Recent ics items

Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks

Wed, 4 Nov 2026 00:00:00 +0000

Electromagnetic field reconstruction is crucial in many applications, including antenna diagnostics, electromagnetic interference analysis, and system modeling. This paper presents a deep learning-based approach for Far-Field to NearField (FF-NF) transformation using Convolutional Neural Networks (CNNs). The goal is to reconstruct near-field distributions from the far-field data of an antenna without relying on explicit analytical transformations. The CNNs are trained on paired farfield and near-field data and evaluated using mean squared error (MSE). The best model achieves a training error of 0.0199 and a test error of 0.3898. Moreover, visual comparisons between the predicted and true near-field distributions demonstrate the model's effectiveness in capturing complex electromagnetic field behavior, highlighting the potential of deep learning in electromagnetic field reconstruction.

Twelve quick tips for AI-assisted coding in science

Mon, 2 Nov 2026 00:00:00 +0000

While AI coding tools have demonstrated potential to accelerate software development, their use in scientific computing raises critical questions about code quality and scientific validity. In this paper, we provide twelve practical tips for AI-assisted coding that balance the capabilities of AI with the demands of scientific and methodological rigor. We address how AI can be leveraged strategically throughout the development cycle with four key themes: problem preparation and understanding, managing context and interaction, testing and validation, and code quality assurance and iterative improvement. These principles serve to emphasize maintaining human agency in coding decisions, establishing robust validation procedures, and preserving the domain expertise essential for methodologically sound research. These tips are intended to help researchers harness AI's transformative potential for faster software development while ensuring that their code meets the standards of reliability,...

A Reconstruct-then-Bootstrap Test for the Sufficiency of Diffusion Processes

Mon, 2 Nov 2026 00:00:00 +0000

Diffusion processes are commonly used in the modeling of time series that exhibit stationarity and Markovianity. However, those two properties do not guarantee that a diffusive process is sufficient for the time series. In this paper, we develop a test for the sufficiency of a diffusion process for an observed time series. To develop the test we capitalize on the Kramers–Moyal (KM) expansion: a Taylor expansion of the integral form of the master equation that describes Markov continuous-time processes. In the idealized case, if the observed data indeed arise from a true diffusion process, then the KM expansion should truncate naturally after the second term. In theory, this means that any higher-order ($$\varvec{\ge } \varvec{3}$$) KM coefficients should be zero. However, in practice, the discrete nature of measurement introduces artificial higher-order KM coefficients, even when the underlying process is truly diffusive. Nonetheless, for genuinely diffusive systems, it is expected...

Robust Bayesian hypothesis testing with the hierarchical EZ-DDM

Mon, 2 Nov 2026 00:00:00 +0000

The EZ-diffusion model (EZ-DDM) uses a method of moments to provide closed-form estimators for the three-parameter drift-diffusion model from summary statistics. In previous work, we showed that using the sampling distributions of these statistics enables the implementation of hierarchical EZ-DDM extensions, supporting scalable Bayesian inference in cognitive psychometrics applications. However, the summary statistics used in EZ-DDM implementations (the mean and variance of the response time distribution) are sensitive to contaminant data points, limiting its utility in real-world applications. To address this, we propose a variation on the EZ-DDM implementation in which the summary statistics are replaced with robust alternatives, substituting mean RT with median RT and RT variance with an estimate derived from the interquartile range. We explore and evaluate the effectiveness of this substitution through simulation studies using a within-subject t test design across varying...

Comment on Shiffrin et al. (2026): Illusions of Replication, Illusions of Truth

Mon, 2 Nov 2026 00:00:00 +0000

Shiffrin et al. argue that scientific practice often produces illusions of understanding; situations in which familiar inferential tools generate misplaced confidence. We describe a related illusion that arises not from statistical misapplications, but from ordinary theory testing itself. Here we highlight a different source of epistemic uncertainty or overconfidence; one that emerges even when statistical tools are used correctly, effect sizes are precisely estimated, findings replicate without controversy, and heterogeneity is explicitly acknowledged within hierarchical models.

Epistemic Disadvantage in Open Science Practices

Mon, 2 Nov 2026 00:00:00 +0000

Open science advocates aim to address epistemic injustice and global power asymmetries in research infrastructures yet underestimate structural vulnerabilities and risks embedded in data sharing practices. Drawing on an ethnographic study of the GCRF South–South Migration Inequality and Development Hub’s (MIDEQ), we argue that even well-intentioned systems, such as the MIDEQ hub, do not reach these vulnerabilities. To capture these overlooked harms, we employ the concept of epistemic disadvantage, a category coextensive with, but separate from epistemic injustice. Epistemic disadvantage includes ethically justified data withholding and misappropriation within consensual systems, which is not captured by existing notions of extractivism as those focus on the monetisation of epistemic resources without regard for the loss incurred by the originating community. To better account for these harms, we draw on two recent epistemic attitudes: the Book of Truths (BoT), which sees scientific...

AAV9-Mimetic Peptides and Electroporation Synergistically Enhance Nanoparticle Transport through the Blood–Brain Barrier

Fri, 30 Oct 2026 00:00:00 +0000

Crossing the blood-brain barrier (BBB) remains a major hurdle in neurotherapeutics delivery. However, adeno-associated virus serotype 9 (AAV9) variants can cross from the bloodstream to accumulate in brain tissue, with their tropism facilitated by surface peptide loops. In this work, we examined the feasibility of grafting AAV9-based peptides (AAV.PHP.eB, AAV.X1, and AAV.CPP.16) onto nanoparticles (NPs) to increase transport through brain endothelial cells. We also evaluated the effects of combining the cell-mediated transcytosis strategy with nanosecond pulsed electric fields (nsPEFs). To test the importance of the AAV9-mimetic peptide structural presentation, we chemically conjugated linear peptides or recombinantly inserted peptides into external loops of a protein NP delivery scaffold (E2). E2 shares similar size, symmetry, and nanostructure features to AAV9 but is based on a non-viral source. Computational modeling, particle sizes, and circular dichroism data showed that...

Charge readout electronics for the DUNE horizontal drift far detector: design and performance in ProtoDUNE-HD

Mon, 26 Oct 2026 00:00:00 +0000

DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon time projection chambers ever built. This detector design calls for custom-built cryogenic front-end electronics to meet its performance requirements. This paper describes the charge readout electronics that will be used in the DUNE horizontal drift (HD) far detector and presents performance results using data from the ProtoDUNE-HD detector, a 770 ton liquid argon time projection chamber operated at the CERN Neutrino Platform in 2024 that served as the final prototype of the DUNE HD design.

