Search Results (1,429)

Nietzsche claimed, in The Birth of Tragedy, that the Platonic dialogues are the lifeboat on which Socrates rescues older poetry, only to finish off comedy. This article contests that claim. Distinguishing comedy (a mode organized around to geloion, whose object Plato himself defines in the Philebus as self-ignorance) from irony (a two-level device of meaning), it argues that Socratic irony in Plato is comic in a precise threefold sense: genealogically, as the inheritance of the eirōn-mask from the Aristophanic Clouds; structurally, wherever the dialogues frame it for staged laughter; and thematically, as the instrument of the laughing exposure of doxosophia. Drawing on Kierkegaard’s reading of the Socratic maieutic as ‘indirect communication’ and Vlastos’s concept of ‘complex irony,’ the article offers a close reading of Alcibiades’ speech in the Symposium within the generic frame Plato himself names at 222d: the satyr-play, ‘tragedy at play,’ in which tragic material is held inside a laughing form. The speech’s mythological imagery (Silenus, Marsyas, Corybants, Sirens) carries a coordinated dark sub-text that reverses its overt meaning—the satyric signature, not a departure from the comic. Against Vlastos, who locates pedagogical failure in Alcibiades’ self-deception, and Lane, who finds complex irony’s deliberate indeterminacy ethically indefensible, the article proposes a meta-ironic resolution: Plato stages both positions simultaneously, performing at the authorial level the very pharmakon-logic the speech describes—a seriocomic philosophical method grounded in the mythological ambivalence of the theatre of Dionysus. Full article

The electrification of the industrial sector is an important pathway to decarbonizing the industry and achieving a sustainable society. High-temperature heat pumps (HTHPs) are critical devices for providing industrial heat and consuming green electricity. The goal of the present work is the theoretical thermodynamic analysis of a reverse Brayton HTHP that operates with novel working fluids. Specifically, the idea of using mixtures of working fluids with CO₂ is studied for the first time with the aim of suggesting new candidates to increase the performance of industrial HTHPs. A model of an HTHP with an internal heat exchanger is developed and verified in the MATLAB programming language. Nine different mixtures are studied: CO₂/R152a, CO₂/R1234ze(E), CO₂/Propane, CO₂/Butane, CO₂/Isobutane, CO₂/Pentane, CO₂/Isopentane, CO₂/Hexane and CO₂/Heptane. The examined industrial heat production temperatures are 150 °C, 200 °C and 250 °C, while the waste heat stream temperatures that drive the heat pump are considered to be 80 °C and 100 °C. The results prove that the application of the mixtures can enhance the COP, especially in the case of lower temperature lifts. CO₂/R152a seems to be a promising choice compared to pure CO₂, presenting performance enhancements ranging from 4.12% to 64.02% among the studied scenarios. Full article

Accurate comparison of water-surface evaporation observations from different devices is essential for integrating long-term hydrological records. This study analyzed two years of synchronous daily observations from five co-located evaporation devices in Duyun, Guizhou, China: D20, E601, and three evaporation ponds with surface areas of 1, 5, and 20 m² (P1, P5, and P20). Evaporation differences, conversion coefficients, correlations, and statistical variability were evaluated at annual, seasonal, monthly, and daily scales. The main findings were as follows: (1) among the three similarly constructed ponds, annual evaporation decreased from 946 mm for P1 to 896 mm for P5 and 874 mm for P20, whereas the annual totals measured by D20 and E601 were 842 and 861 mm, respectively; (2) the relationship among the three ponds varied within the year, reversing in July and August and becoming non-monotonic in May and June; (3) pairwise correlations were generally strongest in summer and weakest in winter, indicating pronounced seasonal variation in inter-device relationships; and (4) because D20 was installed above ground whereas E601 and the three ponds were buried, differences among all five devices reflected combined scale- and design-related effects, while comparisons among the three ponds primarily represented surface-area-related effects. These findings provide a site-specific basis for harmonizing evaporation records and improving their application in hydrological and water-balance studies. Full article

