Search Results (7,139)

Idiopathic pulmonary fibrosis (IPF) is a chronic and irreversible interstitial lung disease characterized by progressive scarring of the lungs. The available therapeutic strategies are limited and primarily focus on slowing disease progression rather than achieving fibrosis reversal. Cardamonin (CDN), a food-derived natural chalcone, has exhibited anti-fibrotic activity in liver and kidney fibrosis models; however, its role and underlying mechanism in IPF remain unelucidated. Herein, we integrated network pharmacology, machine learning, molecular simulations, and in vitro experiments. Network pharmacology identified 135 overlapping targets between CDN and IPF, which demonstrated a significant enrichment in the Phosphatidylinositol 3-Kinase/Protein Kinase B signaling pathway (PI3K/AKT). Machine learning further prioritized 6 core targets, with IGF1 emerging as a key candidate. Molecular docking revealed a favorable binding energy of −7.9 kcal/mol for the CDN-IGF1 complex. Subsequent 100 ns molecular dynamics simulations further confirmed its robust binding stability, yielding a mean binding free energy of −150.978 kcal/mol. In vitro, CDN significantly mitigated fibrosis in bleomycin (BLM)-challenged A549 cells, downregulating the expression of α-smooth muscle actin (α-SMA) and fibronectin. This effect was accompanied by a beneficial reversal of epithelial–mesenchymal transition (EMT), as indicated by increased E-cadherin levels and decreased vimentin expression. Mechanistically, CDN significantly suppressed the IGF1/PI3K/AKT axis; this inhibitory effect was partially reversed by exogenous IGF1 supplementation and further enhanced by the PI3K-specific inhibitor LY294002. This work provides the evidence that CDN alleviates BLM-induced pulmonary fibrosis by targeting the IGF1/PI3K/AKT-EMT axis. These findings lend support to a robust mechanistic basis for developing CDN as a potential therapeutic candidate for IPF. It should be noted that these conclusions are drawn from in vitro experiments using A549 cells, and further validation in primary alveolar epithelial cells and animal models is warranted to confirm their physiological relevance. Full article

Macrocyclic drugs are promising for targeting undruggable proteins, including those in cancer. Our prior work identified BE-43547A₂ (BE) as a selective inhibitor of pancreatic cancer stem cells in PANC-1 cultures, but its high lipophilicity limits clinical application. To address this, we designed derivatives retaining BE’s backbone while modifying tail groups to improve its properties. A concise total synthesis enabled a versatile late-stage intermediate (compound 17), serving as a platform for efficient diversification of BE analogs via modular click chemistry. This approach introduced a central triazole ring connected by flexible alkyl spacers. Key properties, including lipophilicity, solubility, and Caco-2 permeability, were experimentally determined. These derivatives exhibited reduced lipophilicity and improved solubility but unexpectedly lost cellular activity. Direct target engagement studies using MicroScale Thermophoresis (MST) revealed compound-dependent deactivation mechanisms: certain derivatives retained binding to eEF1A1 with only modestly reduced affinity (e.g., compound 29), while others showed no detectable binding (e.g., compound 31). Microsecond-scale molecular dynamics simulations and free-energy calculations showed that, for derivatives retaining target affinity, tail modifications disrupted the delicate balance of drug–membrane and drug–solvent interactions, resulting in substantially higher transmembrane free-energy penalties (>5 kcal/mol) compared to active compounds (<2 kcal/mol). These insights emphasize the need to simultaneously preserve both target engagement and optimal permeability when modifying side chains in cell-permeable macrocyclic peptides, positioning compound 17 as a robust scaffold for future lead optimization. This work furnishes a blueprint for balancing drug-like properties with therapeutic potency in macrocyclic therapeutics. Full article

