Search Results (8,435)

Graphite has been recognized as a critical material by the United States (US), the European Union (EU), and Australia. Owing to its unique structure and properties, it is utilized in many industries and has played a key role in the clean energy sector, particularly in the lithium-ion battery (LIB) industries. With the projected increase in global graphite demand, driven by the shift to clean energy and the use of EVs, as well as the geographically concentrated production and reserves of natural graphite, interest in graphite recycling has increased, with a specific focus on using spent LIBs and other waste carbon material. Although most established and developing LIB recycling technologies are focused on cathode materials, some have started recycling graphite, with promising results. Based on the different secondary sources and recycling paths reported, hydrometallurgy-based treatment is usually employed, especially for the purification of graphite; greener alternatives are being explored, replacing HF both in lab-scale research and in industry. This offers a viable solution to resource dependency and mitigates the environmental impact associated with graphite production. These developments signal a trend toward sustainable and circular pathways for graphite recycling. Full article

Fyn is widely involved in diverse cellular physiological processes, including cell growth and survival, and has been implicated in the regulation of energy metabolism and the pathogenesis of diabetes mellitus through multiple pathways. Fyn plays a role in increasing fat accumulation and promoting insulin resistance, and it also contributes to the development of diabetic complications such as diabetic kidney disease and diabetic retinopathy. The primary mechanism by which Fyn modulates lipid metabolism is that it inhibits AMP-activated protein kinase (AMPK). Additionally, it affects energy homeostasis through regulating specific signal pathways affecting lipid metabolism including pathways related to CD36, through enhancement of adipocyte differentiation, and through modulating insulin signal transduction. Inflammatory stress is one of the fundamental mechanisms in diabetes mellitus and its complications. Fyn also plays a role in inflammatory stress-related signaling cascades such as the Akt/GSK-3β/Fyn/Nrf2 pathway, exacerbating inflammation in diabetes mellitus. Therefore, Fyn emerges as a promising therapeutic target for regulating glucolipid metabolism and alleviating type 2 diabetes mellitus. This review synthesizes research on the role of Fyn in the regulation of energy metabolism and the development of diabetes mellitus, while exploring its specific regulatory mechanisms. Full article

Maternal undernutrition (MUN) causes severe metabolic disruption in the offspring of mammals. Here we determined the role of histone modification in hepatic gene expression in late-gestation fetuses of nutritionally restricted cows, an established model using low-nutrition (LN) and high-nutrition (HN) conditions. The chromatin immunoprecipitation sequencing results show that genes with an altered trimethylation of histone 3 lysine 4 (H3K4me3) are associated with cortisol synthesis and secretion, the PPAR signaling pathway, and aldosterone synthesis and secretion. Genes with the H3K27me3 alteration were associated with glutamatergic synapse and gastric acid secretion. Compared to HN fetuses, promoter H3K4me3 levels in LN fetuses were higher in GDF15, IRF2BP2, PPP1R3B, and QRFPR but lower in ANGPTL4 and APOA5. Intriguingly, genes with the greatest expression changes (>1.5-fold) exhibited the anticipated up-/downregulation from elevated or reduced H3K4me3 levels; however, a significant relationship was not observed between promoter CpG methylation or H3K27me3 and the gene set with the greatest expression changes. Furthermore, the stress response genes EIF2A, ATF4, DDIT3, and TRIB3 were upregulated in the MUN fetal liver, suggesting activation by upregulated GDF15. Thus, H3K4me3 likely plays a crucial role in MUN-induced physiological adaptation, altering the hepatic gene expression responsible for the integrated stress response and systemic energy metabolism, especially circulating lipoprotein lipase regulation. Full article

For further investigation of the properties of the muon component in the core regions of extensive air showers (EASs), a new underground hodoscopic set-up with a total sensitive area of 22 m${}^2$ was built at the Tien Shan High Mountain Cosmic Ray Station. The hodoscope is based on a set of large-sized scintillation charged particle detectors with an output signal of analog type. The installation ensures a (5–8) GeV energy threshold of muon registration and a ∼${10}^4$ dynamic range for the measurement of the density of muon flux. A program facility was designed that uses modern machine learning techniques for automated search for the typical scintillation pulse pattern in an oscillogram of a noisy analog signal at the output of the hodoscope detector. The program provides a ∼99% detection probability of useful signals, with a relative share of false positives below 1%, and has a sufficient operation speed for real-time analysis of incoming data. Complete verification of the hardware and software tools was performed under realistic operation conditions, and the results obtained demonstrate the correctness of the proposed method and its practical applicability to the investigation of the muon flux in EASs. In the course of the installation testing, a preliminary physical result was obtained concerning the rise of the multiplicity of muon particles around an EAS core in dependence on the primary EAS energy. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