Optimization of Antenna Array Configurations Using Deep Learning

Wed, 21 Oct 2026 00:00:00 +0000

We introduce a deep learning-based optimization method that enhances the design of sparse phased array by reducing grating lobes. Our approach begins with a generation of sparse antenna array configurations, efficiently addressing the non-convex challenges and high degrees of freedom in array design. We then employ neural networks, trained on 70,000 and tested on 30,000 configurations, to approximate a non-convex cost function that measures the ratio between the energy of the main lobe and the side lobe level. The approximation is differentiable and allows for the minimization of the cost function by gradient descent with respect to the antenna’s elements coordinates, yielding a new optimized configuration. A custom penalty mechanism is also implemented, integrating various physical and design constraints into our optimization method. The effectiveness of our method is tested on the ten configurations with the lowest initial cost values, showing a further reduction in cost by...

Optimization of Sparse Phased Arrays Using Deep Learning

Wed, 21 Oct 2026 00:00:00 +0000

Antenna arrays enhance communication, sensing, and imaging in fields like wireless communications, radar, and radio astronomy by improving signal quality and range through beamforming and spatial multiplexing. However, they face challenges such as mutual coupling, calibration issues, and increased complexity compared to single antennas. These issues require careful engineering to optimize array performance across applications. We present an optimization technique based on deep learning that improves the design of sparse phased arrays by minimizing grating lobes. This method starts with generating configurations for sparse antenna arrays, effectively tackling the non-convex challenges and the high degrees of freedom involved in array design. We utilize neural networks, trained on 70,000 and tested on 30,000 configurations, to estimate a non-convex cost function that evaluates the energy ratio between the main lobe and side lobe levels. This estimation is differentiable, facilitating...

Sparse Phased Array Optimization Using Deep Learning

Wed, 21 Oct 2026 00:00:00 +0000

Antenna arrays are widely used in wireless communication, radar systems, radio astronomy, and military defense to enhance signal strength, directivity, and interference suppression. We introduce a deep learning-based optimization approach that enhances the design of sparse phased arrays by reducing grating lobes. This approach begins by generating sparse array configurations to address the non-convex challenges and extensive degrees of freedom inherent in array design. We use neural networks to approximate the non-convex cost function that estimates the energy ratio between the main and side lobes. This differentiable approximation facilitates cost function minimization through gradient descent, optimizing the antenna elements' coordinates and leading to an improved layout. Additionally, we incorporate a tailored penalty mechanism that includes various physical and design constraints into the optimization process, enhancing its robustness and practical applicability. We demonstrate...

Online expressions of heat risk: social media posting and social vulnerability in California

Wed, 21 Oct 2026 00:00:00 +0000

Heat waves in California are occurring with increasing frequency, duration, and intensity, largely due to climate change. Furthermore, the burden of extreme heat disproportionately affects various communities both geographically and socioeconomically. As a result, understanding how heat is discussed on social media is critical for informing the design of public health interventions. This study involved a spatiotemporal analysis of heat-related tweets (N = 24,598) posted across California between April and October from 2016 to 2022. Poisson regression models were used to examine county-level counts of heat tweets, with population size included as an offset term in the model while logistic regression models estimated the probability that a heat tweet reflected coping or life impact (LI) themes. A different set of logistic regression models were fit to examine the effect of socio-demographic and housing variables on the likelihood of heat tweets expressing positive and negative sentiment,...

Serum per- and polyfluoroalkyl substances and prevalent heart disease: cross-sectional analysis of the multi-site study

Wed, 7 Oct 2026 00:00:00 +0000

Previous research investigating exposure to per- and polyfluoroalkyl substances (PFAS) and their association with heart disease has found conflicting results, with recent studies reporting null or inverse associations. We estimated associations between serum PFAS and prevalent heart disease in a large, cross-sectional, multi-site study of adults residing in communities in the United States with historic PFAS drinking water contamination. Participants were recruited from communities in eight states (2019–2023), where they completed questionnaires and provided blood samples. PFAS concentrations were measured in serum. Four PFAS detected in > 70% of participants were modeled as continuous variables, and three less-frequently detected PFAS were modeled as binary variables (above/below the limit of detection). Logistic regression models estimated associations between each PFAS and prevalent heart disease based on self-report, adjusting for demographics, smoking and alcohol consumption,...

Phylodynamic analysis reveals disparate transmission dynamics of Mycobacterium tuberculosis-complex lineages in Botswana

Wed, 7 Oct 2026 00:00:00 +0000

Tuberculosis epidemics have traditionally been conceptualized as arising from a single uniform pathogen. However, Mycobacterium tuberculosis-complex (Mtbc), the pathogen causing tuberculosis in humans, encompasses multiple lineages exhibiting genetic and phenotypic diversity that may be responsible for heterogeneity in TB transmission. We analysed a population-based dataset of 1,354 Mtbc whole-genome sequences collected over four years in Botswana, a country with high HIV and tuberculosis burden. We identified Lineage 4 (L4) as the most prevalent (87.4%), followed by L1 (6.4%), L2 (5.3%), and L3 (0.9%). Within L4, multiple sublineages were identified, with L4.3.4 being the predominant sublineage. Phylodynamic analysis revealed L4.3.4 expanded steadily from late 1800s to early 2000s. Conversely, L1, L4.4, and L4.3.2 showed population trajectories closely aligned with the HIV epidemic. Meanwhile, L2 saw rapid expansion throughout most of the 20^th century but declined...

Investigating the Kidney–Gut–Brain Axis in CKD: Uremic Toxins and Brain Microhemorrhages

Fri, 2 Oct 2026 00:00:00 +0000

Alterations of gut microbiota are common in chronic kidney disease (CKD) and contribute to increased uremic toxins including indoxyl sulfate (IS), p-cresyl sulfate (pCS) and trimethylamine N-oxide (TMAO), which are linked to cerebrovascular disease risk. This study examined the kidney-gut-brain axis in CKD mice and in dialysis patients. Male and female mice with adenine-induced CKD were fed a high-amino-acid (HAA) diet to increase precursors of gut-derived uremic toxins. A subgroup of mice received antibiotics in drinking water to suppress gut microbiota and evaluate its role in toxin generation. Behavior tests, gut microbiome composition and brain histology for cerebral microhemorrhages were analyzed. CKD mice had higher serum levels of creatinine, cystatin C and gut-derived toxins, a 2.5-fold increase in brain microhemorrhages, and decreased locomotor activity. The HAA diet significantly increased serum TMAO but not IS and pCS, and all three toxins were reduced by antibiotic...

