Search Results (36,614)

Background: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. Methods: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. Results: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. Conclusions: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM. Full article

Alopecia, a prevalent dermatological disorder affecting over half of the global population, is strongly associated with psychological distress. Extracts from Periplaneta americana (L. PA), a medicinal insect resource, exhibit pharmacological activities (e.g., antioxidant, anti-inflammatory, microcirculation improvement) that align with core therapeutic targets for alopecia. This study aimed to systematically investigate the efficacy and mechanisms of PA extracts in promoting hair regeneration. A strategy combining network pharmacology prediction and in vivo experiments was adopted. The efficacy of a Periplaneta americana extract was validated by evaluating hair regrowth status and skin pathological staining in C57BL/6J mice. Transcriptomics, metabolomics, RT-qPCR, and 16s rRNA techniques were integrated to dissect the underlying mechanisms of its hair-growth-promoting effects. PA-011 significantly promoted hair regeneration in depilated mice via multiple mechanisms: enhanced skin superoxide dismutase activity and upregulated vascular endothelial growth factor expression; modulated FOXO/PI3K/AKT signaling pathway and restored skin microbiota homeostasis; and accelerated transition of hair follicles from the telogen to anagen phase. PA-011 exerts hair-promoting effects through synergistic modulation of FOXO/PI3K/AKT signaling and the skin microbiome. As a novel therapeutic candidate, it warrants further systematic investigation for clinical translation. Full article

As urbanization accelerates and climate change intensifies, the ecological integrity of urban riparian forests faces growing threats, underscoring the need for a systematic framework to guide their sustainable management. To address this gap, we developed a causal framework by applying text mining and sentence classification to 1001 abstracts from previous studies, structured within the DPSIR (Driver–Pressure–State–Impact–Response) model. The analysis identified six dominant thematic clusters—water quality, ecosystem services, basin and land use management, climate-related stressors, anthropogenic impacts, and greenhouse gas emissions—which reflect the multifaceted concerns surrounding urban riparian forest research. These themes were synthesized into a structured causal model that illustrates how urbanization, land use, and pollution contribute to ecological degradation, while also suggesting potential restoration pathways. To validate its applicability, the framework was applied to four major urban streams in Seoul, where indicator-based analysis and correlation mapping revealed meaningful linkages among urban drivers, biodiversity, air quality, and civic engagement. Ultimately, by integrating large-scale text mining with causal inference modeling, this study offers a transferable approach to support adaptive planning and evidence-based decision-making under the uncertainties posed by climate change. Full article

Bassia scoparia (Syn. Kochia scoparia (L.) Schrad.) is a medicinal plant whose fruit, Kochiae Fructus, has been extensively studied for its dermatological applications. This study focused on extracts from the whole plant B. scoparia (WPBS), excluding fruits, to address the research gap regarding the medicinal properties of non-fruit parts. The diverse skin benefits of WPBS, including its anti-photoaging, moisturizing, wound healing, anti-inflammatory, and anti-angiogenic effects, were investigated. The WPBS extract enhanced the viability of keratinocytes (HaCaT) without inducing cytotoxic effects. WPBS significantly reduced matrix metalloproteinase-1 (MMP-1) levels and increased collagen type I alpha 1 (COL1A1) levels (p < 0.01) in fibroblasts exposed to ultraviolet B (UVB) radiation, indicating strong anti-photoaging effects. WPBS upregulated skin hydration markers such as aquaporin-3 (AQP3) and hyaluronan synthase-3 (HAS3) and effectively accelerated fibroblast wound closure compared to the positive control. Furthermore, WPBS substantially downregulated the expression of inflammatory (COX-2 and IL-1β) and angiogenic markers (VEGF). Transcriptome analysis (RNA-seq) confirmed that WPBS suppressed inflammation-related and UV-induced gene expression pathways. Overall, these findings expand the therapeutic scope of B. scoparia beyond its traditional fruit use and suggest that WPBS is a promising botanical ingredient for various skin applications. Full article

