Fully integrated AI-enhanced flexible wearable sensor for real-time movement evaluation and table tennis training.

ABSTRACT The United States is experiencing an influx of executive orders (EOs) as well as proposed and enacted laws restricting how institutions and educators attend to diversity, equity, and inclusion (DEI). We investigated the influence of the contentious sociopolitical climate created by anti-DEI laws in the context of higher education biology. Accurate and inclusive approaches of addressing the biological and social dimensions of diverse human experiences is becoming ever more imperative, particularly because of the way inaccurate claims about biology are being wielded to justify prejudicial EOs and legislation. Through the lens of faculty agency, we examined how biology instructors’ agentic actions (behavior aimed at achieving one’s goals) and agentic perspectives (viewing the self as capable of achieving those goals) are being influenced by anti-DEI laws. Participants expressed minimally changing their instructional content, although they became more aware of possible negative consequences of teaching potentially controversial topics. This led to participants making subtle changes to avoid scrutiny regarding their teaching. We call for more coalition building across administrators, faculty, and students for ongoing and proactive resistance against the anti-DEI movement.

U.S. immigration laws and policies are subject to rapid and constant changes, rendering the landscape for immigration legal research complicated, messy, and sometimes unreliable. This article explores an immigration researcher’s challenge of accessing, evaluating, and relying on immigration resources when laws and policies change almost daily through executive orders, regulatory changes, or the simple disappearance of information from government websites. Using a survey of research platforms and resources, including comprehensive legal research databases, specialized online libraries, and publicly accessible websites and newsletters, this analysis identifies the strengths and weaknesses of accessing immigration law and policy information. In an era defined by uncertainty, and when researchers are confronted with paywalls, subscription fees, broken links, and outdated information, this article can serve as a practical guide for navigating the complex and evolving maze of U.S. immigration law resources.

ABSTRACT This paper examines the escalating entanglement between American universities and what the authors term the tech‐industrial complex—a political‐economic nexus linking the state and private technology industries through infrastructures of data, surveillance, and digital governance. Building upon historical precedents from McCarthyism and COINTELPRO to the Trump administrations, the paper argues that contemporary academic repression differs from earlier eras not merely in ideology but in infrastructure: a digital ecosystem of participatory surveillance, anticipatory obedience, and neoliberal efficiency. Using case studies from recent U.S. higher education—including institutional compliance with federal executive orders, social media surveillance, and the rise of platforms such as Indiana's Eyes on Education portal—the authors trace how universities have become both subjects and agents of this expanding technopolitical order. The analysis situates this development within a broader genealogy of American state power, neoliberalism, and digital capitalism, highlighting the complicity of academic institutions in sustaining systems of control. In response, the authors advocate for renewed anthropological and humanistic engagement that resists resignation, promotes open access, and reclaims critical inquiry within and beyond the academy. Ultimately, the paper contends that understanding and contesting the tech‐industrial complex is essential for reimagining intellectual labor and sustaining democratic life in an age of pervasive digital surveillance.

On June 9 th and 10 th , 2025 the Health Physics Society (HPS) and National Council on Radiation Protection and Measurements (NCRP) jointly sponsored two open forums with the hopes of discussing and responding to constituent beliefs regarding a series of nuclear-related Executive Orders (EOs). The HPS and NCRP leaders were joined by members from the American Academy of Physicists in Medicine (AAPM), and the Conference of Radiation Control Program Directors (CRCPD) as panelists to help respond and moderate the forums. The forums focused on three of the nine relevant EOs, and, while varying opinions were shared, three common themes were strongly supported: First, many of the constituents support change, particularly regulatory harmonization (205/212, 97%), and the time to make changes [now] is appropriate due to these EOs. Second, the constituents believe that these EOs will have a significant impact on the nuclear fields (420/468, 90%). Third, the constituents strongly support the United States in increasing its use of nuclear power (236/245, 96%).

This perspective discusses the public health implications of the recent executive order (EO), and subsequent court challenges, regarding the termination of birthright citizenship under the 14th Amendment to the US Constitution. The EO threatens to undermine the justification that undergirds many state and federal policies regarding access to maternal healthcare for immigrants: the expectation that babies born in the USA will become US citizens. In so doing, the EO creates conditions under which many children born in the USA may be rendered stateless and thus ineligible for public benefits.