Gallium (Ga)-based liquid metal (LM) composites, particularly those based on eutectic gallium–indium (EGaIn) and related alloys, have emerged as a promising materials platform for soft and deformable energy storage owing to their unique combination of metallic conductivity, fluidic deformability, and adaptive interfaces. Despite rapid advances in LM-enabled devices, binders remain insufficiently understood and are still commonly regarded as passive structural components. Here, we present a comprehensive binder-centered perspective for LM composites, establishing the binder as a key regulator of electro-chemo-mechanical coupling, interfacial stability, transport behavior, and processability in soft energy systems. We show that tailored binder chemistries in Ga-based LM systems—including stretchable batteries, printable conductors, and soft electrochemical devices—govern LM droplet dispersion, suppress coalescence and leakage, and preserve conductive percolation under large deformation, while enabling room-temperature fabrication and printability through rheological regulation and interfacial wetting. Beyond mechanical confinement, emerging binder functionalities—including dynamic bonding, supramolecular interactions, ionically conductive networks, and reversible polymer architectures—enable self-healing interfaces, adaptive transport pathways, and robust adhesion in deformable devices. By integrating recent advances in stretchable batteries, flexible supercapacitors, printable electronics, and multifunctional soft energy systems, we establish a unified multiscale framework linking binder molecular design to device-level electrochemical and mechanical performance. We further discuss sustainability and manufacturing considerations, including recyclable polymer networks, low-temperature fabrication, and scalable processing strategies. Finally, we outline current challenges and future opportunities toward programmable binder systems with tunable viscoelasticity, interfacial reactivity, and adaptive functionality. This Review establishes binder-centered engineering as a key pathway for transforming LM composites from proof-of-concept materials into resilient, manufacturable, and multifunctional soft energy technologies for wearable, stretchable, and biointegrated electronics. Full article

Proton-exchange-membrane reversible fuel cells (rPEM) are emerging as key technologies for integrated hydrogen-based energy storage systems, enabling both electricity generation and hydrogen production within a single electrochemical device. However, the transition from laboratory-scale characterization to system-level deployment requires a consistent framework linking electrochemical modeling, parameter calibration, and system design. In this work, a semi-empirical electrochemical model of an rPEM cell is developed and calibrated against literature experimental data in both fuel cell (FC) and water electrolysis (WE) modes. The calibrated model achieves high predictive accuracy, with coefficients of determination exceeding 0.997. The validated model is subsequently extended to a preliminary system-level design, enabling the development of a 100 kW reversible PEM system coupled with a 300 kW electrolyzer configuration. The proposed system features symmetric hydrogen flow (6 kg h⁻¹), a 200 kWh hydrogen storage buffer, and operating conditions of 2.5 bar/70 °C in FC mode and 30 bar/65 °C in WE mode. Thermal effects and efficiency trends are analyzed, highlighting the critical role of heat management and balance of plant proposed design. The proposed methodology provides a consistent framework for scaling rPEM technology toward industrial applications. Full article

Atrial fibrillation (AF) is the most prevalent sustained arrhythmia and is increasingly driven by cardiometabolic disease, including type 2 diabetes mellitus (T2DM), obesity, and insulin resistance. These conditions promote atrial electrical instability and a permissive substrate through mitochondrial dysfunction, oxidative stress, inflammation, calcium-handling abnormalities, and profibrotic signaling, culminating in atrial fibrosis and conduction heterogeneity. Metformin, the foundational glucose-lowering therapy for T2DM, exerts pleiotropic actions that intersect with these upstream pathways. Beyond glycemic control, metformin induces mild mitochondrial complex I modulation with reduction of reverse electron transfer-derived reactive oxygen species, activates adenosine monophosphate (AMP) activated protein kinase, and attenuates nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB)-mediated cytokine signaling; experimental data further suggest favorable effects on adiponectin–sarcoendoplasmic reticulum calcium adenosine triphosphatase (SERCA) 2a-dependent calcium cycling, connexin expression, small-conductance Ca²⁺-activated K+ channel remodeling, lipid handling, and transforming growth factor-β (TGF)-β-associated fibrotic remodeling. Observational cohort studies have reported associations between metformin exposure and a modest reduction in incident AF, particularly with longer treatment duration and in higher-risk metabolic phenotypes; device-based surveillance cohorts support a preventive association for new-onset AF rather than reduction of established AF burden. Data after catheter ablation suggest improved freedom from recurrence in metformin-treated patients, whereas evidence in postoperative AF is largely neutral, likely reflecting distinct acute mechanisms. Collectively, metformin may be best conceptualized as a potential substrate-modifying, upstream therapy candidate; however, confounding, exposure misclassification, and heterogeneity in comparators limit causal inference, underscoring the need for prospective randomized trials with AF endpoints. In practice, integration with comprehensive risk-factor modification (blood pressure, weight, sleep apnea, and glycemic optimization) remains essential when considering AF prevention strategies. Full article