This paper describes the complex process of rock crushing removal by AWJ impact from the microscopic perspective. The acceleration and deceleration mechanism of abrasive particles throughout the whole process of single abrasive particles impacting rocks, the spherical cavity expansion mechanism of the abrasive particles’ impact on the rock, and the elastic contact force of the collision between the abrasive particles and rock were investigated; a mathematical model of AWJ’s impact on the rock crushing removal conditions was established; and the threshold values of the jet impact parameters were obtained. The mathematical model of the rock crushing removal conditions was verified through numerical simulation and jet impact experiments. The research results show that the theoretical value of the jet impact velocity that meets the conditions for limestone crushing removal is greater than or equal to 36 m/s, and the theoretical value of the pressure is greater than or equal to 2.7 MPa. Numerical simulation was used to obtain the displacement of marked points, stress, and strain variation in marked elements of rock under different impact velocities. The effect of impact rock breaking obtained through the experiment demonstrates the correspondence between the test pressure and the theoretical pressure, which verifies the accuracy of the mathematical model of the rock crushing removal conditions. Full article

In this study, we consider monthly load forecasting, which is an essential decision for energy infrastructure planning and investment. This study focuses on the Texas power grid, where electricity consumption has surged due to rising industrial activity and the increased construction of data centers driven by growing demand for AI. Based on an extensive exploratory data analysis, we identify key characteristics of monthly electricity demand in Texas, including an accelerating upward trend, strong seasonality, and temperature sensitivity. In response, we propose a regression-based forecasting model that incorporates a carefully designed set of input features, including a nonlinear trend, lagged demand variables, a seasonality-adjusted month variable, average temperature of a representative area, and calendar-based proxies for industrial activity. We adopt a rolling forecasting approach, generating 12-month-ahead forecasts for both 2023 and 2024 using monthly data from 2013 onward. Comparative experiments against benchmarks including Holt–Winters, SARIMA, Prophet, RNN, LSTM, Transformer, Random Forest, LightGBM, and XGBoost show that the proposed model achieves superior performance with a mean absolute percentage error of approximately 2%. The results indicate that a well-designed regression approach can effectively outperform even the latest machine learning methods in monthly load forecasting. Full article

Model-based sensorless control of interior permanent-magnet synchronous motors (IPMSMs) typically employs an estimation observer with embedded position information, followed by a position extraction process. Although a type-2 phase-locked loop (PLL) is widely adopted for position and speed extraction, it suffers from steady-state tracking errors under variable-speed operation, leading to torque bias in IPMSM torque control. To mitigate this issue, this paper first proposes an adaptive bandpass full-order observer in the stationary reference frame. Subsequently, a Kalman filter (KF)-based compensation strategy is introduced for the PLL to eliminate tracking errors while maintaining system stability. Experimental validation on a 300 kW platform confirms the effectiveness of the proposed sensorless torque control algorithm, demonstrating significant reductions in position error and torque fluctuations during acceleration and deceleration. Full article

Al-Mg-Si (6XXX) series aluminum alloys are widely applied in aerospace and transportation industries. However, exploring how varying compositions affect alloy properties and deformation mechanisms is often time-consuming and labor-intensive due to the complexity of the multicomponent composition space and the diversity of processing and heat treatments. This study, inspired by the Materials Genome Initiative, employs high-throughput experimentation—specifically the kinetic diffusion multiple (KDM) method—to systematically investigate how the pop-in effect, indentation size effect (ISE), and creep behavior vary with the composition of Al-Mg-Si alloys at room temperature. To this end, a 6016/Al-3Si/Al-1.2Mg/Al KDM material was designed and fabricated. After diffusion annealing at 530 °C for 72 h, two junction areas were formed with compositional and microstructural gradients extending over more than one thousand micrometers. Subsequent solution treatment (530 °C for 30 min) and artificial aging (185 °C for 20 min) were applied to simulate industrial processing conditions. Comprehensive characterization using electron probe microanalysis (EPMA), nanoindentation with continuous stiffness measurement (CSM), and nanoindentation creep tests across these gradient regions revealed key insights. The results show that increasing Mg and Si content progressively suppresses the pop-in effect. When the alloy composition exceeds 1.0 wt.%, the pop-in events are nearly eliminated due to strong interactions between solute atoms and mobile dislocations. In addition, adjustments in the ISE enabled rapid evaluation of the strengthening contributions from Mg and Si in the microscale compositional array, demonstrating that the optimum strengthening occurs when the Mg-to-Si atomic ratio is approximately 1 under a fixed total alloy content. Furthermore, analysis of the creep stress exponent and activation volume indicated that dislocation motion is the dominant creep mechanism. Overall, this enhanced KDM method proves to be an effective conceptual tool for accelerating the study of composition–deformation relationships in Al-Mg-Si alloys. Full article