‘The smokin’ hot trophy wife of the oil and gas industry’: The role of petro-masculinity in geothermal rhetoric and policy

The proposed framework, LOCKE (Logic Optimization, Compilation, and Execution), enables efficient expression and execution of bit-level computation by transforming programs into a fully dependency-resolved Boolean representation. Instead of relying on control flow and sequential execution, the system models computation as a flattened graph of logic operations, exposing complete dependencies and eliminating ambiguity. The framework follows a structured pipeline consisting of parsing, logic lowering, static scheduling, and code generation, ensuring that execution order is resolved entirely at compile time. This approach allows deterministic execution, improved parallelism, and reduced runtime overhead. By shifting computation from a control-driven model to a logic-driven paradigm, LOCKE achieves higher efficiency and scalability for performance-critical bit-level applications.

In order to solve the problems of poor connection between business data and financial data, lag in risk identification and insufficient collaborative decision-making support in the intelligent transformation of financial data, an intelligent analysis model of industry and finance data fusion was constructed, and the application effect was evaluated with case enterprises. The research established an analysis link around data access, feature fusion, risk identification, decision support and effect feedback, and jointly identified order execution, inventory turnover, payment collection cycle, revenue cost and gross profit change. The results show that after the application of the model, the accuracy of risk identification increases from 83.6% to 92.4%, the average response time of early warning decreases from 26.5 hours to 9.8 hours, the completion rate of collaborative processing of industry and finance increases from 71.2% to 88.7%, and the gross profit rate increases from 22.34% to 23.41%. The research shows that the model can enhance the ability of business risk perception, improve the efficiency of collaborative decision-making, and provide method support and application reference for the intelligent transformation of enterprise financial data.

The rapid growth of online trading platforms and digital financial technologies has significantly improved access to stock market participation and investment opportunities. However, it has also exposed novice investors to increased financial risk due to limited practical knowledge and inadequate understanding of trading strategies. This paper presents a web-based virtual stock trading system designed to provide a risk-free simulation environment for beginner investors. The platform integrates realtime stock data simulation, portfolio management tools, virtual wallet functionality, order execution modules, and market news feeds to replicate realistic trading conditions. Experimental evaluation through pilot user testing indicates that the system enhances understanding of trading mechanisms, portfolio allocation, and risk management compared to traditional theoretical learning methods. The modular layered architecture further ensures scalability, maintainability, and flexibility for integrating additional financial instruments and advanced analytics in future developments.

States across the U.S. are advancing the age-friendly movement by joining networks that promote age-friendly communities. While much of the existing scholarship has focused on local initiatives, far less attention has been paid to the role of states in sustaining and scaling age-friendly change. Age-Friendly States build on local momentum to drive systems-level changes that create healthier, more inclusive environments for older adults. This forum examines the role of states in shaping sustainable aging policy and practice. An environmental scan of age-friendly state plans and multisector plans on aging was conducted to document and examine how states design and implement approaches to healthy aging and to identify promising strategies for systems change across public sectors. Nineteen states met the inclusion criteria: 12 had AARP Age-Friendly State designations, eight had Multisector Plans for Aging (MPAs) in development or implementation, and four had MPAs supported by legislation or executive orders. Findings from this analysis were synthesized to surface transferable models, enabling conditions, and actionable lessons that can inform future state and local efforts to embed age-friendly principles across systems to support long-term, equitable change. The authors argue that states involvement is a crucial lynchpin in advancing age-friendly ecosystems.

Trump's psychedelic executive order: president expands access to psychoactive drugs.

Walking is a fundamental component of daily human activity, in which orderly execution of key gait events [such as heel strike (HS) and toe-off (TO) ] is essential for maintaining gait coordination and stability. However, the underlying brain-muscle neural coordination mechanisms associated with these events remain unclear. In this study, electroencephalography (EEG) signals from 19 channels and surface electromyography (sEMG) signals from 14 lower-limb muscles were synchronously recorded from 18 healthy participants during steady-state walking at a constant speed of 3.2 km/h. Brain-muscle connectivity networks were constructed using the adaptive directed transfer function (ADTF), and the dynamic connectivity characteristics associated with HS and TO events were quantitatively analyzed. The results showed that brain-muscle connectivity was strongest in the β frequency band. Intra-brain (EEG-EEG), brain-to-muscle (EEG-sEMG), and intra-muscle (sEMG-sEMG) connections were significantly stronger than muscle-to-brain (sEMG-EEG) connections. At both HS and TO events, information exchange between frontal-central cortical regions and the muscles of the supporting leg was markedly enhanced. Furthermore, compared with TO, the brain-muscle network at HS exhibited higher clustering coefficient, global efficiency, and betweenness centrality. These findings suggest that brain-muscle interactions during walking are predominantly mediated in the β band and dynamically modulated by gait events. Accurate characterization of gait event-related brain-muscle connectivity may provide important technical support for clarifying the neuromuscular coordination mechanisms underlying fine gait control.

