Search Results (2,349)

In this study, a special version of the Flexible Job Shop Scheduling Problem with equally and consistently batching constraints (hereafter called ECBFJSP) is considered, which involves multiple aspects of coordination, such as machine selection, process sorting, and batch splitting, which is highly complex and places strict demands on the optimization strategy. To effectively meet this challenge, this study constructs a dual-action deep reinforcement learning algorithm framework based on the Enhanced Heterogeneous Graph Neural Network (EHGNN). First, an enhanced heterogeneous graph and EHGNN model for the ECBFJSP is innovatively proposed. By integrating multi-dimensional node features such as work order priority, machine tool processing capability, and process constraints, dynamic feature aggregation of various types of information is achieved with the help of GATs and GRUs. The model can output context-aware representations containing global resource constraints, greatly improving the joint optimization efficiency of job scheduling and batch partitioning and significantly enhancing the adaptability of the dual-action decision framework to the complexity of the ECBFJSP. At the decision-making mechanism level, this study designed a dual-action decision space of process sequencing–machine selection action and batch partitioning action and used the DAPPO algorithm to collaboratively optimize the dual-action strategy to ensure the stability and efficiency of the decision-making process. The experimental data results show that compared with traditional algorithms, the proposed intelligent decision framework performs better in scheduling quality when solving the ECBFJSP, which fully verifies the significant effectiveness and practicality of the framework in solving the ECBFJSP. Full article

Neuronal excitability manifests itself mainly in the form of non-linear, self-regenerative waves of electricity moving along the surface of neuronal axons. These waves are commonly known as action potentials (APs). Theoretical and experimental investigations of the physical and functional characteristics of APs have broadly followed along the lines of the ionic hypothesis and the associated mathematical model introduced by Hodgkin and Huxley (HH). In the current form of this bioelectrical framework, adopted in mainstream physiology and other biological sciences, the axonal membrane is conceptualized as an electronic circuit where electric current is generated and propelled as a result of the time-dependent opening and closure of voltage-operated ion channel proteins, allowing passive flow of specific ions across and along the membrane, powered by their respective electrochemical gradients. Although representing mainstream research, the bioelectric perspective has been criticized for its narrow focus on the electrical characteristics of APs, whilst ignoring other physical manifestations of the nerve signal, particularly mechanical and thermal changes coinciding with AP propagation. As an alternative, a macroscopic thermodynamics-based acoustic theory has been outlined, in which all electric and non-electric manifestations of the nerve signal are considered as a result of a single density pulse in the axonal membrane carried by a reversible lipid membrane phase transition and momentum conservation. Representing a minority view, however, this unified, acoustic perspective on the physical nature of neuronal excitability is largely ignored by representatives of the bioelectric perspective. Here, we draw special attention to the philosophical dimension of the communication failure between the two communities of scientists. We argue that adherents of the bioelectric perspective favor a mechanist type of explanation, whilst supporters of the acoustic perspective are committed to so-called covering-law types of explanation. We conclude that it is this thus far unrecognized philosophical rift, rather than specific scientific differences in opinion, that blocks fruitful interdisciplinary cooperation necessary for building a comprehensive, fully integrated notion of the physical nature of neuronal excitability. Suggestions of how to bridge this conceptual gap are formulated. Full article

MaChiP 1.0 used static magnetic resonance imaging (MRI) to identify coexistent idiopathic intracranial hypertension (IIH) and spontaneous intracranial hypotension (SIH) in Chiari I malformation (CM-I), improving etiologic characterization. This Protocol/Perspective paper presents MaChiP 2.0 as a testable, artificial intelligence (AI)-integrated imaging roadmap for acquired Chiari I malformation (CM-I), intended to support the differentiation between congenital and acquired tonsillar descent and to guide leak-localization imaging in suspected spontaneous intracranial hypotension (SIH). Building on the structural foundation of MaChiP 1.0, this framework outlines how dynamic craniospinal imaging tools, including phase-contrast magnetic resonance imaging (PC-MRI) and displacement encoding with stimulated echoes (DENSE), may be combined with conventional morphologic markers to refine imaging evaluation. It further describes the potential use of currently available artificial intelligence (AI) methods for segmentation, cerebrospinal fluid (CSF) flow quantification, and imaging biomarker assessment. Noninvasive magnetic resonance (MR)-based techniques are proposed as first-line approaches for leak detection, while digital subtraction myelography (DSM) and computed tomography myelography (CTM) remain the reference standards when initial imaging is inconclusive. Full article

