Search Results (934)

Digestate valorization is essential for sustainable waste management and circular economy strategies, yet large-scale adoption faces technical, economic, and environmental challenges. Beyond waste-to-energy conversion, digestate is a valuable soil amendment, enhancing soil structure and reducing reliance on synthetic fertilizers. However, its agronomic benefits depend on feedstock characteristics, treatment processes, and application methods. This study reviews digestate composition, treatment technologies, regulatory frameworks, and environmental impact assessment through Life Cycle Assessment. It analyzes the influence of functional unit selection and system boundary definitions on Life Cycle Assessment outcomes and the effects of feedstock selection, pretreatment, and post-processing on its environmental footprint and fertilization efficiency. A review of 28 JCR-indexed articles (2018–present) analyzed LCA studies on digestate, focusing on methodologies, system boundaries, and impact categories. The findings indicate that Life Cycle Assessment methodologies vary widely, complicating direct comparisons. Transportation distances, nutrient stability, and post-processing strategies significantly impact greenhouse gas emissions and nutrient retention efficiency. Techniques like solid–liquid separation and composting enhance digestate stability and agronomic performance. Digestate remains a promising alternative to synthetic fertilizers despite market uncertainty and regulatory inconsistencies. Standardized Life Cycle Assessment methodologies and policy incentives are needed to promote its adoption as a sustainable soil amendment within circular economy frameworks. Full article

This study presents an integrated geospatial framework for assessing the risk to high-speed railway (HSR) infrastructure, combining a persistent scatterer interferometric synthetic aperture radar (PS-InSAR) analysis with multi-criteria decision-making in a geographic information system (GIS) environment. Focusing on the Honam HSR corridor in South Korea, the model incorporates both maximum ground deformation and subsidence velocity to construct a dynamic hazard index. Social vulnerability is quantified using five demographic and infrastructural indicators, and a two-stage analytic hierarchy process (AHP) is applied with dependency correction to mitigate inter-variable redundancy. The resulting high-resolution risk maps highlight spatial mismatches between geotechnical hazards and social exposure, revealing vulnerable segments in Gongju and Iksan that require prioritized maintenance and mitigation. The framework also addresses data limitations by interpolating groundwater levels and estimating train speed using spatial techniques. Designed to be scalable and transferable, this methodology offers a practical decision-support tool for infrastructure managers and policymakers aiming to enhance the resilience of linear transport systems. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

Cadmium (Cd) toxicity destroys plant cells and affects plant growth and development. Due to its unique metallic properties, selenium (Se) has been shown to be effective in antioxidants, cellular immunity, and heavy metal detoxification. When Se and Cd are present together in plants, they antagonize. However, the mechanism of action of the two in the rice cell wall remains to be clarified. In this study, we analyzed the mechanism of Cd detoxification by rice (Oryza sativa L.) cellular polysaccharides mediated by Se, using the cell wall as an entry point. Proteomic and transcriptomic analyses revealed that “Glycosyl hydrolases family 17”, “O-methyltransferase”, and “Polygalacturonase” protein pathways were significantly expressed in the cell wall. The most abundant enzymes involved in polysaccharide biosynthesis were found, including bglB, otsB, HK, PFP, ADH1, and ALDH, which resulted in the synthetic pathway of polysaccharide formation in the rice cell wall. Finally, the essential genes/proteins, such as protein Os03g0170500, were identified. The study showed that Se inhibits Cd uptake and transport when Se (1 mg/kg) is low relative to Cd (3 mg/kg), has little inhibitory effect, and even promotes Cd (3 mg/kg) uptake when Se (5 mg/kg) is relatively high. Full article