Technopoly: The Surrender of Culture to Technology

Wed, 30 Sep 2026 00:00:00 +0000

Technopoly: The Surrender of Culture to Technology

What Computers Still Can't Do: A Critique of Artificial Reason

Wed, 30 Sep 2026 00:00:00 +0000

What Computers Still Can't Do: A Critique of Artificial Reason

Virtual Reality as the End of the Enlightenment Project

Wed, 30 Sep 2026 00:00:00 +0000

Virtual Reality as the End of the Enlightenment Project

Wed, 30 Sep 2026 00:00:00 +0000

Virtual Reality as the End of the Enlightenment Project

Zip-Tries: Simple Dynamic Data Structures for Strings

Wed, 23 Sep 2026 00:00:00 +0000

Zip-Tries: Simple Dynamic Data Structures for Strings

On the expansion of Hanoi graphs

Wed, 23 Sep 2026 00:00:00 +0000

The famous Tower of Hanoi puzzle involves moving $n$ discs of distinct sizes from one of $p\geq 3$ pegs (traditionally $p=3$) to another of the pegs, subject to the constraints that only one disc may be moved at a time, and no disc can ever be placed on a disc smaller than itself. Much is known about the Hanoi graph $H_p^n$, whose $p^n$ vertices represent the configurations of the puzzle, and whose edges represent the pairs of configurations separated by a single legal move. In a previous paper, the present authors presented nearly tight asymptotic bounds of $O((p-2)^n)$ and $Ω(n^{(1-p)/2}(p-2)^n)$ on the treewidth of this graph for fixed $p \geq 3$. In this paper we show that the upper bound is tight, by giving a matching lower bound of $Ω((p-2)^n)$ for the expansion of $H_p^n$. 18 pages, 5 figures

Zip-Tries: Simple Dynamic Data Structures for Strings

Wed, 23 Sep 2026 00:00:00 +0000

In this paper, we introduce zip-tries, which are simple, dynamic, memory-efficient data structures for strings. Zip-tries support search and update operations for $k$-length strings in $\mathcal{O}(k+\log n)$ time in the standard RAM model or in $\mathcal{O}(k/\alpha+\log n)$ time in the word RAM model, where $\alpha$ is the length of the longest string that can fit in a memory word, and $n$ is the number of strings in the trie. Importantly, we show how zip-tries can achieve this while only requiring $\mathcal{O}(\log{\log{n}} + \log{\log{\frac{k}{\alpha}}})$ bits of metadata per node w.h.p., which is an exponential improvement over previous results for long strings. Despite being considerably simpler and more memory efficient, we show how zip-tries perform competitively with state-of-the-art data structures on large datasets of long strings. Furthermore, we provide a simple, general framework for parallelizing string comparison operations in linked data structures, which we...

Bicriteria Polygon Aggregation with Arbitrary Shapes

Wed, 23 Sep 2026 00:00:00 +0000

We study the problem of aggregating a set of polygons by covering them with disjoint representative regions, thereby inducing a clustering of the polygons. Equivalently, this can be seen as a fence enclosure problem, where the goal is to surround the polygons with a set of closed curves. Our objective is to minimize a weighted sum of the total area and the total perimeter of the regions, which naturally extends other fencing problems and has applications in geographical information systems. Previously, this objective was only studied in a restricted variant, in which the boundary curves of the regions must be selected from a fixed subdivision of the plane. It is natural to ask whether the problem is still tractable if this restriction is removed, allowing output regions to be bounded by arbitrary curves. We provide a positive answer in the form of an algorithm with runtime Õ(n⁴), where n is the number of input vertices. To achieve this, we fully characterize the optimal...

Hamiltonian cycles in subdivided doubles

Wed, 23 Sep 2026 00:00:00 +0000

The subdivided double construction on 4-regular graphs was used by Potočnik and Wilson to explore semi-symmetric (edge-transitive but not vertex-transitive) graphs, and can be used to construct every semi-symmetric 4-regular graph that contains a pair of twin vertices. We show that (regardless of symmetry) subdivided doubles have another curious property: they have exponentially many Hamiltonian cycles each of which is complementary to another Hamiltonian cycle.

Fast Schulze Voting Using Quickselect

Wed, 23 Sep 2026 00:00:00 +0000

The Schulze voting method aggregates voter preference data using maxmin-weight graph paths, achieving the Condorcet property that a candidate who would win every head-to-head contest will also win the overall election. Once the voter preferences among $m$ candidates have been arranged into an $m\times m$ matrix of pairwise election outcomes, a previous algorithm of Sornat, Vassilevska Williams and Xu (EC '21) determines the Schulze winner in randomized expected time $O(m^2\log^4 m)$. We improve this to randomized expected time $O(m^2\log m)$ using a modified version of quickselect.

Visualizing Treewidth

Wed, 23 Sep 2026 00:00:00 +0000

A witness drawing of a graph is a visualization that clearly shows a given property of a graph.We study and implement various drawing paradigms for witness drawings to clearly show that graphs have bounded pathwidth or treewidth. Our approach draws the tree decomposition or path decomposition as a tree of bags, with induced subgraphs shown in each bag, and with ``tracks'' for each vertex of the graph connecting its copies in multiple bags.Within bags, we optimize the vertex layout to avoid crossings of edges and tracks.We implement a visualization prototype for crossing minimization using dynamic programming for graphs of small width and heuristic approaches for graphs of larger width.We explore the design space for width-witness drawings and investigate drawing styles that render the subgraph for each bag as an arc diagram with one or two pages or as a circular layout with straight-line edges, and we render tracks either with straight lines or with orbital-radial paths.Finally,...

Likelihood specification in simultaneous equation models for discrete data

Wed, 12 Aug 2026 00:00:00 +0000

In this article, we examine the foundations of a rich and diverse literature in economics and derive the likelihood function of simultaneous equation models for discrete data as the invariant distribution of a suitably specified Markov process. This formulation offers a well-defined reduced form of the model and dispenses with the need for controversial recursivity requirements and ad hoc indeterminacy rules. The derivation resolves puzzling paradoxes highlighted in earlier work and shows that the likelihood is unique, proper, coherent, complete, and theoretically grounded in conditional distribution modeling – a framework that has yet to be popularized in economics. We note possible extensions and relevant links with other models, comment on computational issues, and implement the methodology in three empirical applications involving female labor force participation, the interactions between health and wealth, and the interplay between banks’ lending practices and their reliance...