The United States faces a worsening maternal mortality crisis that starkly contrasts with trends in other high-income nations. Maternal mortality rates (MMRs) have more than doubled over the past two decades, rising from 9.65 deaths per 100,000 live births in 1999–2002 to 23.6 in 2018–2021, with approximately 700 deaths annually. Black and American Indian/Alaska Native women experience maternal mortality rates two to three times higher than their White counterparts, reflecting persistent structural inequities rather than biological differences. This narrative review synthesizes current evidence on the underlying drivers of racial inequities in maternal mortality and evaluates evidence-based interventions and policy strategies to address these disparities. A comprehensive literature review between 2000 and 2025 was conducted using databases including PubMed, Scopus, Web of Science, and Google Scholar, focusing on studies examining clinical, social, and structural determinants of maternal health outcomes, as well as evidence-based interventions and maternal health policy. Targeted searches of policy reports and grey literature were also performed to identify relevant policy initiatives and system-level interventions. Key contributors to disparities include underlying health conditions, postpartum mental health inequities, provider shortages, and limited access to postpartum care, with pregnancy-associated breast cancer (PABC) representing a less common but clinically significant risk factor that warrants further investigation in the context of racial inequities. Structural racism and socioeconomic disparities further exacerbate inequities through differential access to care, treatment bias, and barriers to healthcare utilization. System-level challenges, including workforce shortages, maternity care deserts, and the absence of federally mandated paid maternity leave, disproportionately impact marginalized populations. Although policy initiatives such as Medicaid postpartum coverage extensions, the Maternal Health Momnibus Act, and Maternal Mortality Review Committees represent important progress, they remain insufficient without broader structural reform. Evidence-based interventions, including midwife- and doula-led care, community-based peer support, and culturally tailored mental health programs, demonstrate measurable improvements in maternal outcomes. Outcomes of this review highlight the need for a comprehensive, equity-centered approach to reducing maternal mortality disparities, emphasizing structural reform, expanded access to care, strengthened data systems, and community-driven solutions. Full article

Environmental sustainability is crucial for human survival and the future of new generations. Anticipating regret can influence decision-making and promote sustainable behaviors. This study examines the effect of anticipated regret on pro-environmental behaviors among young adults (18–30) using regret-based short videos called “EkoToks.” A total of 128 participants were randomly assigned to an experimental group, receiving regret-evoking videos, or a control group, receiving informational videos. Pro-environmental behaviors were measured at baseline, post-test, and at three-month follow-up. Results showed significant short-term improvements in the experimental group compared to the control group, with higher scores in total pro-environmental behavior, prosocial behavior, reuse, recycling, and pro-environmental actions. At follow-up, the experimental group continued to outperform the control group in terms of total behavior, prosocial behavior, recycling, reuse, pro-environmental actions, and waste reduction. Regression analyses revealed that post-test regret significantly predicted further improvements at follow-up (compared to post-test) in total behavior, prosocial behavior, reuse, and pro-environmental actions. These findings highlight the effectiveness of anticipated regret in improving environmental behaviors, particularly low-cost ones. Full article