Highway accessibility plays a vital role in supporting local economic development, particularly in regions lacking access to sea or river ports. Recognizing the functional transformation of road infrastructure, the Chinese government has made substantial investments in its expansion. Nevertheless, a theoretical gap remains in justifying whether such investments yield significant economic returns. Drawing on the theory of economic externalities, this study investigates the causal relationship between highway development and regional economic growth, and assesses whether highway construction leads to an acceleration in growth rates. Utilizing panel data from 14 Chinese cities spanning 2000 to 2014, the synthetic control method (SCM) is employed to evaluate the economic externalities of highway investment. The results indicate a positive impact on surrounding industries. Furthermore, a growth rate forecasting analysis based on Back-Propagation Neural Networks (BPNNs) is conducted using industrial enterprise data from 2005 to 2014. The growth rate in the treated city is 1.144%, which is close to the real number 1.117%, higher than the number for the weighted control group, which is 1.000%. The findings suggest that the growth rate of total industrial output improved significantly, confirming the existence of positive spillover effects. This not only enriches the empirical literature on transport infrastructure but also provides targeted enlightenment for the sustainable development of urban economy in terms of policy guidance. Full article

Background: Sodium–glucose cotransporter 2 (SGLT2) inhibitors have transformed type 2 diabetes mellitus (T2DM) management by promoting glucosuria, lowering glycated hemoglobin (HbA1c), blood pressure, and weight; however, their use is limited by genitourinary infections and ketoacidosis. Phytocannabinoids—bioactive compounds from Cannabis sativa—exhibit multi-target pharmacology, including interactions with cannabinoid receptors, Peroxisome Proliferator-Activated Receptors (PPARs), Transient Receptor Potential (TRP) channels, and potentially SGLT2. Objective: To evaluate the potential of phytocannabinoids as novel modulators of renal glucose reabsorption via SGLT2 and to compare their efficacy, safety, and pharmacological profiles with synthetic SGLT2 inhibitors. Methods: We performed a narrative review encompassing the following: (1) the molecular and physiological roles of SGLT2; (2) chemical classification, natural sources, and pharmacokinetics/pharmacodynamics of major phytocannabinoids (Δ⁹-Tetrahydrocannabinol or Δ⁹-THC, Cannabidiol or CBD, Cannabigerol or CBG, Cannabichromene or CBC, Tetrahydrocannabivarin or THCV, and β-caryophyllene); (3) in silico docking and drug-likeness assessments; (4) in vitro assays of receptor binding, TRP channel modulation, and glucose transport; (5) in vivo rodent models evaluating glycemic control, weight change, and organ protection; (6) pilot clinical studies of THCV and case reports of CBD/BCP; (7) comparative analysis with established synthetic inhibitors. Results: In silico studies identify high-affinity binding of several phytocannabinoids within the SGLT2 substrate pocket. In vitro, CBG and THCV modulate SGLT2-related pathways indirectly via TRP channels and CB receptors; direct IC₅₀ values for SGLT2 remain to be determined. In vivo, THCV and CBD demonstrate glucose-lowering, insulin-sensitizing, weight-reducing, anti-inflammatory, and organ-protective effects. Pilot clinical data (n = 62) show that THCV decreases fasting glucose, enhances β-cell function, and lacks psychoactive side effects. Compared to synthetic inhibitors, phytocannabinoids offer pleiotropic benefits but face challenges of low oral bioavailability, polypharmacology, inter-individual variability, and limited large-scale trials. Discussion: While preclinical and early clinical data highlight phytocannabinoids’ potential in SGLT2 modulation and broader metabolic improvement, their translation is impeded by significant challenges. These include low oral bioavailability, inconsistent pharmacokinetic profiles, and the absence of standardized formulations, necessitating advanced delivery system development. Furthermore, the inherent polypharmacology of these compounds, while beneficial, demands comprehensive safety assessments for potential off-target effects and drug interactions. The scarcity of large-scale, well-controlled clinical trials and the need for clear regulatory frameworks remain critical hurdles. Addressing these aspects is paramount to fully realize the therapeutic utility of phytocannabinoids as a comprehensive approach to T2DM management. Conclusion: Phytocannabinoids represent promising multi-target agents for T2DM through potential SGLT2 modulation and complementary metabolic effects. Future work should focus on pharmacokinetic optimization, precise quantification of SGLT2 inhibition, and robust clinical trials to establish efficacy and safety profiles relative to synthetic inhibitors. Full article