As wheelchair technology evolves and embraces a more prominent role in assistive technology, the onset of intelligent control systems necessitates a comprehensive review from an engineering perspective. In this work, we analyze the development and the emerging trends in intelligent wheelchair control. A specific focus is provided on classifying and comparing model-driven and data-driven control methodologies. In this review, findings from a range of past contributions are examined, including conventional control theories, rule-based systems, and modern data-driven approaches that include supervised, unsupervised, and reinforcement learning control algorithms. The analysis indicates that while model-driven methods offer interpretability, data-driven techniques—in particular those leveraging machine learning—provide for a superior adaptability for navigating complex and dynamic environments. We further highlight key supporting systems found in sensors, actuators, and human-machine interfaces. Additionally, the important functionalities such as autonomous navigation and obstacle avoidance methods are identified. Our findings point to some future objectives that need to be addressed. For example, energy efficiency, robustness in unpredictable settings, computational requirements, and associated demands when utilizing data-driven methods. One of the highlighted fields of study in this work is the integration of reinforcement learning and sensor fusion, which may hold some promising results for future wheelchair technologies. Full article

A scalable and facile fabrication strategy is presented for developing a flexible, nanostructured, non-enzymatic electrochemical sensor for lactate detection based on copper-modified laser-induced graphene (CuNPs/LIG). A one-step electrodeposition process was employed to uniformly decorate the porous LIG framework with copper nanostructures, offering a cost-effective and reproducible approach for constructing enzyme-free sensing platforms. Scanning electron microscopy and energy-dispersive X-ray spectroscopy confirmed dense Cu nanostructure loading and efficient interfacial integration across the conductive LIG surface. The resulting CuNPs/LIG electrode exhibited excellent electrocatalytic performance, achieving a sensitivity of 8.56 μA µM⁻¹ cm⁻² with a low detection limit of 42.65 μM and a linear response toward lactate concentrations ranging from 100 to 1100 μM in artificial saliva under physiological conditions. The sensor maintained high selectivity in the presence of physiologically relevant interferents. Practical applicability was demonstrated through recovery studies, where recovery rates exceeding 104% showcase the sensor’s analytical reliability in complex biological matrices. Overall, this work establishes a robust, sensitive, and cost-efficient Cu-nanostructured LIG sensing platform, offering strong potential for non-invasive lactate monitoring in real-world biomedical and wearable applications. Full article

High penetration of renewable energy sources (RES) in power systems introduces substantial source-load uncertainty and flexibility challenges, leading to misalignments between economic optimization and environmental sustainability. An edge-side electricity-carbon coordinated hybrid trading mechanism was proposed to enhance flexibility in microgrid clusters. A three-layer time-varying carbon emission factor (CEF) model is developed to quantify negative emissions as tradable Chinese Certified Emission Reductions (CCERs). An endogenous economic equilibrium point enables dynamic switching between Incentive-Based Demand Response during high-carbon periods and Price-Based Demand Response during low-carbon periods, based on marginal profit comparisons. A Wasserstein distance-based distributionally robust CVaR (WDR-CVaR) strategy constructs a data-driven ambiguity set to optimize decisions under worst-case distributional shifts in edge-side data. Simulations on a modified IEEE 33-bus system show that the mechanism increases the Multi-Energy Aggregator’s (MEA) expected profit by 12.3%, reduces carbon emissions by 17.6%, with WDR-CVaR demonstrating superior out-of-sample performance compared to sample average approximation methods. The approach internalizes environmental values through carbon-electricity coupling and edge intelligence, providing a resilient framework for low-carbon distribution network operations. Full article

Robust condition monitoring of wind turbine blades is essential for reducing downtime and maintenance costs, particularly under variable operating conditions. While recent studies suggest that combining trend monitoring (TM) with change point detection (CPD) can improve diagnostic performance, it remains unclear whether such integration is beneficial for all fault types. This study experimentally evaluates the integration of TM and CPD using vibration data from a laboratory-scale wind turbine for two representative blade faults: leading-edge erosion and twist misalignment. For the erosion case, discrete wavelet transform (DWT) energy features exhibit a clear and persistent increase in mid-frequency content, with energy deviations of approximately 34–45% relative to the healthy state. However, Bayesian Online Change Point Detection (BOCPD) does not reveal distinct change points, indicating that CPD provides limited additional value for gradual, steady-state degradation. In contrast, for twist misalignment, the short-time Fast Fourier Transform (FFT) features reveal dynamic spectral redistribution, and CPD applied to spectral centroid trends produces a sharp, localized detection signature. These results demonstrate that integrating TM with CPD significantly enhances fault detectability for dynamic, instability-driven faults, while TM alone is sufficient for smooth, steady-state degradation. This study provides an evidence-based guideline for selectively integrating CPD into wind turbine blade condition monitoring systems based on fault physics. Full article