Higher Education Institutions (HEIs) are increasingly called upon to integrate sustainability across curricula and to prepare students to respond critically and responsibly to complex environmental challenges. While sustainability education is often associated with scientific, technological, or policy-oriented disciplines, the contribution of the humanities remains underexplored, particularly in digitally mediated university teaching. This paper argues that ancient texts, approached through the lens of the Environmental Humanities and supported by digital pedagogy, can offer a valuable framework for fostering sustainability literacy in higher education. Drawing on a humanities-based pedagogical model, this article explores how practices such as collaborative close reading, ecocritical discussion, narrative mapping, reflective writing, and digital storytelling can help students connect classical representations of nature, fragility, order, and human responsibility with contemporary ecological concerns. These activities encourage the development of sustainability-related competencies—including critical thinking, ethical reflection, interpretive complexity, and ecological awareness—while also supporting Inner Development Goals such as self-awareness, empathy, relational thinking, and responsible action. Based on a conceptual pedagogical model supported by exploratory qualitative evidence from a small-scale higher education course, this paper suggests that digital pedagogy can make sustainability learning in the humanities more dialogic and reflective. In doing so, this article proposes a practice-based pedagogical framework that may help Higher Education Institutions explore ways of embedding sustainability meaningfully beyond traditionally environmental fields. This article’s primary contribution is therefore pedagogical: it presents a humanities-based model for sustainability education while using exploratory qualitative evidence from one course context to illustrate how such a model may support interpretive, ethical, and sustainability-oriented learning. Full article

Background/Objective: Patients with peripheral artery disease (PAD) are known to have poor awareness and understanding of the diagnosis. The role of generative AI chatbots in improving PAD patient education is unknown. Our goal is to compare a generative AI chatbot customized for PAD patient education to publicly available AI chatbots. Methods: This is a cross-sectional comparative evaluation of the responses of four AI chatbots to ten prompts that are commonly asked questions about PAD. The three publicly available AI chatbots were ChatGPT-5, Gemini 2.5 Flash, and Claude Sonnet 4.5. We created a customized, voice AI chatbot for PAD education grounded on curated and prompt-injected guidance called Vascular Education and Resources using Artificial Intelligence, or “VERA.” De-identified chatbot-generated responses to inputs were assessed for readability (Flesch–Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, Simple Measure of Gobbledygook Index, and Average Reading Level Consensus Score), accuracy, comprehensiveness, and patient education quality (Patient Education Materials Assessment Tool; PEMAT) using validated instruments and expert scoring rubrics. Nonparametric statistical testing was used to compare chatbot performance across all evaluation domains. Results: VERA generated the most accessible text compared to the other chatbots and produced responses at a median grade level of 6.6, which was lower than responses from the other chatbots. PAD expert-rated accuracy scores were high across all the chatbots without significant differences between them. Comprehensiveness scores were more varied and demonstrated that VERA was less comprehensive than the other chatbots. PEMAT understandability scores were uniformly high. PEMAT actionability scores were low overall but did not differ significantly across chatbots on post hoc analysis. Conclusions: A generative AI chatbot research tool customized for PAD patient education generates textual information about PAD that is more accessible (mean grade level 6.6) than publicly available AI chatbots without loss of accuracy, albeit with modestly reduced comprehensiveness that reflects intentional simplification for patient-centered communication. Future research will assess the acceptability and feasibility of this research tool to be adopted as part of PAD patient education. Full article

This paper presents the toolset, methodology and procedure followed to create a dataset from battery electric vehicle trajectories, called DEVRT—Dataset of Electric Vehicle Real Trips. Understanding the behaviour of electric vehicles and their battery consumption under real-life conditions and journeys is required in the shift towards the electrification of transport of people and goods. This paper aims to contribute with the provision of real measurements in different types of routes and environmental contexts at the time of driving to support data analytics and modelling techniques, essential for extracting actionable insights from electric vehicle battery consumption. The preparation, on-route and post-processing steps of the followed methodology are depicted. The outcome dataset consists of probe data collected over 4 days following heterogeneous routes performed by four different drivers using two electric vehicles (one more suitable to city usage and the other one more suitable for longer trips). This probe data is complemented with associated road network characterisation information, traffic flow measurements and weather extracted from auxiliary data sources. The paper presents a comprehensive description of the geographical characteristics of the trajectories, qualitative and quantitative characterisation of planned routes to create these trajectories, and criteria used to select them. Full article

