Lehigh University

Background and Aims Students with intellectual disability benefit from high‐quality instruction in reading and mathematics. Methods This Theoretical Paper outlines the need for effective, evidence‐based instructional practices for this population and the potential for observation research to inform such advancements. We report our systematic process of adapting two widely used observation tools initially developed for students with learning disabilities, to address content and instructional practices relevant to classrooms serving elementary students with intellectual disability. Results Our revised observation tools facilitated more inclusive data collection, taking into account the needs and goals of students with intellectual disability, in addition to their peers for whom the original tool was designed. Conclusions We offer guidance for researchers and practitioners to make similar adaptations when using research tools intended for other populations or instructional settings.

This paper introduces an on-demand collaborative sensing scheme for Industrial Internet of Things (IIoT) sensors in time-varying sensing environments, aiming to optimize the sensing performance by effectively allocating communication resources for sensory data sharing. Particularly, we propose a novel digital twins (DTs)-empowered resource allocation solution to facilitate scalable and flexible collaborative sensing. First, DTs create mathematical models using real-time network data to characterize the dynamic resource demands in collaborative sensing. Second, the performance of mathematical models in DTs is evaluated through data-driven methods. Building on our DT design, we propose a joint collaborative sensing and DT management scheme to optimize the resource allocation for sensory data sharing and DT operation. Furthermore, we develop a DT evaluation method featuring a variational autoencoder to evaluate the accuracy of DTs and enable closed-loop DT-based resource allocation. Numerical results demonstrate the effectiveness of our proposed collaborative sensing scheme in optimizing the sensing performance for all sensors.

Two-dimensional layered materials, such as transition metal dichalcogenides (TMDs), possess an intrinsic van der Waals gap at the layer interface, allowing for remarkable tunability of the optoelectronic features via external intercalation of foreign guests such as atoms, ions, or molecules. Herein, we introduce a high-throughput, data-driven computational framework for the design of novel quantum materials derived from intercalating planar conjugated organic molecules into bilayer transition metal dichalcogenides and dioxides. By combining first-principles methods, material informatics, and machine learning, we characterize the energetic and mechanical stability of this new class of materials and identify the fifty (50) most stable hybrid materials from a vast configurational space comprising ∼105 materials, employing intercalation energy as the screening criterion.

This study investigated the optimal feedback intervals for tasks of varying difficulty levels in online testing and whether task difficulty moderates the effect of feedback intervals on student performance. A pre-experimental study with 36 students was conducted to determine the delayed time for providing feedback based on student behavioral data. A formal experiment with 80 students was then conducted to explore the joint effects of feedback interventions and task difficulty on student performance improvement. The findings revealed that feedback conditions had no significant impact on student learning gains in easy or difficult tasks. However, for medium difficulty tasks, feedback delays of 23 and 30 s led to greater academic improvement compared to immediate feedback, while feedback with a 35-s delay resulted in worse performance. Overall, the study highlights the importance of considering task difficulty when determining optimal feedback intervals and suggests that well-timed feedback can significantly enhance student learning, especially for tasks of moderate complexity. This challenges the common practice of providing immediate feedback, which may not always be optimal. Moreover, the findings of this study provide valuable insights for educators and instructional designers when designing online assessments and feedback mechanisms.

Considering the role of human interactions in infectious disease outbreaks and cooperation in mitigating natural disasters consequences, ecological threats to human survival have been among proposed drivers of collectivism. Utilizing established and novel measures of parasite stress and natural disasters, we investigated their association with collectivism in a large sample of countries (N = 188). Linear mixed-effect models indicated that after controlling for national wealth, neither natural disasters nor infectious disease can predict collectivism scores. Null results were consistent across different measures of threats, suggesting that previous findings can be attributed to small, non-representative samples of cultures. When universal patterns are a major concern, drawing conclusions based on small, nonrepresentative subsets of cultures risks promoting unreliable findings. Future cross-cultural research will benefit from data-driven exploratory methods to uncover factors previously unexamined in the theory-driven studies of collectivism.