Energy and exergy analyses were conducted on a triple-pass seawater reverse osmosis desalination system to evaluate thermodynamic performance and identify primary sources of irreversibility. A comprehensive simulation model, developed in IPSEpro (Version 7.0) and validated against manufacturer data, demonstrated strong agreement with the reported values. Exergetic efficiency of the reverse osmosis (RO) units increased across the passes, from 57% in the first pass to 80% and 78% in the second and third passes, respectively, while exergy destruction decreased correspondingly from approximately 375 kW in the first pass to 120 kW and 130 kW in the second and third passes. The pumping system, particularly the main high-pressure pump, was responsible for 49% of total exergy destruction, followed by the first RO unit at 23%. The impacts of feed water temperature, high-pressure pump pressure, and water recovery ratio (RC) on exergetic efficiency, specific energy consumption, and permeate flow rate were systematically assessed. Increasing the feed water temperature from 15 °C to 33 °C enhanced exergetic efficiency from 27.8% to 29.9% and reduced total exergy destruction from 1622 to 1582 kW, supporting the integration of hybrid RO-thermal desalination systems. The first-pass recovery ratio emerged as the most influential operational parameter overall, with exergetic efficiency rising from 25.1% to 33.7% as RC₁ increased from 0.35 to 0.60. Analysis of the overall recovery ratio identified RC = 0.39 as a practical operating target that balances specific energy consumption of 4.05 kWh/m³ and exergy destruction of 1700 kW, offering the most favourable compromise between energy efficiency and thermodynamic performance. The results presented here provide practical guidance and recommendations for the optimization of the performance of large-scale multi-pass reverse osmosis seawater desalination plants. Full article

Rural distribution networks increasingly face voltage quality challenges due to high penetration of distributed renewable energy, heterogeneous rural load behavior, and long radial feeder structures with limited voltage regulation capability. Photovoltaic generation variability and agricultural load fluctuations can lead to voltage rise, reverse power flow, and branch congestion, particularly in weak rural grids. Conventional deterministic voltage control approaches relying on tap changers and capacitor banks often struggle to maintain stable voltage profiles under stochastic operating conditions. This paper proposes a risk-aware coordinated operation framework for rural multi-energy microgrids that integrates stochastic scenario modeling, voltage state perception, and adaptive optimization-based control. Renewable generation uncertainty and rural load variability are represented through correlated scenario generation and Wasserstein-distance-based scenario reduction, where 100 raw joint photovoltaic-load trajectories are reduced to 20 representative scenarios after convergence and distributional-fidelity tests. A stochastic optimization model is developed to coordinate photovoltaic inverters, battery energy storage systems, demand-side flexibility, and reactive compensation devices while satisfying network power-flow, voltage-security, storage, and communication-delay-aware implementation constraints. To mitigate extreme voltage deviation events, the framework incorporates a Conditional Value-at-Risk formulation that penalizes tail-risk voltage violations and maintains voltages within a preferred operating band of $0.97$–$1.03$ p.u. Case studies on a modified IEEE 33-bus rural distribution system with $2.00$ MW of photovoltaic capacity and $2.50$ MWh of battery storage demonstrate consistent performance improvements across deterministic, risk-neutral stochastic, chance-constrained, and robust baselines. The proposed strategy reduces peak branch loading from $0.95$ in the deterministic benchmark to $0.72$, while the $95\mathrm{th}$ percentile voltage deviation risk decreases from $0.0071$ p.u.${}^2$ to $0.0020$ p.u.${}^2$. Sensitivity, scenario-convergence, scalability, and seasonal representative-day analyses further confirm that the CVaR layer suppresses rare but severe voltage excursions without imposing excessive curtailment or computational burden. Full article