During the global transition of energy structures toward renewable sources, offshore wind power has experienced rapid advancement, coinciding with increasingly complex wave environments. This study focuses on the wave conditions of an offshore wind farm project in Vietnam. A dual-nested numerical framework (WAVEWATCH III + SWAN) is established, integrated with 32-year (1988–2019) high-resolution WRF wind fields and fused bathymetry data (GEBCO + in situ measurements). This framework overcomes the limitations of short-term datasets (10–22 years) in prior studies and achieves 1′ × 1′ (≈1.8 km) intra-farm resolution—critical for capturing topographic modulation of waves. A systematic analysis of the regional wave climate characteristics is performed, encompassing wave roses, joint distributions of significant wave height and spectral peak period, wave–wind direction correlations, and significant wave height–wind speed relationships. Extreme value theory, specifically the Pearson Type-III distribution, is applied to estimate extreme wave heights and corresponding periods for return periods ranging from 1 to 100 years, yielding critical design wave parameters for wind turbine foundations and support structures. Key findings reveal that the wave climate is dominated by E–SE (90°–120°) monsoon-driven waves (60% of $H_s$ = 0.5–1.5 m), while extreme waves are uniquely concentrated at 120°—attributed to westward Pacific typhoon track alignment and long fetch. For the outmost site (A55, 7.18 m water depth), the 100-year return period significant wave height ($H_{s100}$ = 4.66 m, $T_{p100}$ = 13.05 s) is 38% higher than sheltered shallow-water sites (A28, $H_{s100}$ = 2.7 m), reflecting strong bathymetric control on wave energy. This study makes twofold contributions: (1) Methodologically, it validates a robust framework for long-term wave simulation in tropical monsoon–typhoon regions, combining 32-year high-resolution data with dual-nested models. (2) Scientifically, it reveals the directional dominance and spatial variability of waves in the Mekong estuary, advancing understanding of typhoon–wave–topography interactions. Practically, it provides standardized design parameters (compliant with DNV-OS-J101/IEC 61400-3) for offshore wind projects in Southeast Asia. Full article

The fourth industrial revolution, driven by the Energy Internet (EI), is having a profound impact on economic development and way of life. With the growth of EI networks, the integration of numerous energy devices poses challenges across different domains. To address this, we propose a self-adaptive NSGA-III algorithm (SA-NSGA-III) for multi-objective optimization of the EI topology, accounting for connectivity, robustness, and operational efficiency. We construct an initial scale-free topology based on real-world EI characteristics and optimize it while preserving its scale-free nature. The method incorporates an adaptive dynamic reference point generation strategy and an adaptive population selection mechanism. Experimental results demonstrate that SA-NSGA-III achieves a 29.5% fitness improvement, outperforming other multi-objective optimization algorithms in both optimization performance and convergence efficiency across various network scales and densities. Full article

This review examines the impact of data analytics powered by the Internet of Things (IoT), edge computing, and artificial intelligence (AI) on improving energy efficiency in smart environments, with a focus on smart factories, smart cities, and smart territories. Advanced AI, machine learning (ML), and deep learning (DL) techniques enable real-time energy optimization and intelligent decision-making in complex, data-intensive systems. Integrating edge computing reduces latency and improves responsiveness in IoT and Industrial Internet of Things (IIoT) networks, enabling local energy management and reducing grid load. Federated learning further enhances data privacy and efficiency by enabling decentralized model training across distributed smart nodes without exposing sensitive information or personal data. Emerging 5G and 6G technologies provide the necessary bandwidth and speed for seamless data exchange and control across energy-intensive, connected infrastructures. Blockchain increases transparency, security, and trust in energy transactions and decentralized energy trading in smart grids. Together, these technologies support dynamic demand response mechanisms, predictive maintenance, and self-regulating systems, leading to significant improvements in energy sustainability. Case studies of smart cities and industrial ecosystems within Industry 4.0/5.0/6.0 demonstrate measurable reductions in energy consumption and carbon emissions through these synergistic approaches. Despite significant progress, challenges remain in interoperability, scalability, and regulatory frameworks. This review demonstrates that AI-based edge computing, supported by robust connectivity and secure IoT and IIoT architectures, has a transformative potential for creating energy-efficient and sustainable smart environments. Full article