This study explores the spatial and social implications of urban food systems in Quito, Ecuador, focusing on how food access inequalities reflect and reinforce broader urban disparities. The research addresses a critical problem in contemporary urbanization: the disconnection between food provisioning and spatial equity in rapidly growing cities. The objective is to assess and map disparities in food accessibility using a mixed-methods approach that includes field observation, participatory mapping, value chain analysis, and statistical modeling. Five traditional and emerging food markets were studied in diverse districts across the city. A synthetic accessibility function F(x) was constructed to model food access levels, integrating variables such as income, infrastructure, transport availability, and travel time. These variables were subjected to Principal Component Analysis (PCA) and hierarchical clustering to generate three typologies of territorial vulnerability. The results reveal that peripheral areas exhibit lower F(x) values and weaker integration with the formal food system, leading to higher consumer costs and limited fresh food options. In contrast, central districts benefit from multimodal infrastructure and greater diversity of supply. This study concludes that food systems should be treated as critical urban infrastructure. Integrating food equity into land use and mobility planning is essential to promote inclusive, sustainable, and resilient urban development. Full article

Urban Air Mobility (UAM) is envisioned to revolutionize urban transportation by improving traffic efficiency and mitigating surface-level congestion. One of the fundamental challenges in implementing UAM systems lies in the optimal siting of vertiports, which requires a delicate balance among infrastructure construction costs, passenger access costs to their assigned vertiports, and the operational connectivity of the resulting vertiport network. This study develops an integrated mathematical model for vertiport location decision, aiming to minimize total system cost while ensuring UAM network connectivity among the selected vertiport locations. To efficiently solve the problem and improve solution quality, a hybrid genetic algorithm is developed by incorporating a Minimum Spanning Tree (MST)-based connectivity enforcement mechanism, a fundamental concept in graph theory that connects all nodes in a given network with minimal total link cost, enhanced by a greedy initialization strategy. The effectiveness of the proposed algorithm is demonstrated through numerical experiments conducted on both synthetic datasets and the real-world transportation network of New York City. The results show that the proposed hybrid methodology not only yields high-quality solutions but also significantly reduces computational time, enabling faster convergence. Overall, this study provides practical insights for UAM infrastructure planning by emphasizing demand-oriented vertiport siting and inter-vertiport connectivity, thereby contributing to both theoretical development and large-scale implementation in complex urban environments. Full article

The cryopreservation of three-dimensional (3D) biofabricated constructs is a key enabler for their clinical application in regenerative medicine. Unlike two-dimensional (2D) cultures, 3D systems such as encapsulated cell spheroids, molded hydrogels, and bioprinted tissues present specific challenges related to cryoprotectant (CPA) diffusion, thermal gradients, and ice formation during freezing and thawing. This review examines the current strategies for preserving 3D constructs, focusing on the role of biomaterials as cryoprotective matrices. Natural polymers (e.g., hyaluronic acid, alginate, chitosan), protein-based scaffolds (e.g., silk fibroin, sericin), and synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA)) are evaluated for their ability to support cell viability, structural integrity, and CPA transport. Special attention is given to cryoprotectant systems that are free of dimethyl sulfoxide (DMSO), and to the influence of hydrogel architecture on freezing outcomes. We have compared the efficacy and limitations of slow freezing and vitrification protocols and review innovative approaches such as temperature-controlled cryoprinting, nano-warming, and hybrid scaffolds with improved cryocompatibility. Additionally, we address the regulatory and manufacturing challenges associated with developing Good Manufacturing Practice (GMP)-compliant cryopreservation workflows. Overall, this review provides an integrated perspective on material-based strategies for 3D cryopreservation and identifies future directions to enable the long-term storage and clinical translation of engineered tissues. Full article