Background Substance use induces large economic and societal costs in the U.S. Understanding the change in substance use behaviors of persons who use drugs (PWUDs) over time, therefore, is important in order to inform healthcare providers, policymakers, and other stakeholders toward more efficient allocation of limited resources to at-risk PWUDs. Objective This study examines the short-term (within a year) behavioral changes in substance use of PWUDs at the population and individual levels. Methods 237 PWUDs in the Great Plains of the U.S. were recruited by our team. The sample provides us longitudinal survey data regarding their individual attributes, including drug use behaviors, at two separate time periods spanning 4-12 months. At the population level, we analyze our data quantitatively for 18 illicit drugs; then, at the individual level, we build interpretable machine learning logistic regression and decision tree models for identifying relevant attributes to predict, for a given PWUD, (i) which drug(s) they would likely use and (ii) which drug(s) they would likely increase usage within the next 12 months. All predictive models were evaluated by computing the (averaged) Area under the Receiver Operating Characteristic curve (AUROC) and Area under the Precision-Recall curve (AUPR) on multiple distinct sets of hold-out sample. Results At the population level, the extent of usage change and the number of drugs exhibiting usage changes follow power-law distributions. At the individual level, AUROC’s of the models for the top-4 prevalent drugs (marijuana, methamphetamines, amphetamines, and cocaine) range 0.756-0.829 (+2.88-7.66% improvement with respect to baseline models using only current usage of the respective drugs as input) for (i) and 0.670-0.765 (+4.34-18.0%) for (ii). The corresponding AUPR’s of the said models range 0.729-0.947 (+2.49-13.6%) for (i) and 0.348-0.618 (+26.9-87.6%) for (ii). Conclusion The observed qualitative changes in short-term substance usage and the trained predictive models for (i) and (ii) can potentially inform human decision-making toward efficient allocation of appropriate resources to PWUDs at highest risk.

Volcanic activity has been shown to affect Earth's climate in a myriad of ways. One such example is that eruptions proximate to surface ice will promote ice melting. In turn, the crustal unloading associated with melting an ice sheet affects the internal dynamics of the underlying magma plumbing system. Geochronologic data from the Andes over the last two glacial cycles suggest that glaciation and volcanism may interact via a positive feedback loop. At present, accurate sea‐level predictions hinge on our ability to forecast the stability of the West Antarctic Ice Sheet, and thus require consideration of two‐way subglacial volcano‐deglaciation processes. The West Antarctic Ice Sheet is particularly vulnerable to collapse, yet its position atop an active volcanic rift is seldom considered. Ice unloading deepens the zone of melting and alters the crustal stress field, impacting conditions for dike initiation, propagation, and arrest. However, the consequences for internal magma chamber dynamics and long‐term eruption behavior remain elusive. Given that unloading‐triggered volcanism in West Antarctica may contribute to the uncertainty of ice loss projections, we adapt a previously published thermomechanical magma chamber model and simulate a shrinking ice load through a prescribed lithostatic pressure decrease. We investigate the impacts of varying unloading scenarios on magma volatile partitioning and eruptive trajectory. Considering the removal of km‐thick ice sheets, we demonstrate that the rate of unloading influences the cumulative mass erupted and consequently the heat released into the ice. These findings provide fundamental insights into the complex volcano‐ice interactions in West Antarctica and other subglacial volcanic settings.

Purpose Parent engagement is a critical component of optimizing services for young children with disabilities, including those with language disorders. Without training, however, many parents may lack the knowledge and skills to effectively facilitate their children's language development during the essential early childhood years. The Parents Plus intervention was designed to support parents, through online training and coaching, in using focused stimulation, an evidence-based strategy for fostering early language development. Method Thirty-one parents and their children with developmental language disorder participated in a small-scale randomized controlled trial to provide a preliminary test of Parents Plus. Sixteen parent–child dyads completed the Parents Plus intervention, while 15 parent–child dyads were in the control condition. Results Findings indicate that Parents Plus shows promise in improving children's vocabulary and morphosyntactic skills. Additionally, Parents Plus emerged as a socially valid approach, with parents reporting that its goals, content, procedures, and outcomes were acceptable. Conclusion Implications for education and directions for future research are discussed.

The distributions of anthozoan corals are undercharacterized due to their wide bathymetric ranges, occurrences in remote locales, and difficulties of identification from morphology alone. Environmental DNA (eDNA) sequencing promises to be a noninvasive strategy to complement conventional approaches for mapping and monitoring the distribution and biodiversity of coral communities. Primers for eDNA metabarcoding have been designed to amplify nuclear and mitochondrial DNA barcodes in shallow scleractinians and mitochondrial MutS in deep-sea octocorals. However, a comprehensive method for eDNA metabarcoding of all anthozoan corals, including black corals, has not been developed. We leveraged a sequence database of global coral collections, from shallow water to the deep sea, to design new PCR primers for coral eDNA sequencing that target the 28S rRNA gene ( 28S rDNA ). We tested the performance of these primers by amplifying and sequencing eDNA from water samples collected in the Gulf of Mexico near mesophotic and deep-sea corals that were also imaged, sampled, and sequenced. Sequencing libraries produced using the primers were highly enriched in eDNA from octocorals, black corals and scleractinians, with up to 99.9% of the reads originating from these corals. Further, the 28S barcode amplified using the primers distinguished coral genera and species in many cases, like previously developed methods that target eDNA in only octocorals or scleractinians. We recovered amplicon sequencing variants (ASVs) identical to DNA barcodes derived from Sanger sequencing and genome skimming of corals sampled at the same field sites. This new eDNA metabarcoding strategy permits targeted eDNA sequencing of black corals, octocorals, and scleractinians at sites where they co-occur and expands our current toolkit for mapping and monitoring coral communities in shallow coral reefs and the deep sea.