Sodium-ion batteries (SIBs) have attracted growing attention as an alternative to lithium-ion technologies for electric mobility and stationary energy-storage applications, owing to the wide availability of sodium resources, cost advantages, and comparatively favorable safety characteristics. Accurate state-of-health (SOH) estimation is essential for safe and reliable SIB deployment, yet existing data-driven methods still suffer from limited accuracy and interpretability, as well as a lack of dedicated aging datasets. This study proposes an explainable SOH estimation methodology based on a long short-term memory (LSTM) network combined with model-agnostic KernelSHAP analysis. Thirteen health indicators (HIs) are extracted from charge/discharge data and post-charge relaxation segments, and the most relevant indicators are selected via Pearson correlation screening as model inputs. Built on these HIs, an LSTM-based multi-step framework is developed to take HI sequences as input and forecast the SOH trajectory over the subsequent 20 cycles. Experimental results show that the proposed method achieves high accuracy and robust cross-cell generalization, with mean absolute error (MAE) below 1.0%, root-mean-square error (RMSE) below 1.2% across all cells, and an average RMSE of about 0.75% in the main cross-cell setting. KernelSHAP-based global and temporal analyses further clarify how different HIs and time positions influence SOH estimates, enhancing model transparency and physical interpretability. Full article

In the macro context of promoting sustainable development and achieving net zero emissions, the role of green technology innovation, renewable energy utilization and environmental policy is crucial. However, there is still a lack of consistent empirical evidence regarding the combined emission reduction effect of these three factors in OECD countries. This study aims to empirically examine the combined impact of green technology innovation (GTI), renewable energy consumption (REC), and environmental taxes (ETAX) on carbon dioxide emissions. We expect that the former two will effectively reduce emissions, while the effect of environmental taxes depends on their design. Based on the panel data of 35 OECD economies from 1990 to 2019, this study adopts the augmented mean group (AMG) as the main estimation method, and uses the common correlation mean group (CCEMG) for the robustness test. To control potential endogenous issues, the difference generalized method of moments (GMM) is also employed for estimation. The causal relationship between variables is tested using the Dumitrescu–Herlin method. The results show that, as expected, GTI and REC have a significant negative impact on carbon dioxide reduction. However, ETAX is positively correlated with carbon emissions and does not have statistical significance, which deviates from the ideal policy effect and suggests that there may be efficiency bottlenecks in the current tax design. The causality test further reveals that there is a significant two-way causal relationship between CO₂ emissions and GTI, REC, ETAX, GDP, and fossil fuel consumption (FEC). Therefore, it is recommended that OECD countries give priority to expanding investment in green technologies and renewable energy infrastructure and re-evaluate and optimize environmental tax policies to effectively promote the transition to a low-carbon economy. Full article

Rapid industrialization has generated large volumes of acidic wastewater that, without adequate treatment, pose serious environmental and public health risks. Traditional remediation processes, such as chemical neutralization, ion-exchange, and membrane filtration, are effective but costly, energy-intensive, and generate toxic secondary waste. In contrast, acidophilic microalgae offer a sustainable, cost-effective, and eco-friendly alternative. Algae rely on their cellular structure and metabolism to adsorb, absorb, bioaccumulate, and transform toxic metals while simultaneously neutralizing wastewater with minimal secondary waste production. Although acidophilic algae tolerate highly toxic and low pH conditions, their growth rate and biomass productivity, key drivers of algae-based bioremediation, are often compromised under such conditions. Thus, identifying robust species and evolving strains to thrive in these wastewaters without compromising productivity will facilitate adoption of algae-based bioremediation on a large scale. Integrating algal wastewater remediation with biofuel and biofertilizer production can contribute to the circular economy. In this review, we synthesize mechanisms employed by acidophilic algal strains when exposed to acidic and metal-enriched environments to remediate wastewater. We highlight recent studies applying these strains to acidic wastewater remediation and biogas upgrading and discuss current biotechnological tools aimed at enhancing strain performance for future use in commercial systems. Full article