This study examines the energy behavior of a strengthening system consisting of a Fiber Reinforced Polymer (FRP) plate bonded to a rigid substrate and subjected to tensile loading, where the adhesive interface is governed by a bilinear bond–slip law with a vertical descending branch. The investigation focuses on the interaction between the elastic energy stored in the FRP and the adhesive interface, as well as the characteristic lengths that control the debonding process. Analytical expressions for the strain energy stored in both the FRP plate and the adhesive interface are derived, enabling the identification and evaluation of two critical characteristic lengths as the bond stress at the loaded end approaches its maximum value $l_c$, at which the elastic energies of the FRP and the adhesive interface converge, signaling energy saturation; and $l_{max}$, where the adhesive interface attains its peak energy absorption. Upon reaching the energy saturation state, the system undergoes failure through the sudden and complete debonding of the FRP from the substrate. The onset of unstable debonding is rigorously analyzed in terms of the first and second derivatives of the total potential energy with respect to the bond length. It is further demonstrated that abrupt debonding may also occur in cases where the length exceeds $l_c$ when the bond stress reaches its maximum, and the bond–slip law is characterized by a vertical branch. The findings provide significant insights into the energy balance and stability criteria governing the debonding failure mode in FRP-strengthened structures, highlighting the pivotal role of characteristic lengths in predicting both structural performance and failure mechanisms. Full article

Aging, a progressive and time-dependent decline in physiological functions, is driven by interconnected hallmarks, among which mitochondrial dysfunction plays a central role. Mitochondria not only regulate energy production but also play key roles in other cellular processes, including ROS generation, apoptosis, and metabolic signaling—all of which decline with aging. Polyphenols are a diverse group of natural compounds found in fruits, vegetables, tea, and wine; they emerged as promising anti-aging agents due to their ability to modulate several hallmarks of aging, particularly mitochondrial dysfunction. This review explores how various polyphenolic classes influence mitochondrial function and mitigate aging-related decline. These natural compounds have been shown to reduce oxidative stress, increase energy production, and help maintain normal mitochondrial structure. Moreover, in vitro and in vivo studies suggest that polyphenols can delay signs of aging and improve physical and cognitive functions. Overall, polyphenols show great potential to promote healthy aging and even delay the decline in physiological functions by protecting and enhancing mitochondrial health. Full article

Status epilepticus occurs when a seizure lasts more than five minutes or when multiple seizures occur with incomplete return to baseline. SE induces a myriad of pathological changes involving synaptic and extra-synaptic factors. The transition from a self-limiting seizure to a self-sustaining one is established by maladaptive receptor trafficking, whereby GABA_A receptors are progressively endocytosed while glutamatergic receptors (NMDA and AMPA) are transported to the synaptic membrane, causing excitotoxicity and alteration in glutamate-dependent downstream signaling. The subsequent influx of Ca²⁺ exposes neurons to increased levels of [Ca²⁺]i, which overwhelms mitochondrial buffering, resulting in irreversible mitochondrial membrane depolarization and mitochondrial injury. Oxidative stress resulting from mitochondrial leakage and increased production of reactive oxygen species activates the inflammasome and induces a damage-associated molecular pattern. Neuroinflammation perpetuates oxidative stress and exacerbates mitochondrial injury, thereby jeopardizing mitochondrial energy supply in a state of accelerated ATP consumption. Additionally, Ca²⁺ overload can directly damage neurons by activating enzymes involved in the breakdown of proteins, phospholipids, and nucleic acids. The cumulative effect of these effector pathways is neuronal injury and neuronal death. Surviving neurons undergo long-term alterations that serve as a substrate for epileptogenesis. This review highlights the multifaceted mechanisms underlying SE self-sustainability, pharmacoresistance, and subsequent epileptogenesis. Full article

Flexibility is advocated as an effective solution to address the growing need to alleviate grid congestion, necessitating efficient energy management strategies for industrial operations. This paper presents a mixed-integer linear programming (MILP)-based optimization framework for a flexible asset in an industrial setting, aiming to minimize operational costs and enhance energy efficiency. The method integrates dynamic pricing and real-time grid analysis, alongside a state estimation model using Extended Kalman Filtering (EKF) that improves the accuracy of system state predictions. Model Predictive Control (MPC) is employed for real-time adjustments. A real-world case studies from aquaculture industries and industrial power grids in Denmark demonstrates the approach. By leveraging dynamic pricing and grid signals, the system enables adaptive pump scheduling, achieving a 27% reduction in energy costs while maintaining voltage stability within 0.95–1.05 p.u. and ensuring operational safety. These results confirm the effectiveness of grid-aware, flexible control in reducing costs and enhancing stability, supporting the transition toward smarter, sustainable industrial energy systems. Full article