Accessibility through Awareness of Noise Sensitivity Management and Regulation Practices

Thu, 30 Jul 2026 00:00:00 +0000

Noise sensitivity results in a range of experiences, from small annoyances to extreme pain in response to sound. While anyone can experience it, many neurodivergent people report this sensory processing challenge. Novel technologies can help people with noise sensitivity (PWNS) manage their sensitivities. We conducted an interview study with 21 PWNS and 9 caregivers over three months to understand their management and regulation practices and opportunities for technology-mediated support. We found that awareness is a salient factor that influences both management and regulation practices. Based on their awareness, people primarily use avoidance and sound masking approaches or engage in personal coping strategies such as journaling, setting boundaries, and alerting others to regulate. We build on prior research on interdependence, mutual care, and collaborative work to situate the concept of joint awareness to foster collaborative management and regulation for noise sensitivity....

GAI FOR THE CLASSROOM: UNDERSTANDING TEACHER AND TEEN PERSPECTIVES ON GENERATIVE AI INTEGRATION THROUGH FOCUS GROUP INSIGHTS

Thu, 30 Jul 2026 00:00:00 +0000

GAI FOR THE CLASSROOM: UNDERSTANDING TEACHER AND TEEN PERSPECTIVES ON GENERATIVE AI INTEGRATION THROUGH FOCUS GROUP INSIGHTS

Supporting Noise Sensitivity and Emotion Regulation with Children

Thu, 30 Jul 2026 00:00:00 +0000

Children who are noise sensitive may be easily distracted, experience discomfort, and react with negative emotions in the presence of loud or sudden sounds and in environments where they cannot control the noise; this can impact their ability to regulate their emotions. Therefore, implementing effective regulation strategies is necessary to support noise-sensitive children. Assistive technology has been shown to be an effective tool for supporting children and self-regulation. By conducting co-design sessions with noise-sensitive children, we will better understand how we might design an assistive application to support emotion regulation in noise-sensitive children. In this work in progress, we present the initial findings of two pilot co-design sessions and considerations for conducting co-design sessions with children who are noise sensitive.

“Am I trying hard or harder than others?”: Gender differences in reciprocal relations between perceived effort, science self-concept, and achievement in chemistry

Wed, 29 Jul 2026 00:00:00 +0000

High effort is critical to complete difficult introductory courses in Science, Technology, Engineering, and Mathematics (STEM) fields. However, high effort can be negatively related to science self-concept (SC) as it can indicate a lack of brilliance in STEM. Further, this relation can be more pronounced for women due to gender stereotypes. To address gender differences in the relations between perceived effort and science SC, in this study, we measured two types of perceived effort: (1) criterion effort (the perception of trying hard) and (2) comparative effort (the perception of trying harder than others), and we examined possible gender differences in their correlates. Overall, we examined whether and to what extent reciprocal relations among two types of perceived effort, science SC, and achievement differ as a function of gender over the course of a semester in introductory chemistry. We utilized multiple-group latent cross-lagged panel analysis with undergraduate students...

Adolescents of color have greater mathematics values than White adolescents across gender: Replication evidence across high school and datasets in the U.S.

Wed, 29 Jul 2026 00:00:00 +0000

The unequal mathematics education system in the U.S. highlights the need to examine racial and ethnic differences in mathematics subjective task values as precursors to participation in mathematics-related careers. We investigated whether and to what extent mathematics subjective task values (i.e., interest, utility, and attainment) vary across the four largest U.S. racial/ethnic groups (i.e., Asian, Black, Latine, and White) among boys and girls, separately. We also tested whether these patterns replicated across high school years and across datasets. Using two U.S. large-scale longitudinal datasets (i.e., CAMP and HSLS), results showed that adolescents of color reported higher mathematics subjective task values than White adolescents, with differences favoring Asian over White adolescents and Black over Latine adolescents. These patterns were consistent across gender, though slightly more pronounced among boys, and replicated across both datasets. The findings demonstrate racial...

The Energy Exascale Earth System Model Version 3: 2. Overview of the Coupled System

Wed, 29 Jul 2026 00:00:00 +0000

Abstract The Energy Exascale Earth System Model version 3 (E3SMv3) represents the latest advancement in Earth system modeling developed by the U.S. Department of Energy (DOE). Building upon previous versions, E3SMv3 introduces significant updates across its coupled components to enhance capability and improve fidelity. The atmosphere component incorporates advancements in chemistry, aerosol‐cloud interactions, convection, and microphysics. The ocean features a new time‐stepping scheme and a higher‐resolution unstructured mesh with sub‐ice‐shelf cavities, while the sea ice model integrates advanced snow and ice physics for more realistic cryospheric simulations. The land model introduces prognostic vegetation dynamics and a new sub‐grid topographic treatment of solar radiation. A new tri‐grid configuration harmonizes the horizontal grids of the land and river components for improved process coupling. It is enabled by a new non‐linear remapping between the atmosphere and land. E3SMv3...

The Development of In-Group-Favoring Math Stereotypes Among Adolescents Across Gender and Race/Ethnicity: Teacher Influence and Motivational Consequences

Wed, 29 Jul 2026 00:00:00 +0000

The developmental processes underlying in-group-favoring math stereotypes (i.e., beliefs that favor one's own gender over another gender in math ability) and their associated motivational benefits in adolescence remain understudied. Guided by developmental intergroup theory and situated expectancy-value theory, the present study examined (a) the reciprocal relations between in-group-favoring math stereotypes and perceived math teacher support; (b) their associations with math motivational outcomes (i.e., math expectancies, math identity, and math grade point average); and (c) gender differences in these associations among adolescents within each of four largest racial/ethnic groups in the United States (i.e., Asian, Black, Latine, and White). Using the High School Longitudinal Study of 2009 (N = 18,419; M_age = 14.53, SD = 0.63 in nineth grade), we found that Asian and White girls' female-favoring math stereotypes and their perceived math teacher...

Development of the authentication and authorization processes for the iAgree portal, a platform for patient-controlled data sharing across health systems.