A new executive order from the White House could jump‐start additional research and new approvals of psychedelic drugs for the treatment of serious mental illness (SMI). Among its provisions, the April 18 order directs federal agencies to expedite reviews of products that have successfully completed Phase 3 trials, so that they can be more rapidly rescheduled from their current Schedule I (no recognized medical use) status.

On April 17, President Donald Trump signed an executive order directing the U.S. Food and Drug Administration (FDA) to “establish a pathway for eligible patients to access investigational psychedelic drugs, including ibogaine compounds.” While citing the need for solutions to treatment‐resistant mental illness, Trump also required the U.S. Department of Health and Human Services (HHS) to allocate $50 million through the Advanced Research Projects for Health (ARPA‐H) program to match investments made by state governments to advance research into psychedelic programs for populations with serious mental illness.

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises.

ABSTRACT Throughout the COVID‐19 pandemic, militaries mobilized at an unprecedented scale to support domestic response efforts. This was consistent with the growing trend of asset mobilization for military operations other than war during public health emergencies. However, the global scale and vast breadth of civil‐military cooperation invites new considerations regarding the authority and scope of domestic operations of militaries during public health emergencies. We aimed to identify how each UN member state codifies their military's roles and responsibilities in domestic emergencies. We systematically analyzed each nations' domestic military deployment, focusing on the authority, execution and scope of military involvement pertaining to domestic public health emergencies. We then analyzed legally enforceable policies and categorized them respectively. We found that of countries with active military forces, nearly all (170/171) have codified rules on domestic military deployment and 90.59% (154/170) allow domestic military mobilization for any domestic emergency through executive orders. Furthermore, 58.48% (100/171) of countries with an active military have codified the separation of powers to ensure that civilian decision makers are exclusively empowered to mobilize military forces. Finally, we found that 74.85% (128/171) of countries included language that explicitly authorized military involvement in domestic operations, including support for public health emergency response and humanitarian operations. Our findings provide critical data for analyzing the relationship between military operations and public health outcomes. This will enable future research, including how specific domestic military deployment policies impact the effectiveness of military involvement in public health emergencies for population health and civil order.

Abstract This study investigated how algorithmic accountability is discursively framed across major global AI governance models, aiming to uncover how regulatory narratives shape legal responsibilities and power relations within the European Union, the United States, and ASEAN frameworks. A critical discourse analysis, combined with framing theory, was applied to three key instruments: the European Union Artificial Intelligence Act, Executive Order 14110 of October 30, 2023, and the ASEAN Guide on AI Governance and Ethics. The analysis revealed three distinct framings: the EU adopts a legal and rights-based approach, the U.S. relies on voluntary and innovation-driven standards, and ASEAN emphasizes ethical and cooperative responsibility. Common anchors such as transparency, explainability, and human oversight offer opportunities for regulatory convergence. Algorithmic accountability functions as both a regulatory mechanism and a discursive tool, reflecting divergent governance philosophies and geopolitical strategies. These narratives shape how AI accountability is defined, implemented, and contested globally. Policymakers, industry actors, and civil society should pursue adaptive and interoperable governance frameworks that strike a balance between legal enforcement, innovation, and ethical principles to strengthen global AI accountability.

This paper examines DODEAs successes in student academic performance, arguing this success stems from a standard curriculum and college-and-career readiness initiatives, along with a sustained commitment to diversity, equity, and inclusion. Drawing on social efficiency, critical theory, and humanist pedagogy, this analysis demonstrates how culturally responsive practices have supported resilience and achievement among military-connected students. Recent United States executive orders banning DEI-related content, cultural observances, and literature are explored as threats to the success of the DODEA system. By highlighting racial and gender-based achievement gaps, it is argued that educational restrictions stemming from recent executive orders risk eroding DODEA's academic achievements.

In the first year of his second term in 2025, President Trump systematically reshaped the United States’ immigration system through a series of executive orders (EOs) and proclamations. This article argues that these EOs and their subsequent policies should not be simply understood as exceptional authoritarian tools in extraordinary political times in the United States. Instead, these seemingly disparate measures reflect historical continuity when examined within the broader context of the country’s violent and exclusionary racial politics and anti-immigrant past. As such, the article argues that President Trump’s EOs, proclamations and their corresponding policies are serving as routine technologies of racial statecraft – embedding Great Replacement (GRT) ideology within the administrative machinery of the US such that it is no longer a cultural conspiracy or fringe ideology. Through legitimising state practice and explaining policy coherence across apparently disparate executive actions, these measures are serving as administrative rationality. Using a genealogical reading of policies derived from the current EOs on immigration, the article concludes with the implications for the future of multiracial citizenship and belonging in the United States.