Human Papillomavirus (HPV) is one of the most prevalent infections worldwide and a leading cause of cervical cancer, as well as anal, oropharyngeal, penile, vulval, and vaginal cancers. Despite the availability of safe and effective vaccines, coverage beyond female adolescent programmes remains often insufficient, leaving many adolescents and adults unprotected. The World Federation of Public Health Associations (WFPHA) convened a year-long global expert engagement forum to develop evidence-informed policy recommendations to advance HPV elimination. Building on this work, the resulting Call-to-Action urges countries to expand access to boys and adults. Adopting a life-course approach, integrated with screening, equitable access policies, and sustainable financing, can significantly increase coverage and reduce the burden of HPV-related cancers. This article outlines the main outcomes of the Call-to-Action and highlights key priorities for policy and decision makers committed to accelerating HPV elimination. Full article

Carbapenems comprise a class of β-lactam antibiotics with broad-spectrum hydrolytic activity and are often reserved as last-line agents for the treatment of serious multidrug-resistant (MDR) bacterial infections. Clinically important nosocomial MDR Gram-negative bacteria (GNB) include Klebsiella pneumoniae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Carbapenem resistance among these organisms is predominantly mediated by the production of β-lactamases called carbapenemases, such as K. pneumoniae carbapenemase (KPC), New Delhi metallo-β-lactamase (NDM), imipenemase (IMP), Verona integron-encoded metallo-β-lactamase (VIM), and selected oxacillinase (OXA)-type carbapenemases. These enzymes degrade carbapenems, significantly compromising their clinical efficacy. To address escalating antimicrobial resistance, novel next-generation β-lactamase inhibitors (BLIs), partnered with established β-lactams (BLs), have been approved or are currently under development to inhibit carbapenemase activity. The present narrative review aims to synthesize the most current information on the major carbapenemases and discusses recently approved and investigational BL/BLI combination therapies in terms of their mechanisms of action, spectrum of activity, gaps in coverage, and available clinical and in vitro evidence. Development of resistance to novel BL/BLI combinations is also examined. Comparative analysis of inhibitory spectra and microbiological coverage indicates a continued need for metallo-β-lactamase inhibitors with direct pan-inhibitory activity, pathogen-specific BL/BLI regimens for carbapenem-resistant A. baumannii, and carbapenemase-targeted agents effective in the context of non-enzymatic resistance mechanisms. Treatment-emergent resistance to novel BL/BLIs and limitations in activity profiles underscore the critical need for continued innovation in pipeline development, vigilant global and local surveillance of carbapenemase epidemiology, and robust antimicrobial stewardship strategies to aid in preserving the efficacy of the antibacterial drug armamentarium. Full article

This paper analyses the existing international refugee framework in light of the emergence of alternative so-called ‘safe and legal routes’ devised by the UK government—in particular, the bespoke Ukraine visa schemes—and considers the practical implementation of a Safe Passage Visa programme in the UK. It will consider how safe routes may benefit at-risk people when provided alongside the protection afforded under the Refugee Convention. It will also evaluate the persistent failure of UK government policy that focuses on deterrent-only aims but has been unsuccessful in reducing irregular journeys to the UK. The paper will then explore the case for a ‘Safe Passage Visa’, focusing on the practical challenge of implementation and the potential impact of its development on the workings of the asylum system in the UK, including the potential reduction in irregular entry and other benefits. The paper argues that there is an imperative need to take action to safeguard and protect human lives in transit whilst asserting that this must be conducted in a way that complements and enhances the principles enshrined in the Refugee Convention. Full article

In recent years, the number of deaths caused by traffic accidents has continued to rise. According to investigations, approximately one-fifth of accidents are caused by drivers being distracted. With the rapid development of convolutional neural networks (CNNs) in the field of computer vision, many researchers have developed CNN-based network models to recognize distracted driving actions. However, many models have too many parameters, making them unsuitable for deployment in actual vehicles. To address this issue, we propose a multiscale driver distraction detection network called ICK-PANet, which combines attention, lightweight incremental convolution kernels, and lightweight pyramid convolution to quickly and accurately identify driver distraction actions. First, ICK-PANet uses lightweight incremental convolution kernels to capture global information and driving action details effectively. Then, it introduces lightweight pyramid convolution and attention modules to extract multistage features, thereby expanding the network’s receptive field to improve the recognition ability of key features. Finally, it fuses multistage features to predict the results. ICK-PANet was experimentally evaluated on two public datasets: the American University in Cairo Distracted Driver (AUC) dataset and the StateFarms dataset (SFD) provided by the Kaggle competition platform. The AUC and SFD accuracies are 95.66% and 99.84%, respectively, which are higher than those achieved by many other state-of-the-art methods. ICK-PANet requires only 0.4M parameters, making it one of the most lightweight models currently available. Full article