The increasing electrification of mobility within smart cities has accelerated the need for intelligent energy management strategies that jointly address cost, emissions, and battery health. This study develops a health-aware hybrid reinforcement–predictive energy manager (H-RPEM) designed for photovoltaic–electric vehicle (PV-EV) microgrids. The proposed controller unifies model-based predictive optimisation with adaptive reinforcement learning to achieve both short-term operational efficiency and long-term asset preservation. A comprehensive dataset of solar generation, EV charging behaviour, and stochastic load profiles was employed to train and validate the hybrid control framework under realistic operating conditions. Quantitative results indicate that the proposed H-RPEM controller achieves an 18.7% reduction in total operating cost and a 22.5% decrease in carbon emissions, whilst maintaining the battery state-of-health above 0.95 throughout a 24 h operational cycle. When benchmarked against standard predictive control, the hybrid strategy converges 30–40 episodes faster and delivers a 25% improvement in reward stability, demonstrating enhanced robustness and learning efficiency. The results confirm that H-RPEM achieves robust and balanced performance across economic, environmental, and technical domains, establishing it as a scalable and health-conscious control solution for next-generation smart city microgrids. Full article

Life Cycle Assessment (LCA) is a fundamental methodology for evaluating environmental impacts across the life cycle of products, processes, and services. However, selecting appropriate LCA software is a complex task due to the wide variety of tools, each with different functionalities, sectoral focuses, and technical requirements. This study conducts a systematic literature review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to map the main characteristics, strengths, and limitations of LCA tools. The review includes 41 studies published between 2017 and 2025, identifying and categorizing 24 different tools. Technical and operational features were analyzed, such as modelling capacity, database compatibility, usability, integration capabilities, costs, and user requirements. Among the tools, five stood out for their frequent application: SimaPro, GaBi, OpenLCA, Umberto, and Athena. SimaPro is recognized for flexibility and robustness; GaBi for its industrial applications and Environmental Product Declaration (EPD) support; OpenLCA for being open-source and accessible; Umberto for energy and process modelling; and Athena for integration with Building Information Modelling (BIM) in construction. Despite their advantages, all tools presented specific limitations, including learning curve challenges and limited scope. The results show that no single tool fits all scenarios. In addition to the synthesis of these characteristics, this study also emphasizes the general features of the identified software, the challenges in making a well-supported selection decision, and proposes a decision flowchart designed to guide users through key selection criteria. This visual tool aims to support a more transparent, systematic, and context-oriented choice of LCA software, aligning capabilities with project-specific needs. Tool selection should align with research objectives, available expertise, and context. This review offers practical guidance for enhancing LCA applications in sustainability science. Full article

Accurate pain and stress assessment remains a challenge in patients with limited communication ability. Current galvanic skin response (GSR) devices lack real-time feedback, wireless communication, and robustness against motion artifacts, limiting their clinical utility. This paper presents the design and development of a wearable internet-of-things (IoT) enabled GSR system incorporating Bluetooth Low Energy (BLE) communication, ergonomic mechanical housing, and artifact-filtering through a custom API. The system integrates finger-mounted electrodes, a custom amplifier and signal processor, an nRF52840 BLE microcontroller, and a rechargeable Li-ion battery in a compact 3D-printed wrist-mounted enclosure. Basic validation with two healthy subjects demonstrated reliable detection of stress-induced GSR fluctuations with reduced movement artifacts. Results indicate the feasibility of the proposed design as a low-cost, wireless, and ergonomic solution for objective pain and stress monitoring. Full article