Introduction Physician payments from Intuitive Surgical have increased from 37 million to over 53 million per year since 2018. The study was completed to determine the accuracy of conflict of interest (COI) statements and the influence of industry payments on the valuation of the robotic platform. Methods PubMed and Medline search for “robotic, robotic assisted” and “bariatric, Gastric Bypass, Sleeve Gastrectomy, Biliopancreatic Diversion, and Single Anastomosis Duodeno-Ileal Bypass”. Manuscripts on robotic bariatric surgery with a US author with an electronic publication (EPub) date between 2018 and 2022 were included. Manuscripts were reviewed for disclosure of COI. The manuscripts were reviewed by two reviewers. The Introduction, Results, and Discussion/Conclusion were scored as Robotic Unfavorable, Neutral, or Robotic Favorable. https://OpenPaymentsData.CMS.gov was reviewed for physician payments 1 year prior and 1 year following the EPub date. Results Robotic favorable manuscripts were significantly less likely to have an adequate COI. Authors of robotic favorable manuscripts were significantly more likely to have a COI. Authors of robotic favorable manuscripts had significantly a higher Intuitive physician compensation. In addition, authors of robotic favorable manuscripts were significantly more likely to have an increase in the amount of compensation by Intuitive Surgical the following year. Conclusion Our findings suggest that Intuitive open payments have significantly influenced favorable reports in robotic bariatric literature. The submission of open payments data, to include compensation amounts should be required for manuscript publication or acceptance to surgical conferences.

Narcofeminism Storyshare is an iterative storyshare model developed by NC Survivors Union, a harm reduction and advocacy organization led by directly impacted people, that uses autobiographical story development by women and gender diverse people who use drugs to disrupt stigmatizing societal narratives, achieve healing for individuals and communities, and spark organizing and structural change. We describe the theoretical frameworks behind Narcofeminism Storyshare, including the international movement of narcofeminism, narrative theory, and stigma reduction theory. We detail the components of the Narcofeminism Storyshare process–structural analysis conversations, group editing, one-on-one peer editing, grounding our stories in the evidence base, completing various types of story exercises that Narcofeminism Storyshare has developed, the creation of trauma-informed spaces, and the utilization of the stories in advocacy and trainings for health and social service professionals. Finally, we conclude by describing lessons learned while developing the model that may be useful for other organizations working in this capacity as well as by outlining the model's unique contribution to the field.

Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks across different data modalities. A PFM (e.g., BERT, ChatGPT, GPT-4) is trained on large-scale data, providing a solid parameter initialization for a wide range of downstream applications. In contrast to earlier methods that use convolution and recurrent modules for feature extraction, BERT learns bidirectional encoder representations from Transformers, trained on large datasets as contextual language models. Similarly, the Generative Pretrained Transformer (GPT) method employs Transformers as feature extractors and is trained on large datasets using an autoregressive paradigm. Recently, ChatGPT has demonstrated significant success in large language models, utilizing autoregressive language models with zero-shot or few-shot prompting. The remarkable success of PFMs has driven significant breakthroughs in AI, leading to numerous studies proposing various methods, datasets, and evaluation metrics, which increases the demand for an updated survey. This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, and other data modalities. It covers the basic components and existing pretraining methods used in natural language processing, computer vision, and graph learning, while also exploring advanced PFMs for different data modalities and unified PFMs that address data quality and quantity. Additionally, the review discusses key aspects such as model efficiency, security, and privacy, and provides insights into future research directions and challenges in PFMs. Overall, this survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, and user-friendly interactive ability for artificial general intelligence.

Mammalia comprises a great diversity of diet types and associated adaptations. An understanding of the genomic mechanisms underlying these adaptations may offer insights for improving human health. Comparative genomic studies of diet that employ taxonomically restricted analyses or simplified diet classifications may suffer reduced power to detect molecular convergence associated with diet evolution. Here, we use a quantitative carnivory score—indicative of the amount of animal protein in the diet—for 80 mammalian species to detect significant correlations between the relative evolutionary rates of genes and changes in diet. We have identified six genes— ACADSB , CLDN16 , CPB1 , PNLIP , SLC13A2 , and SLC14A2 —that experienced significant changes in evolutionary constraint alongside changes in carnivory score, becoming less constrained in lineages evolving more herbivorous diets. We further consider the biological functions associated with diet evolution and observe that pathways related to amino acid and lipid metabolism, biological oxidation, and small molecule transport experienced reduced purifying selection as lineages became more herbivorous. Liver and kidney functions show similar patterns of constraint with dietary change. Our results indicate that these functions are important for the consumption of animal matter and become less important with the evolution of increasing herbivory. So, genes expressed in these tissues experience a relaxation of evolutionary constraint in more herbivorous lineages.