This paper is the first attempt to interpret the visual ‘construemes’ by the constructivist poet Alexei N. Chicherin, published in the anthology Mena vsekh which appeared in Moscow in1924. ‘Construemes’ can be considered the most enigmatic artifacts of the Russian avant-garde. Although ‘construemes’ can be easily confused with meaningless visual zaum (‘the transrational’), Chicherin’s actions and the very nature of his personality prevent one from interpreting ‘construemes’ as actionist endeavors to scandalize or a ‘play on nonsense’. Analysis of the poet’s treatise Kan-Fun published in Moscow in 1926 required finding the key to deciphering the ‘construemes’, reveals the positivist nature of Chicherin’s visual–phonological exercises. In the treatise, the poet argues for the primacy of the eye and vision. He illustrates synthetic ‘signs’ or ‘pictograms’ with the quotidian example of propaganda posters, capable of influencing millions more effectively than words alone. The study emphasizes the enigmatic nature of the titles of Chicherin’s books, the Nietzschean subtexts of his self-presentation, encrypted allusions to the esoteric and magical tradition of the Tarot, and religious symbolism. Sixteen illustrations help the understanding of Chicherin’s logic behind the creation of his four ‘construemes’, including the most mysterious composition called ‘Raman’ (‘the shortest Kan-Fun Novel in the world’). The structure of this text synthesizes the verbal, visual–graphic, acoustic (phonological symbols) and musical (notes) levels. The article also examines Chicherin’s proven techniques: the appropriation of the sacred dimension and self-presentation as an actor possessing genuine knowledge and capable of competing alone with the entire literary environment. Full article

Centering on the debate over whether Fichte’s philosophy of religion underwent a shift between his early and late periods, existing studies have largely taken changes in terminology and content as their basis, understanding it as a move from an early, practical moral religion to a late, ontological religion; other studies maintain that the late period, with themes such as God, being, and love, transcends and develops the early framework. This paper argues that, once we return to the Jena Wissenschaftslehre’s genetic construction of spiritual freedom, the so-called shift does not hold. Through the logical structure of “absolute I–finite I–feeling,” the Jena Wissenschaftslehre provides a model of how the Absolute manifests itself within finite consciousness and completes its actual manifestation in free action. The early philosophy of religion unfolds the same genetic logic in the structure “God–the finite–faith,” while the structure “being–image–love” in the late philosophy of religion represents not a negation of the early view or a move beyond it, but rather a renewed clarification and interpretation of the same logic of spiritual freedom. Full article

Cryptococcal meningitis occurs in 62% of persons with HIV-associated meningitis, making Cryptococcus an important cause of meningitis among adults with advanced HIV disease in regions with elevated prevalence of HIV. Despite efforts to advance treatment, the in-hospital death rate of 19% remains unprecedentedly high. Aggregate published clinical trial data evaluating cryptococcosis treatment with survival as the primary endpoint show a significant reduction in the proportion of survivors from diagnosis to 88.5% at 2 weeks of treatment and further to 74% survival at 10 weeks of follow-up (p = 0.001). Disease complications concomitant with unveiling symptoms and reoccurrence of fungal infections, deferment in treatment, and high prevalence of other comorbidities increase the risk of individuals succumbing to cryptococcal meningitis. Among clinical trials of cryptococcal meningitis, the World Health Organization-recommended standard of care was used to randomize participants to the control trial arm. The proportion of participants surviving in the trial was not statistically different between trial randomization arms. In summary, high in-hospital death rates and continued participants’ deterioration post-hospital discharge are challenges for evidence-based new therapies seeking to improve outcomes. Full article