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

Crustins are a family of cysteine-rich antimicrobial peptides (AMPs), predominantly found in crustaceans, and play important roles in innate immunity. However, among the many reported crustins, few studies have explored their immunomodulatory functions. In this study, we investigated the immune function of a type I crustin (LvCrustinIa-2) in Litopenaeus vannamei, with particular emphasis on comparing the roles of its different domains. LvCrustinIa-2 possesses cationic patchy surface and amphipathic structure, and its expression was significantly induced in hemocytes after pathogen challenge. Both the recombinant LvCrustinIa-2 (rLvCrustinIa-2) and its whey acidic protein (WAP) domain (rLvCrustinIa-2-WAP) exhibited significant inhibitory activities against the tested Gram-positive bacteria. They also showed binding affinity not only for Gram-positive bacteria but also for Gram-negative bacteria. Furthermore, rLvCrustinIa-2 induced membrane leakage and structure damage in the target bacteria. Notably, chemotaxis assays revealed that rLvCrustinIa-2 and the synthetic cysteine-rich region (LvCrustinIa-2-CR) significantly enhanced the chemotactic activity of shrimp hemocytes in vitro. Knockdown of LvCrustinIa-2 triggered significant transcriptional activation of genes involved in calcium transport, inflammation, redox regulation, and NF-κB pathway. Taken together, these findings elucidate the distinct roles of the cysteine-rich region and WAP domain in type Ia crustin and provide the first evidence of a crustacean AMP with chemotactic and immunomodulatory activities. Full article

L-asparaginase (L-ASNase) remains a vital chemotherapeutic agent for acute lymphoblastic leukemia (ALL), primarily due to its mechanism of depleting circulating asparagine essential for leukemic cell proliferation. However, existing ASNases (including pegylated ones) face limitations including immunogenicity, rapid clearance, and off-target toxicities. Earlier, we have shown that the conjugation of L-ASNase with the polyamines and their copolymers results in significant enhancement of the antiproliferative activity due to accumulation in tumor cells. We suggested that this effect is probably mediated by polyamine transport system (PTS) receptors that are overexpressed in ALL cells. Here, we investigated the effect of competitive inhibitors of PTS receptors to the L-ASNase interaction with cancer cells (L5178Y, K562 and A549). L-ASNase from Rhodospirillum rubrum (RrA), Erwinia carotovora (EwA), and Escherichia coli (EcA) were conjugated with natural polyamines (spermine—spm, spermidine—spd, putrescine—put) and a synthetic branched polymer, polyethyleneimine 2 kDa (PEI2 ), using carbodiimide chemistry. Polyamine conjugation with L-ASNase significantly increased enzyme binding and cellular uptake, as quantified by fluorimetry and confocal microscopy. This increased cellular uptake translated into increased cytotoxicity of L-ASNase conjugates. The presence of competitive ligands to PTS receptors decreased the uptake of polyamine-conjugated enzymes-fatty acid derivatives of polyamines produced the strongest suppression. Simultaneously with this suppression, in some cases, competitive ligands to PTS significantly promoted the uptake of the native unconjugated enzymes, “equalizing” the cellular access for native vs conjugated ASNase. The screening for competing inhibitors of PTS receptor-mediated endocytosis revealed spermine and caproate/lipoate derivatives as the most potent inhibitors or antagonists, significantly reducing the cytostatic efficacy of polyamine-conjugated ASNases. The results obtained emphasize the complex, cell-type-dependent and inhibitor-specific nature of these interactions, which highlights the profound involvement of PTS in L-ASNase internalization and cytotoxic activity. These findings support the viability of polyamine conjugation as a strategy to enhance L-ASNase delivery and therapeutic efficacy by targeting the PTS. Full article