The impact mechanism of long-term creep in gas-containing coal on coal and gas outbursts has not been fully elucidated and remains insufficiently understood for the purpose of disaster engineering control. This investigation conducted triaxial creep experiments on raw coal specimens under controlled confining pressures, axial stresses, and gas pressures. Through systematic analysis of coal’s physical responses across different loading conditions, we developed and validated a novel creep damage constitutive model for gas-saturated coal through laboratory data calibration. The key findings reveal three characteristic creep regimes: (1) a decelerating phase dominates under low stress conditions, (2) progressive transitions to combined decelerating–steady-state creep with increasing stress, and (3) triphasic decelerating–steady–accelerating behavior at critical stress levels. Comparative analysis shows that gas-free specimens exhibit lower cumulative strain than the 0.5 MPa gas-saturated counterparts, with gas presence accelerating creep progression and reducing the time to failure. Measured creep rates demonstrate stress-dependent behavior: primary creep progresses at 0.002–0.011%/min, decaying exponentially to secondary creep rates below 0.001%/min. Steady-state creep rates follow a power law relationship when subject to deviatoric stress (R² = 0.96). Through the integration of Burgers viscoelastic model with the effective stress principle for porous media, we propose an enhanced constitutive model, incorporating gas adsorption-induced dilatational stresses. This advancement provides a theoretical foundation for predicting time-dependent deformation in deep coal reservoirs and informs monitoring strategies concerning gas-bearing strata stability. This study contributes to the theoretical understanding and engineering monitoring of creep behavior in deep coal rocks. Full article

Flavonoids serve as crucial plant antioxidants in drought tolerance, yet their antioxidant regulatory mechanisms within mycorrhizal plants remain unclear. In this study, using a two-factor design, trifoliate orange (Poncirus trifoliata (L.) Raf.) seedlings in the four-to-five-leaf stage were either inoculated with Funneliformis mosseae or not, and subjected to well-watered (70–75% of field maximum water-holding capacity) or drought stress (50–55% field maximum water-holding capacity) conditions for 10 weeks. Plant growth performance, photosynthetic physiology, leaf flavonoid content and their antioxidant capacity, reactive oxygen species levels, and activities and gene expression of key flavonoid biosynthesis enzymes were analyzed. Although drought stress significantly reduced root colonization and soil hyphal length, inoculation with F. mosseae consistently enhanced the biomass of leaves, stems, and roots, as well as root surface area and diameter, irrespective of soil moisture. Despite drought suppressing photosynthesis in mycorrhizal plants, F. mosseae substantially improved photosynthetic capacity (measured via gas exchange) and optimized photochemical efficiency (assessed by chlorophyll fluorescence) while reducing non-photochemical quenching (heat dissipation). Inoculation with F. mosseae elevated the total flavonoid content in leaves by 46.67% (well-watered) and 14.04% (drought), accompanied by significantly enhanced activities of key synthases such as phenylalanine ammonia-lyase (PAL), chalcone synthase (CHS), chalcone isomerase (CHI), 4-coumarate:coA ligase (4CL), and cinnamate 4-hydroxylase (C4H), with increases ranging from 16.90 to 117.42% under drought. Quantitative real-time PCR revealed that both mycorrhization and drought upregulated the expression of PtPAL1, PtCHI, and Pt4CL genes, with soil moisture critically modulating mycorrhizal regulatory effects. In vitro assays showed that flavonoid extracts scavenged radicals at rates of 30.07–41.60% in hydroxyl radical (•OH), 71.89–78.06% in superoxide radical anion (O₂^•−), and 49.97–74.75% in 2,2-diphenyl-1-picrylhydrazyl (DPPH). Mycorrhizal symbiosis enhanced the antioxidant capacity of flavonoids, resulting in higher scavenging rates of •OH (19.07%), O₂^•− (5.00%), and DPPH (31.81%) under drought. Inoculated plants displayed reduced hydrogen peroxide (19.77%), O₂^•− (23.90%), and malondialdehyde (17.36%) levels. This study concludes that mycorrhizae promote the level of total flavonoids in trifoliate orange by accelerating the flavonoid biosynthesis pathway, hence reducing oxidative damage under drought. Full article