Thu, 2 Jul 2026 00:00:00 +0000

Objective

To develop the authentication and authorization module for iAgree, a privacy-preserving platform that enables patients to manage consent preferences and share electronic health record (EHR) data across multiple health systems with researchers. The consent and blockchain modules are described elsewhere.

Materials and methods

We developed the authentication and authorization module of iAgree. This module supports account creation and cross-institution identity binding as part of the iAgree workflow to enable patients to authenticate using federated credentials (eg, Google, Facebook). Patients link their identities across institutions by signing into each participating health systems patient portal. Identity attributes retrieved via a Fast Healthcare Interoperability Resources (FHIR^®) application programming interface (API) are cryptographically transformed into de-identified tokens so no raw identifiers are stored. Consent preferences are recorded...

Computed tomography staging of colon cancer: improved patient selection for neoadjuvant therapy with combined radiologic tumor and nodal staging.

Thu, 18 Jun 2026 00:00:00 +0000

BACKGROUND: Patient selection for neoadjuvant therapy in colon cancer (CC) needs to be improved as utilizing computed tomography (CT) tumor (T) staging alone is associated with overstaging and overtreatment. Therefore, we sought to identify specific nodal imaging features that can be combined with radiologic T staging to improve patient selection. METHODS: Pre-operative CTs of stage I-III CC patients treated with upfront resection (2018–2023) were assessed by an expert abdominal radiologist blinded to the histopathologic staging. Radiologic T and node (N) staging based on five imaging features (single lymph node (LN) > 1 cm, ≥ 3 prominent LNs, irregular borders, heterogenous enhancement, and extramural venous invasion) were compared to the pathologic staging, grouped by proficient or deficient mismatch repair status (pMMR vs. dMMR). RESULTS: Of the 177 patients, 147 and 30 had pMMR and dMMR CC, respectively. The overstaging rate for pathologic T staging was 6.1% pMMR vs....

We Need Granular Sharing of De-Identified Data—But Will Patients Engage? Investigating Health System Leaders' and Patients' Perspectives on A Patient-Controlled Data-Sharing Platform

Wed, 17 Jun 2026 00:00:00 +0000

Patient-controlled data-sharing systems are increasingly promoted as a way to empower patients with greater autonomy over their health data. Yet it remains unclear how different stakeholders, especially patients and health system leaders, perceive the benefits and challenges of enabling granular control over the sharing of de-identified medical data for research. To address this gap, we developed a high-fidelity prototype of a patient-controlled, web-based consent platform and conducted a two-phase mixed-methods study: semi-structured interviews with 16 health system leaders and a survey with 523 patient participants. While both groups appreciated the potential of such a platform to enhance transparency and autonomy, their views diverged in meaningful ways. Leaders viewed transparency and granular control through the lens of informed consent and institutional ethics, whereas patients interpreted these factors as safeguards against potential risks and uncertainties. Our findings...

Correction: The social amplification and attenuation of COVID-19 risk perception shaping mask wearing behavior: A longitudinal twitter analysis

Wed, 17 Jun 2026 00:00:00 +0000

[This corrects the article DOI: 10.1371/journal.pone.0257428.].

Retinal nerve fibre layer optical texture analysis: retinal nerve fibre bundle defect patterns and the extent of macular involvement across different stages of glaucoma

Wed, 3 Jun 2026 00:00:00 +0000

BACKGROUND/AIMS: To apply retinal nerve fibre layer (RNFL) optical texture analysis (ROTA) to investigate (1) the patterns of RNFL bundle defects, and (2) the frequency of papillomacular and papillofoveal bundle involvement across early, moderate and advanced glaucoma. METHODS: All eyes underwent 24-2 visual field (VF) testing and optical coherence tomography (OCT) for ROTA. The borders of RNFL defects were delineated from ROTA, and the involvement of the arcuate, papillomacular and papillofoveal bundles was determined for each eye. 24-2 VF stimulus projections were mapped onto the corresponding topographic areas of ROTA images. Multilevel logistic regression analysis was applied to evaluate the structure-function association. RESULTS: Papillomacular bundle defects were highly prevalent in glaucoma, increasing from 87.7% in early to 95.35% in moderate and 100% in advanced glaucoma. Papillofoveal bundle defects were also common, increasing from 29.7% in early to 36.05% in moderate...

Genetic Variation and Stroke Recovery: The STRONG Study

Tue, 12 May 2026 00:00:00 +0000

BACKGROUND: Genetic association studies can reveal biology and treatment targets but have received limited attention for stroke recovery. STRONG (Stroke, Stress, Rehabilitation, and Genetics) was a prospective, longitudinal (1-year), genetic study in adults with stroke at 28 US stroke centers. The primary aim was to examine the association that candidate genetic variants have with (1) motor/functional outcomes and (2) stress-related outcomes. METHODS: For motor/functional end points, 3 candidate gene variants (ApoE ε4, BDNF [brain-derived neurotrophic factor], and a dopamine polygenic score) were analyzed for associations with change in grip strength (3 months-baseline), function (3-month Stroke Impact Scale-Activities of Daily Living), mood (3-month Patient Health Questionnaire-8), and cognition (12-month telephone-Montreal Cognitive Assessment). For stress-related outcomes, 7 variants (serotonin transporter gene-linked promoter region, ACE [angiotensin-converting enzyme], oxytocin...

Neurodatascience: Past, Present, and Future

Thu, 7 May 2026 00:00:00 +0000

The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing...

Hippocampal ensembles represent sequential relationships among discrete nonspatial events

Thu, 7 May 2026 00:00:00 +0000

ABSTRACT The hippocampus is critical to the temporal organization of our experiences, including the ability to remember past event sequences and predict future ones. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain poorly understood. Here we recorded hippocampal ensemble activity as rats remembered a sequence of nonspatial events (5 odor presentations unfolding over several seconds), using a task with established parallels in humans. Using novel statistical methods and deep learning techniques, we then identified new forms of sequential organization in hippocampal activity linked with task performance. We discovered that sequential firing fields (“time cells”) provided temporal information within and across events in the sequence, and that distinct types of task-critical information (stimulus identity, temporal order, and trial outcome) were also sequentially differentiated within event presentations. Finally,...