With the rapid development of information technology and semiconductor technology, the iteration speed of electronic devices has accelerated in an unprecedented manner, and the market demand for miniaturized, highly integrated, and highly intelligent devices continues to rise. But when these electronic devices operate at high power, the electronic components generate a large amount of integrated heat. Due to the limitations of existing heat dissipation channels, the current heat dissipation performance of electronic packaging materials is struggling to meet practical needs, resulting in heat accumulation and high temperatures inside the equipment, seriously affecting operational stability. For electronic devices that require high energy density and fast signal transmission, improving the heat dissipation capability of electronic packaging materials can significantly enhance their application prospects. In order to improve the thermal conductivity of composite materials, hexagonal boron nitride (h-BN) was selected as the thermal filling material in this paper. The BMI resin was structurally modified through molecular structure design. The results showed that the micro-branched structure and h-BN synergistically improved the thermal conductivity and insulation performance of the composite material, with a thermal conductivity coefficient of 1.51 W/(m·K) and a significant improvement in insulation performance. The core mechanism is the optimization of the dispersion state of h-BN filler in the matrix resin through the free volume in the micro-branched structure, which improves the thermal conductivity of the composite material while maintaining high insulation. Full article

The enteroendocrine system of the gastrointestinal (GI) tract is the largest endocrine organ in the human body, playing a central role in the regulation of hunger, satiety, digestion, and energy homeostasis. Numerous factors—including dietary components, physical activity, and the gut microbiota—affect the secretion of GI hormones. This study aims to analyze how these factors modulate enteroendocrine function and influence systemic metabolic regulation. This review synthesizes the current scientific literature on the physiology and distribution of enteroendocrine cells and mechanisms of hormone secretion in response to macronutrients, physical activity, and microbial metabolites. Special attention is given to the interactions between gut-derived signals and central nervous system pathways involved in appetite control. Different GI hormones are secreted in specific regions of the digestive tract in response to meal composition and timing. Macronutrients, particularly during absorption, stimulate hormone release, while physical activity influences hormone concentrations, decreasing ghrelin and increasing GLP-1, PYY, and leptin levels. The gut microbiota, through fermentation and metabolite production (e.g., SCFAs and bile acids), modulates enteroendocrine activity. Species such as Akkermansia muciniphila are associated with improved gut barrier integrity and enhanced GLP-1 secretion. These combined effects contribute to appetite regulation and energy balance. Diet composition, physical activity, and gut microbiota are key modulators of gastrointestinal hormone secretion. Their interplay significantly affects appetite regulation and metabolic health. A better understanding of these relationships may support the development of personalized strategies for managing obesity and related disorders. Full article

Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R² = 0.86; Q²_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC₅₀ > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential. Full article

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows. Full article

Electromagnetic and magnetic devices are increasingly prevalent in sectors such as transportation, industry, and renewable energy due to the ongoing electrification trend. These devices exhibit nonlinear behavior, particularly under signals rich in harmonics. They require precise and appropriate modeling for accurate sizing. Identifying model-specific parameters, which depend on frequency, is crucial. This article focuses on a specific frequency range where a circuit model with series resistance and inductance, along with a parallel resistance to account for iron losses (${\mathrm{R}}_{\mathrm{iron}}$), is applicable. While the determination of series elements is well documented, the determination of ${\mathrm{R}}_{\mathrm{iron}}$ remains complex and debated, with traditional methods neglecting operating conditions such as magnetic saturation. To address these limitations, an innovative experimental method is proposed, comprising two main steps: determining the complex impedance of the magnetic device and extracting ${\mathrm{R}}_{\mathrm{iron}}$ from the model. This method aims to provide a more precise and representative estimation of ${\mathrm{R}}_{\mathrm{iron}}$, improving the reliability and accuracy of electromagnetic and magnetic device simulations and designs. The obtained values of the iron loss equivalent resistance are different by at least 300% than those obtained by an impedance analyzer. The proposed method is expected to advance the understanding and modeling of losses in electromagnetic and magnetic devices, offering more robust tools for engineers and researchers in optimizing device performance and efficiency. Full article

Accurately predicting how quickly people will adopt electric vehicles (EVs) is vital for planning charging stations, managing supply chains, and shaping climate policy. We present a forecasting model that uses three separate Long Short-Term Memory (LSTM) branches—one for past EV sales, one for infrastructure and policy signals, and one for economic trends. An attention mechanism first highlights the most important weeks in each branch, then decides which branch matters most at any point in time. Trained end-to-end on publicly available data, the model beats traditional statistical methods and newer deep learning baselines while remaining small enough to run efficiently. An ablation study shows that every branch and both attention steps improve accuracy, and that adding policy and economic data helps more than relying on EV history alone. Because the network is modular and its attention weights are easy to interpret, it can be extended to produce confidence intervals, include physical constraints, or forecast adoption of other clean-energy technologies. Full article