Background/Objectives: We described adverse event reporting patterns for insulin glargine marketed as 100 units/mL (Lantus), 300 units/mL (Toujeo), and 100 units/mL biosimilars (Basaglar/Semglee). Methods: We analyzed Food and Drug Administration Adverse Event Reporting System (FAERS) reports received from 2015 through 2025 that listed insulin glargine as a primary or secondary suspect. Reports were grouped by trade name into standard-strength originator (Lantus), high-strength (Toujeo), and biosimilar products. We defined a composite serious outcome (death, life-threatening event, hospitalization, or disability). Logistic regression adjusted for age, sex, reporter type, and year received; Firth’s penalized regression was used for sparse death and hospitalization outcomes. Sensitivity analyses restricted to reports without concomitant suspect products, overlapping calendar windows, and healthcare professional reports. Geographic reporting patterns were analyzed by country. Results: We included 43,905 eligible reports (Lantus: 35,927; Toujeo: 3699; biosimilars: 4279). Unadjusted death was highest for Lantus (8.2%) followed by biosimilars (6.0%) and Toujeo (3.2%). In the primary adjusted model (n = 28,519), reports listing Toujeo had lower adjusted reporting odds of the composite serious outcome compared with Lantus (aOR 0.88, 95% CI 0.81–0.96), while biosimilars showed higher odds (aOR 1.24, 95% CI 1.11–1.38). The biosimilar association was not robust: it attenuated without concomitant drugs (aOR 1.07, 95% CI 0.92–1.23) and reversed in healthcare professional reports (aOR 0.65, 95% CI 0.49–0.87). Device-related events were lower for biosimilars (aOR 0.58) and Toujeo (aOR 0.85). Geographic heterogeneity was extreme (Egypt 76.9% death vs. Italy 0.8%), and the US protective association reversed in recent years. Conclusions: After adjustment for report-level characteristics, Toujeo was associated with lower reporting odds of serious outcomes, while biosimilar findings were inconsistent across sensitivity analyses and driven by consumer reporting. Geographic heterogeneity and the Egypt outlier underscore the fragility of cross-country comparisons in spontaneous reporting data. These findings reflect reporting-behavior heterogeneity rather than differential product safety. Full article

In modern precision manufacturing systems, multilayer metal structures are key to achieving high-performance devices. However, during actual processing, they are highly prone to interlayer thermal stress concentration and defects such as interface delamination. To thoroughly elucidate and address this stress evolution issue, this study proposes a two-temperature model based on thermomechanical coupling. A thorough analysis of the thermal–mechanical coupling behavior of copper/aluminum two-layer metal films under femtosecond laser irradiation was conducted, investigating non-equilibrium heat transfer within the two-layer material and the resulting stress evolution. The results indicate that stress waves dynamically modulate the temperature distribution, revealing the critical role of thermo-mechanical coupling in energy transfer. Further studies show that stress waves undergo reflection and transmission at material interfaces, with their phases influenced by the acoustic impedance of the materials. When stress waves propagate from a medium with high acoustic impedance to one with low acoustic impedance, the phase of the transmitted wave remains unchanged, while the phase of the reflected wave reverses. Stress unloading occurs during the phase transition; tensile stress at the interface due to reflection can induce delamination, while horizontal stress tends to initiate cracks. This work contributes to the analysis of stress evolution during laser processing of multilayer metals. Full article