Modeling Local Field Potentials with Regularized Matrix Data Clustering

Thu, 7 May 2026 00:00:00 +0000

In this paper, we propose a novel regularized mixture model for clustering matrix-valued image data. The new framework introduces a sparsity structure (e.g., low rank, spatial sparsity) and separable covariance structure motivated by scientific interpretability. We formulate the problem as a fi-nite mixture model of matrix-normal distributions with regularization terms, and then develop an Expectation-Maximization-type of algorithm for efficient computation. Simulation results and analysis on brain signals show the excellent performance of the proposed method in terms of a better prediction accuracy than the competitors and the scientific interpretability of the solution.

Unity by Diversity: Improved Representation Learning for Multimodal VAEs

Thu, 7 May 2026 00:00:00 +0000

Variational Autoencoders for multimodal data hold promise for many tasks in data analysis, such as representation learning, conditional generation, and imputation. Current architectures either share the encoder output, decoder input, or both across modalities to learn a shared representation. Such architectures impose hard constraints on the model. In this work, we show that a better latent representation can be obtained by replacing these hard constraints with a soft constraint. We propose a new mixture-of-experts prior, softly guiding each modality's latent representation towards a shared aggregate posterior. This approach results in a superior latent representation and allows each encoding to preserve information better from its uncompressed original features. In extensive experiments on multiple benchmark datasets and two challenging real-world datasets, we show improved learned latent representations and imputation of missing data modalities compared to existing methods.

A Model-Agnostic Graph Neural Network for Integrating Local and Global Information

Thu, 7 May 2026 00:00:00 +0000

Graph Neural Networks (GNNs) have achieved promising performance in a variety of graph-focused tasks. Despite their success, however, existing GNNs suffer from two significant limitations: a lack of interpretability in their results due to their black-box nature, and an inability to learn representations of varying orders. To tackle these issues, we propose a novel Model-agnostic Graph Neural Network (MaGNet) framework, which is able to effectively integrate information of various orders, extract knowledge from high-order neighbors, and provide meaningful and interpretable results by identifying influential compact graph structures. In particular, MaGNet consists of two components: an estimation model for the latent representation of complex relationships under graph topology, and an interpretation model that identifies influential nodes, edges, and node features. Theoretically, we establish the generalization error bound for MaGNet via empirical Rademacher...

A scalable reinforcement learning framework inspired by hippocampal memory mechanisms for efficient contextual and sequential decision making

Thu, 7 May 2026 00:00:00 +0000

Efficient decision-making in context-dependent, sequential tasks remains a fundamental challenge in reinforcement learning (RL). Inspired by the function of the brain’s hippocampal system, we introduce Hippocampal-Augmented Memory Integration (HAMI), a biologically inspired memory-based RL framework that leverages symbolic indexing, hierarchical memory refinement, and structured episodic retrieval to enhance both learning efficiency and adaptability. We also propose Hierarchical Contextual Sequences (HiCoS), a structured RL environment grounded in neuroscience studies on episodic and sequence memory and context-driven decision-making, which serves as a controlled testbed for evaluating biologically inspired memory-based decision-making systems. Our experimental results demonstrate that HAMI achieves high decision accuracy and improved sample efficiency while maintaining low memory utilization. HAMI’s architecture exhibits significantly lower inference latency than baseline memory-based...

Optimal Transport based Cross-Domain Integration for Heterogeneous Data

Thu, 7 May 2026 00:00:00 +0000

Detecting dynamic patterns shared across heterogeneous datasets is a critical yet challenging task in many scientific domains, particularly within the biomedical sciences. Systematic heterogeneity inherent in diverse data sources can significantly hinder the effectiveness of existing machine learning methods in uncovering shared underlying dynamics. Additionally, practical and technical constraints in real-world experimental designs often limit data collection to only a small number of subjects, even when rich, time-dependent measurements are available for each individual. These limited sample sizes further diminish the power to detect common dynamic patterns across subjects. In this article, we propose a novel heterogeneous data integration framework based on optimal transport to extract shared patterns in the conditional mean dynamics of target responses. The key advantage of the proposed method is its ability to enhance discriminative power by reducing heterogeneity unrelated...

STABLE-MATCHING VORONOI DIAGRAMS: COMBINATORIAL COMPLEXITY AND ALGORITHMS

Wed, 6 May 2026 00:00:00 +0000

We study algorithms and combinatorial complexity bounds for stable-matching Voronoi diagrams, where a set, S, of n point sites in the plane determines a stable matching between the points in R² and the sites in S such that (i) the points prefer sites closer to them and sites prefer points closer to them, and (ii) each site has a quota or "appetite" indicating the area of the set of points that can be matched to it. Thus, a stable-matching Voronoi diagram is a solution to the well-known post office problem with the added (realistic) constraint that each post office has a limit on the size of its jurisdiction. Previous work on the stable-matching Voronoi diagram provided existence and uniqueness proofs, but did not analyze its combinatorial or algorithmic complexity. In this paper, we show that a stable-matching Voronoi diagram of n point sites has O(n^2+ε) faces and edges, for any ε > 0, and show that this bound is almost tight by giving a family of diagrams...

Folding a paper strip to minimize thickness

Wed, 6 May 2026 00:00:00 +0000

In this paper, we study how to fold a specified origami crease pattern in order to minimize the impact of paper thickness. Specifically, origami designs are often expressed by a mountain–valley pattern (plane graph of creases with relative fold orientations), but in general this specification is consistent with exponentially many possible folded states. We analyze the complexity of finding the best consistent folded state according to two metrics: minimizing the total number of layers in the folded state (so that a “flat folding” is indeed close to flat), and minimizing the total amount of paper required to execute the folding (where “thicker” creases consume more paper). We prove both problems strongly NP-complete even for 1D folding. On the other hand, we prove both problems fixed-parameter tractable in 1D with respect to the number of layers.

NON-EUCLIDEAN ERDŐS-ANNING THEOREMS

Wed, 6 May 2026 00:00:00 +0000

NON-EUCLIDEAN ERDŐS-ANNING THEOREMS

Shaping HCI Research for Children's Care Ecosystem Involvement

Wed, 6 May 2026 00:00:00 +0000

Recent HCI research emphasizes the importance of considering children’s care ecosystems in the design of technologies, extending the focus beyond families to include teachers, peers, therapists, and institutions. While this ecosystem perspective opens opportunities for more inclusive and collaborative technologies, it also introduces challenges such as recruitment, power dynamics, reconciling diverse perspectives, and complex ethical considerations. This CHI 2026 workshop builds on prior community efforts at IDC 2023, CHI 2024, and IDC 2025. Its primary focus is on children’s care ecosystems, but we also welcome researchers working with other populations who wish to apply an ecosystem lens. The workshop will bring together researchers and practitioners to discuss opportunities, challenges, and methods, and to collaboratively articulate a research agenda for care ecosystem-centered HCI. Participants will engage in mapping and synthesis activities that produce care ecosystem maps...