With the acceleration of the pace of life, increased stress levels, and changes in lifestyle factors such as diet and exercise, the incidence of diseases such as cancer and immunodeficiency has been on the rise, which is closely associated with the impaired antioxidant capacity of the body. Polypeptides and polysaccharides derived from edible fungi demonstrate significant strong antioxidant activity and immunomodulatory effects. Auricularia auricula, the second most cultivated mushroom in China, is not only nutritionally rich but also offers considerable health benefits. In particular, its polysaccharides have been widely recognized for their immunomodulatory activities, while its abundant protein content holds great promise as a raw material for developing immunomodulatory peptides. To meet the demand for high-value utilization of Auricularia auricula resources, this study developed a key technology for the stepwise extraction of polypeptides (AAPP1) and polysaccharides (AAPS3) using a composite enzymatic hydrolysis process. Their antioxidant and immunomodulatory effects were assessed using cyclophosphamide (CTX)-induced immune-suppressed mice. The results showed that both AAPP1 and AAPS3 significantly reversed CTX-induced decreases in thymus and spleen indices (p < 0.05); upregulated serum levels of cytokines (e.g., IL-4, TNF-α) and immunoglobulins (e.g., IgA, IgG); enhanced the activities of hepatic antioxidant enzymes SOD and CAT (p < 0.05); and reduced the content of MDA, a marker of oxidative damage. Intestinal microbiota analysis revealed that these compounds restored CTX-induced reductions in microbial α-diversity, increased the abundance of beneficial bacteria (Paramuribaculum, Prevotella; p < 0.05), decreased the proportion of pro-inflammatory Duncaniella, and reshaped the balance of the Bacteroidota/Firmicutes phyla. This study represents the first instance of synergistic extraction of polypeptides and polysaccharides from Auricularia auricula using a single process. It demonstrates their immune-enhancing effects through multiple mechanisms, including “antioxidation-immune organ repair-intestinal microbiota regulation.” The findings offer a theoretical and technical foundation for the deep processing of Auricularia auricula and the development of functional foods. Full article

Ocean Acoustic Tomography (OAT) is an important marine remote sensing technique used for inverting large-scale ocean environmental parameters, but traditional methods face challenges in computational complexity and environmental interference. This paper proposes a causal analysis-driven AI FOR SCIENCE method for high-precision and rapid inversion of oceanic hydrological parameters in complex underwater environments. Based on the open-source VTUAD (Vessel Type Underwater Acoustic Data) dataset, the method first utilizes a fine-tuned Paraformer (a fast and accurate parallel transformer) model for precise classification of sound source targets. Then, using structural causal models (SCM) and potential outcome frameworks, causal embedding vectors with physical significance are constructed. Finally, a cross-modal Transformer network is employed to fuse acoustic features, sound source priors, and environmental variables, enabling inversion of temperature and salinity in the Georgia Strait of Canada. Experimental results show that the method achieves accuracies of 97.77% and 95.52% for temperature and salinity inversion tasks, respectively, significantly outperforming traditional methods. Additionally, with GPU acceleration, the inference speed is improved by over sixfold, aimed at enabling real-time Ocean Acoustic Tomography (OAT) on edge computing platforms as smart hardware, thereby validating the method’s practicality. By incorporating causal inference and cross-modal data fusion, this study not only enhances inversion accuracy and model interpretability but also provides new insights for real-time applications of OAT. Full article