Epidermolysis bullosa (EB) comprises a group of rare inherited genodermatoses characterized by fragility and blistering of the skin and mucous membranes, chronic wounding, and significant morbidity including increased risk of squamous cell carcinoma in severe subtypes. Key unmet priorities include reducing diagnostic latency, establishing objective wound monitoring, enabling early detection of malignant transformation within chronic ulcerations, and developing therapies that durably modify disease progression. Artificial intelligence (AI) encompassing machine learning (ML), and deep learning (DL) is increasingly integrated into EB research and clinical practice to address these unmet needs. This structured narrative review synthesises current evidence on AI applications in EB spanning genetic diagnostics, wound assessment, inflammatory endotyping, drug repurposing, and emerging therapeutic technologies, and integrates evidence from registered clinical trials. In genomics, DL-based splicing prediction models and variant prioritisation frameworks accelerate pathogenic variant detection and reduce diagnostic latency. In wound care, convolutional neural networks-based platforms enable automated lesion segmentation and remote monitoring, while multimodal AI models predict healing trajectories and support stratification of wounds by chronicity. Computational transcriptomic analyses have identified candidate repurposing agents by reversing pathogenic gene expression signatures in EB tissue. Emerging convergence of AI with biosensors-integrated wound dressings and three-dimensional bioprinting of genetically corrected skin substitutes represents a transformative future direction. Translational barriers include limited EB-specific training datasets, algorithmic bias across diverse skin phototypes, the interpretability deficit of DL systems, and evolving regulatory frameworks for AI as a medical device. Expansion of internationally interoperable EB disease registries with standardised wound imaging protocols is identified as the single most impactful intervention to accelerate AI adoption. A minimum endpoint set for AI-assisted EB wound assessment, incorporating wound area trajectory, wound type classification, tissue composition, and paired patient-reported pain and itch scores, is proposed to standardise outcome reporting across future studies. Full article

Lithium-ion batteries are the core energy storage devices for electric vehicles, and accurate state of charge (SOC) estimation is critical to ensuring their safe and reliable operation. Most existing SOC estimation methods are only suitable for constant-temperature scenarios and cannot adapt to the dynamic temperature variations in actual charging and discharging processes. To address the issue of insufficient estimation accuracy under complex conditions such as high and low temperatures, this study proposes a Window Attention Sinks Transformer (WASFormer) model. Based on the PatchTST framework, the model integrates Rotary Positional Encoding (RoPE) and Window Attention Sinks (WAS) mechanisms, and combines Huber Loss with Reversible Instance Normalization (RevIN) to establish a full-chain robustness enhancement scheme from feature preprocessing to loss optimization, which effectively suppresses the interference of noise and distribution shift on estimation stability. Comparative experiments, generalization tests, and ablation studies under various temperatures and working conditions show that the proposed model achieves higher estimation accuracy, stronger generalization ability, and robustness. It provides an effective and stable new approach for high-precision SOC estimation of lithium-ion batteries over a wide temperature range and under complex operating conditions. Full article

Recently, aqueous asymmetric supercapacitors (ASCs) have attracted considerable attention as safe and high-power energy storage devices. However, achieving high energy density while maintaining long-term cycling stability remains a significant challenge. Herein, an aqueous ASC employing a Mo_1.33CT_x/CNT negative electrode and a hydrated V₂O₅·nH₂O/CNT positive electrode in a 5 M LiCl electrolyte is reported. The Mo_1.33CT_x i-MXene was synthesized via hydrothermal selective etching of an i-MAX precursor, whereas hydrated V₂O₅·nH₂O nanoflakes were prepared with peroxide-assisted hydrothermal treatment. The ordered-vacancy Mo_1.33CT_x i-MXene provides a stable negative potential window, redox-active sites, and favorable conditions for reversible Li⁺ intercalation/deintercalation, thereby contributing to pseudocapacitive charge storage. The assembled ASC delivered a stable operating voltage of 1.7 V, a specific capacitance of 61 F·g⁻¹ at 1 A·g⁻¹, an energy density of 25.2 Wh·kg⁻¹ at 883 W·kg⁻¹ and 86% capacitance retention after 10,000 cycles. Electrochemical impedance spectroscopy revealed relatively low internal resistance and efficient ion transport within the layered electrode architectures. These results highlight the strong potential of ordered-vacancy MXene/vanadium oxide systems for advanced aqueous energy storage applications. Full article