FamilyBloom: Examining Ecologies of Collaboration in Family-Centered Health Tracking

Wed, 6 May 2026 00:00:00 +0000

Family health informatics tools can help support well-being with shared data tracking. Prior work typically focused on shared data review, but often in specific moments, like bedtime, or centered on caregiving of children or elderly members. To investigate how tracking can support mutual health collaboration between family members pervasively across daily contexts, we designed and deployed FamilyBloom, a glanceable smartwatch and home display system for mood and goal tracking. Twelve families with both neurotypical and ADHD members used FamilyBloom for three months on average. Our findings reveal how family-centered tracking created collaboration opportunities and tensions across multiple ecological systems: individual self-regulation, collaborations within family dynamics, involvement of care networks with varying trust levels, institutional school constraints and cultural stigma, and temporality of regular routines and crisis periods. We discuss an ecosystem-aware approach to...

Collaboration and Assistive Technology: Facilitating Joint Awareness for Noise Sensitivity

Wed, 6 May 2026 00:00:00 +0000

Existing research has explored various methods to support people with noise sensitivity (PWNS), from desensitization therapies to technological solutions. However, there is a gap in systems that identify and monitor characteristics of noise sensitivity experiences to help PWNS and their companions better understand their condition and make informed management decisions. To fill this gap, we developed AudioBuddy, an app with sensing and tracking features designed to promote awareness between PWNS and their companions. We tested AudioBuddy as a technological probe over a two-week field deployment. Our results show that AudioBuddy can support awareness of how sounds and environments influence the psychophysiological states of PWNS, aiding in understanding noise sensitivity experiences. Nonetheless, technical limitations impacted the depth of awareness participants could attain. We discuss challenges and opportunities for future systems to facilitate awareness among PWNS and their...

Efficacy of Full-Packet Encryption in Mitigating Protocol Detection for Evasive VPNs

Wed, 6 May 2026 00:00:00 +0000

Full-packet encryption is a technique used by modern evasive Virtual Private Networks (VPNs) to avoid protocol-based flagging from censorship models by disguising their traffic as random noise on the network. Traditional methods for censoring full-packet-encryption based VPN protocols requires assuming a substantial amount of collateral damage, as other non-VPN network traffic that appears random will be blocked. We tested several machine learning-based classification models against the Aggressive Circumvention of Censorship (ACC) protocol, a fully-encrypted evasive VPN protocol which merges strategies from a wide variety of currently in-use evasive VPN protocols. Our testing found that while ACC was able to survive our models when compared to random noise, it was easily detectable with minimal collateral damage using several different machine learning models when within a stream of regular network traffic. While resistant to the current techniques deployed by nation-state censors,...

Towards Verifying Crash Consistency

Thu, 23 Apr 2026 00:00:00 +0000

Compute Express Link (CXL) memory sharing, persistent memory, and other related technologies allow data to survive crash events. A key challenge is ensuring that data is consistent after crashes such that it can be safely accessed. While there has been much work on bug-finding tools for persistent memory programs, these tools cannot guarantee that a program is crash-consistent. In this paper, we present a language, CrashLang, and its type system, that together guarantee that well-typed data structure implementations written in CrashLang are crash-consistent. CrashLang leverages the well-known commit-store pattern in which a single store logically commits an entire data structure operation. In this paper, we prove that well-typed CrashLang programs are crash-consistent, and provide a prototype implementation of the CrashLang compiler. We have evaluated CrashLang on five benchmarks: the Harris linked list, the Treiber stack, the Michael–Scott queue, a Read-Copy-Update binary search...

Automated Insertion of Flushes and Fences for Persistency

Thu, 23 Apr 2026 00:00:00 +0000

CXL shared memory and persistent memory allow the contents of memory to persist beyond crashes. Stores to persistent or CXL memory are typically not immediately made persistent; developers must manually flush the corresponding cache lines to force the data to be written to the underlying storage. Correctly using flush and fence operations is known to be challenging. While state-of-the-art tools can find missing flush instructions, they often require bug-revealing test cases. No existing tools can ensure the absence of missing flush bugs. In this paper, we present PMRobust, a compiler that automatically inserts flush and fence operations to ensure that code using persistent memory is free from missing flush and fence bugs. PMRobust employs a novel static analysis with optimizations that target newly allocated objects. We have evaluated PMRobust on persistent memory libraries and several persistent memory data structures and measured a geometric mean overhead of 0.26% relative to...

Understanding the Perspectives of Autistic Gamers through an Online Autistic Community and a Survey

Wed, 22 Apr 2026 00:00:00 +0000

Autistic people often have an interest in and spend a substantial amount of time engaged with video games. Games can be supportive of their mental health and social needs and have been widely used for behavioral interventions among autistic people. However, the gaming experiences and preferences of autistic people themselves have not been thoroughly studied. To explore these experiences, we used a multi-method approach, analyzing game-related posts from a large autism-related subreddit and conducting a survey with 145 autistic people. The survey allowed us to further understand preferences around accessibility and sensory experiences, representation, and social experiences in communities that emerged in the Reddit posts. We found that games offering a sense of freedom, control, and creativity might be particularly appealing to autistic gamers. Discussions also emerged around what types of audio and visual sensory input were considered soothing and appropriate. Moreover, both the...

DermaVision

Wed, 15 Apr 2026 00:00:00 +0000

Approximately 10 million people in the United States suffer from domestic violence annually, with 4 out of 10 cases affecting people of color. Traditional coloration guides remain the primary forensic strategy to evaluate bruise injuries, which are highly subjective and inaccurate for monitoring bruises. Additionally, this approach fails to consider bruise pigmentation in darker skin tones, and the results of this qualitative method vary by the medical professional conducting the inspection. There is a need for reliable, quantitative bruise information across all skin tones that can be utilized in both medicine and justice. DermaVision aims to address this need by designing a portable multi-spectral camera to quantitatively analyze bruises in diverse skin tones. By correlating the reflective spectra of a bruise with its age and healing progression, our camera will provide an accurate timeline for when bruises occur irrespective of patients’ skin color. This technology will assist...