Neurodevelopmental disorders (NDDs) significantly impact children’s health and development. They pose a substantial burden to families and the healthcare system. Challenges in early identification, accurate and timely diagnosis, and effective treatment persist due to overlapping symptoms, lack of appropriate diagnostic biomarkers, significant stigma and discrimination, and systemic barriers. Generative Artificial Intelligence (GenAI) offers promising solutions to these challenges by enhancing screening, diagnosis, personalized treatment, and research. Although GenAI is already in use in some aspects of NDD management, effective and strategic leveraging of evolving AI tools and resources will enhance early identification and screening, reduce diagnostic processing by up to 90%, and improve clinical decision support. Proper use of GenAI will ensure individualized therapy regimens with demonstrated 36% improvement in at least one objective attention measure compared to baseline and 81–84% accuracy relative to clinician-generated plans, customize learning materials, and deliver better treatment monitoring. GenAI will also accelerate NDD-specific research and innovation with significant time savings, as well as provide tailored family support systems. Finally, it will significantly reduce the mortality and morbidity associated with NDDs. This article explores the potential of GenAI in transforming NDD management and calls for policy initiatives to integrate GenAI into NDD management systems. Full article

Background: There is growing evidence suggesting that SARS-CoV-2 may contribute to metabolic dysfunction. SARS-CoV-2 infection is associated with systemic inflammation, oxidative stress, and metabolic dysregulation, all of which may impair liver function and promote glucose intolerance. This study investigated the role of SARS-CoV-2, specifically its Main Protease (M^pro), in accelerating insulin resistance and metabolic dysfunction in HepG2 cells in vitro. Methods: HepG2 cells were treated with varying concentrations of M^pro (2.5, 5, 10, 20, 40, 80, and 160 nmol/mL) for 24 h to assess cytotoxicity and glucose uptake. Based on initial findings, subsequent assays focused on higher concentrations (40, 80, and 160 nmol/mL). The effects of M^pro on cell viability, protein kinase B (AKT) expression, matrix metallopeptidase-1 (MMP1), dipeptidyl peptidase 4 (DPP4), interleukin-6 (IL-6) expression, and lipid peroxidation were investigated. Results: Our findings reveal that the SARS-CoV-2 M^pro treatment led to a concentration-dependent reduction in glucose uptake in HepG2 cells. Additionally, the M^pro treatment was associated with reduced insulin-stimulated AKT activation, particularly at higher concentrations. Inflammatory markers such as IL-6 were elevated in the extracellular medium, while DPP4 expression was decreased. However, extracellular soluble DPP4 (sDPP4) levels did not show a significant change. Despite these changes, cell viability remained relatively unaffected, suggesting that the HepG2 cells were able to maintain overall metabolic functions under M^pro exposure. Conclusions: This study demonstrated the concentration-dependent impairment of hepatic glucose metabolism, insulin signaling, and inflammatory pathways in HepG2 cells acutely exposed to the SARS-CoV-2 M^pro. These findings warrant further investigation to explore the long-term metabolic effects of SARS-CoV-2 and its proteases in the liver and to develop potential therapeutic approaches for post-viral metabolic complications. Full article

Global sequence alignment remains a fundamental technique in bioinformatics, yet its large-scale application is often limited by computational demands. In this work, we explore the impact of optimal global sequence alignment on sequence similarity by combining a GPU-accelerated version of the Needleman–Wunsch algorithm with Sentence-BERT (SBERT), a transformer-based embedding model. This setup allows us to perform exhaustive alignments across large datasets while also capturing semantic similarities that traditional scoring schemes may miss. By comparing alignment scores with SBERT-derived similarity metrics, we examine where classical alignment aligns with—or diverges from—contextual similarity. Our results show that while there is general agreement between the two approaches, notable exceptions highlight cases where semantic meaning extends beyond structural alignment. The combination of efficient global alignment and deep semantic modeling offers new insights into the relationship between sequence form and function, opening the door for more nuanced similarity measures in both biological and textual sequence analysis. Full article