Image dehazing technology is a crucial component in the fields of intelligent transportation and autonomous driving. However, most existing dehazing algorithms only process images in the spatial domain, failing to fully exploit the rich information in the frequency domain, which leads to residual haze in the images. To address this issue, we propose a novel Frequency-domain Information Guided Symmetric Dual-branch Dehazing Network (FIGD-Net), which utilizes the spatial branch to extract local haze features and the frequency branch to capture the global haze distribution, thereby guiding the feature learning process in the spatial branch. The FIGD-Net mainly consists of three key modules: the Frequency Detail Extraction Module (FDEM), the Dual-Domain Multi-scale Feature Extraction Module (DMFEM), and the Dual-Domain Guidance Module (DGM). First, the FDEM employs the Discrete Cosine Transform (DCT) to convert the spatial domain into the frequency domain. It then selectively extracts high-frequency and low-frequency features based on predefined proportions. The high-frequency features, which contain haze-related information, are correlated with the overall characteristics of the low-frequency features to enhance the representation of haze attributes. Next, the DMFEM utilizes stacked residual blocks and gradient feature flows to capture local detail features. Specifically, frequency-guided weights are applied to adjust the focus of feature channels, thereby improving the module’s ability to capture multi-scale features and distinguish haze features. Finally, the DGM adjusts channel weights guided by frequency information. This smooths out redundant signals and enables cross-branch information exchange, which helps to restore the original image colors. Extensive experiments demonstrate that the proposed FIGD-Net achieves superior dehazing performance on multiple synthetic and real-world datasets. Full article

In pursuit of zero-emission targets, increasing sustainability concerns have prompted urban centers to adopt more environmentally friendly modes of transportation, notably through the deployment of electric vehicles (EVs). A prominent manifestation of this shift is the transition from conventional fuel-powered buses to electric buses (e-buses), which, despite their environmental benefits, introduce significant operational challenges—chief among them, the management of battery systems, the most critical and complex component of e-buses. The development of efficient and reliable Battery Management Systems (BMSs) is thus central to ensuring battery longevity, operational safety, and overall vehicle performance. This study examines the potential of intelligent BMSs to improve battery health diagnostics, extend service life, and optimize system performance through the integration of simulation, real-time analytics, and advanced deep learning techniques. Particular emphasis is placed on the estimation of battery state of health (SoH), a key metric for predictive maintenance and operational planning. Two widely recognized deep learning models—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—are evaluated for their efficacy in predicting SoH. These models are embedded within a unified framework that combines synthetic data generated by a physics-informed battery simulation model with empirical measurements obtained from real-world battery aging datasets. The proposed approach demonstrates a viable pathway for enhancing SoH prediction by leveraging both simulation-based data augmentation and deep learning. Experimental evaluations confirm the effectiveness of the framework in handling diverse data inputs, thereby supporting more robust and scalable battery management solutions for next-generation electric urban transportation systems. Full article

Machine learning (ML) techniques are becoming increasingly valuable for modeling the transport of pollutants in plant systems. However, two challenges (small sample sizes and a lack of quantitative calculation functions) remain when using ML to predict migration in hydroponic systems. For the bioaccumulation of per- and polyfluoroalkyl substances, we studied the key factors and quantitative calculation equations based on data augmentation, ML, and symbolic regression. First, feature expansion was performed on the input data after data preprocessing; the most important step was data augmentation. The original training set was expanded nine times by combining the synthetic minority oversampling technique and a variational autoencoder. Subsequently, the four ML models were applied to the test set to predict the selected output parameters. Categorical boosting (CatBoost) had the highest prediction accuracy (R² = 0.83). The Shapley Additive Explanation values indicated that molecular weight and exposure time were the most important parameters. We applied three symbolic regression models to obtain accurate prediction equations based on the original and augmented data. Based on augmented data, the high-dimensional sparse interaction equation exhibited the highest accuracy (R² = 0.776). Our results indicate that this method could provide crucial insights into absorption and accumulation in plant roots. Full article