HyperXite 9

Wed, 15 Apr 2026 00:00:00 +0000

The overall objective for HyperXite 9 was to design and build a more robust, and reliable pod, capable of proving the feasibility of a high-speed transportation system. We are working to improve a linear induction motor as the pod's propulsion system. We are also designing and implementing a thermal cooling system to actively dissipate the heat generated by this propulsion system. Our team is comprised of the following 7 subteams: Static Structures, Braking & Pneumatics, Dynamic Structures, Propulsion, Power Systems, Control Systems, and Outreach.

Biometric Advanced Driver Assistance System (ADAS)

Wed, 15 Apr 2026 00:00:00 +0000

This project focuses on the development of a human-aware advanced driver assistance system (ADAS) that helps promote safe driving based on a driver's biometrics and driving behavior. The system uses biometric signals from the driver using BioHarness Belt and Galvanic Skin Response (GSR) sensors to monitor the driver's heart rate, breathing rate, ECG and GSR signals. These sensory information are then analyzed by machine learning models to determine whether the driver is stressed or drowsy. The project extended the current state-of-the-art driving simulator CARLA to not only include the driver's brake intensity, speed, throttle and steering but also the driver's biometric state. The vehicle controls and biometrics are then plotted and analyzed for correlations that could imply a driver is driving aggressively, assertively, or defensively. Based on these correlations, the driver will be displayed a warning on the simulation which advises them to drive carefully. The output from...

2025 UCI CanSat Annual Design Review Poster

Wed, 15 Apr 2026 00:00:00 +0000

The UCI CanSat team is a senior design team that competes in the international CanSat competition, an annual design-build-launch competition held by the American Astronautical Society focused on space-type systems. Each year, a ten-person team designs a “CanSat,” following competition requirements. This year, the CanSat is required to operate in 4 main phases: ascent, apogee, descent, and landing. During ascent, the CanSat must act as the nose cone of the rocket. At apogee, an ejection charge releases the CanSat from the rocket, and the CanSat must activate a parachute to begin a safe descent. At three-quarters of the peak altitude, the CanSat must deploy its payload. The payload consists of two cameras, one to film payload deployment, while the other is spin-stabilized to film the north side of the CanSat. In addition, while the container descends with a parachute, the payload must descend with an auto-gyro system. The auto-gyro system must significantly decrease the descent...

The effectiveness of participatory near-peer digital media literacy interventions

Wed, 15 Apr 2026 00:00:00 +0000

The impacts of digital media literacy (DML) interventions are mixed, perhaps due to adult-driven curricula and misalignment with youth’s needs. We tested how student-led, developmentally-informed DML education could leverage youth’s expertise. Using Youth Participatory Action Research and near-peer mentoring, high school students (n = 31) designed and taught digital literacy interventions in two areas (1) digital communication (i.e. group-chats) and (2) persuasive design (e.g. features such as infinite scroll), resulting in significant increases in knowledge of digital communication conflict prevention and remediation strategies, along with identification and knowledge of persuasive design for 79 middle and 31 high school students. High schoolers also reported more feelings of agency and research knowledge. These findings indicate that making DML interventions developmentally aligned might be key for supporting youth in the digital age.

Summary the Savior: Harmful Keyword and Query-based Summarization for LLM Jailbreak Defense

Wed, 8 Apr 2026 00:00:00 +0000

Summary the Savior: Harmful Keyword and Query-based Summarization for LLM Jailbreak Defense

Evaluating the potential of acupuncture for Alzheimer’s disease treatment: A meta-analysis and systematic review of mouse model studies

Wed, 8 Apr 2026 00:00:00 +0000

Acupuncture is an ancient practice that was developed within the framework of traditional Chinese medicine. While acupuncture has been recently proposed as a therapy for Alzheimer’s disease (AD), acupuncture effects are not well understood in terms of neural mechanisms. Here, we review and examine the studies that used AD mouse models and analyze the experiments where researchers administered electroacupuncture (EA) to AD mice to assess the potential therapeutic impact of acupuncture on disease pathology and cognitive function in controlled laboratory settings. We analyzed 29 relevant PubMed articles published between January 2014 and July 2025. Our results reveal that EA significantly reduces both amyloid-beta (Aβ) and phosphorylated tau (p-tau) levels and neuroinflammatory biomarkers, including molecular signatures for activated microglia and astrocytes in the brain. EA also enhances cognitive functions. While no study directly compared acupoint strategies, the indirect comparisons...

Video Games and Learning

Mon, 30 Mar 2026 00:00:00 +0000

The interdisciplinary field of the learning sciences encompasses educational psychology, cognitive science, computer science, and anthropology, among other disciplines. The Cambridge Handbook of the Learning Sciences, first published in 2006, is the definitive introduction to this innovative approach to teaching, learning, and educational technology. In this significantly revised third edition, leading scholars incorporate the latest research to provide seminal overviews of the field. This research is essential in developing effective innovations that enhance student learning - including how to write textbooks, design educational software, prepare effective teachers, and organize classrooms. The chapters illustrate the importance of creating productive learning environments both inside and outside school, including after school clubs, libraries, and museums. The Handbook has proven to be an essential resource for graduate students, researchers, consultants, software designers,...

Situating Big Data

Mon, 30 Mar 2026 00:00:00 +0000

Lacking a digital crystal ball, we cannot predict the future of education or the precise instructional role games will have going forward. Yet we can safely say that games will play some role in the future of K-12 and higher education, and members of the games community will have to choose between being passive observers or active, progressive contributors to the complex and often political process of weaving together pedagogy, technology, and culture. This will involve agreeing that games—or, more specifically, game mechanics and the engagement in joyful learning that they engender—are not only critical for shaping online and classroom instruction but also the evolution of schooling as a whole. Likewise, it will involve a hard push beyond questions like “Are video games ‘good’ or ‘bad’ for education?” and “Are games ‘better’ for all students than traditional face-to-face teaching” to unpack how game experiences vary with individual learner goals as an interaction with the parameters...

Editorial: Extremism in games

Mon, 30 Mar 2026 00:00:00 +0000

Editorial: Extremism in games

PIKACHU: (est. 1996) Franchise: Pokémon Developer: Game Freak

Mon, 30 Mar 2026 00:00:00 +0000

PIKACHU: (est. 1996) Franchise: Pokémon Developer: Game Freak

How much we express love predicts how much we feel loved in daily life