1300°C, but significant distortion from partial melting occurred at 1400°C. Higher sintering temperatures lead to more shrinkage, but it is non‐uniform among directions, with the open cell plane shrinking more than the dense plane of the sample. As expected, the density of samples also increases with the sintering temperature. At higher sintering temperatures, there is an increase in cristobalite and a decrease in quartz, tridymite, and amorphous silica. Regarding mechanical properties, the out‐of‐plane compressive strength is approximately one order of magnitude higher than the in‐plane compressive strength. When compressed along the out‐of‐plane direction, the samples sintered at lower temperatures surprisingly exhibit higher strength, which is explained by the micro‐cracking mechanism. As expected, the samples sintered at higher temperatures display higher strength when compressed along the in‐plane direction.

DesignCross-sectional studyCase selectionConsecutive patient charts (n = 220) at Guy’s Dental Hospital between April 2018 and April 2020 were included. The inclusion criteria were adults ≥18 years with a diagnosis of periodontitis (localized or generalized, all stages and grades) and who have received professional mechanical plaque removal (PMPR) by periodontology graduate students. Data of periodontal indices before and after PMPR (6–20 weeks) were also needed to be available. Exclusion criteria included uncontrolled diabetes, pregnancy, medications attributed to drug induced overgrowth, among others.Data analysisThis retrospective study evaluated the response to periodontal treatment in e-cigarette users and they compared the outcomes to non-smokers, former and current smokers. The primary outcome to evaluate the response to periodontal therapy was ‘need for surgery’. This was defined by the authors as the number of sextants with ≥2 non-adjacent sites with probing depth (PD) ≥ 5 mm after PMPR. Secondary outcomes included periodontal parameters such as number of sextants with ≥1 site with PD ≥ 5 mm, PD, clinical attachment level (CAL), bleeding on probing, recession, and plaque scores.ResultsE-cigarette users and current smokers had similar poorer clinical response to periodontal therapy. Analysis revealed e-cigarette users had more sextants with ‘need for surgery’ as the primary outcome. Pocket closure outcome (PD ≤ 4 mm with no bleeding on probing) were highest in nonsmokers (77.1%), followed by former smokers (74.9%), current smokers (69.4%), and e-cigarette users (66.6%).ConclusionsE-cigarette users showed less than beneficial response to periodontal therapy compared to non-smokers, who had the best outcome overall.