This work explores the possibility of using 4H-SiC PIN diodes to provide a high electric field able to induce the Stark effect in ⁷Be atoms implanted in the diode space charge region, modifying the ⁷Be radioactive decay time. A set of PIN diodes of area ranging between 2.12 × 10⁻³ cm² and 9.88 × 10⁻³ cm² was designed and fabricated to reach breakdown voltages up to 1000 V. Be ions were implanted in the epitaxial layer, and then the devices were reverse biased at about 75% of the theoretical breakdown voltage for durations exceeding 100 days, long enough for a precise measurement of the ⁷Be radioactive decay time. Electrical characterization in the pristine state, after Be ion implantation, and after long reverse bias allowed us to verify the suitability of 4H-SiC PIN diodes by assessing both the agreement between simulated and measured performance and the stability of the electric field. Be ion implantation-related defects induced both an increase in the reverse current generation and a decrease in the junction capacitance, though not affecting the breakdown voltage. Comparison with test devices implanted with the stable isotope ⁹Be indicates that any defects introduced by the ⁷Be radioactive decay are below the detection limit of the employed characterization techniques and have a negligible impact on the reverse-blocking characteristics of the diodes. Device simulations allowed us to conclude that the electric field remains close to its theoretical value throughout the experiment duration, confirming the suitability of 4H-SiC diodes for both induction and measurement of ⁷Be lifetime variations. Full article

Wearable fitness trackers are increasingly popular for monitoring health-related metrics, yet their ownership patterns across socioeconomic, demographic, and gender-diverse populations remain underexplored at a population level. This study utilized data from the NIH All of Us Research Program to investigate how area-level socioeconomic status, race, and gender identity influence wearable device ownership. Methods. Data were analyzed from 633,547 participants from the All of Us Dataset. Fitbit ownership was modeled with four binary logistic regression models: a demographics-only model, a ZIP3-level socioeconomic indicators model, and a combined model incorporating four demographic × median household income interactions (race, gender, age, and Hispanic/Latino ethnicity), and an intersectional model adding a race x gender interaction. Continuous socioeconomic predictors were rescaled for interpretability (median income per USD 10,000; area-level fractions per 10 percentage points). Socioeconomic-adjusted models were restricted to 606,414 participants with available ZIP3-linked data. Fitbit ownership was defined as having a Fitbit record in the database. Results. Fitbit ownership was observed in 8.34% of the study population. Logistic regression analyses revealed significant demographic disparities: female participants and gender-diverse identities had significantly higher odds of ownership than males (OR = 1.25–2.2). Black or African American (OR = 0.38) and NHPI/MENA (OR = 0.82) participants had lower odds compared to White participants, while Asian (OR = 1.13), more than one race (OR = 1.25), and Hispanic or Latino (OR = 1.25) participants had higher odds. Each USD 10,000 increase in ZIP3 median household income was associated with 12.5% lower odds of ownership overall (OR = 0.875), but this gradient varied significantly by race. For Black or African American participants, the relationship reversed direction (OR = 1.08 per $10,000). A race x gender interaction further showed that female ownership was not uniform across race, being the largest among Black or African American participants (OR = 2.27) and reversed among Asian participants (OR = 0.87). ZIP3 socioeconomic data were structurally unavailable for all American Indian or Alaska Native participants due to the All of Us program’s small-population ZIP3 aggregation policy, precluding their inclusion in socioeconomic-adjusted models. Conclusions. This analysis demonstrates significant gender, racial, and socioeconomic disparities in wearable fitness tracker ownership, showing significantly higher device usage among females and gender-diverse individuals, but lower usage among certain racial groups and a seemingly contradictory negative ownership rates among higher socioeconomic levels. Ownership patterns nonetheless appear more equitable than in consumer cohorts, likely reflecting the device-provision programs undertaken by the NIH. Full article