Search Results (7,215)

To address the research gap on water-soluble heavy metals (WSHMs) in Taiyuan, China, we conducted a winter campaign (18–29 January 2019) at an urban site to measure fifteen WSHMs (Zn, Fe, Mn, Ba, Cu, Se, As, Sb, Sn, Pb, Ni, V, Ti, Cd, and Co). The mean concentration of total WSHMs (∑WSHMs) in PM_2.5 was 209.17 ± 187.21 ng m⁻³. Notably, the mass concentrations of ∑WSHMs on heavy pollution days (291.01 ± 170.64 ng m⁻³) were 224.8% higher than those on mild pollution days (89.61 ± 55.36 ng m⁻³). Principal component analysis (PCA) was applied in combination with absolute principal component score–multiple linear regression (APCS-MLR) to analyze pollution sources and their contributions. The results showed that the main sources of pollution were coal combustion and vehicle emissions (42.50%), along with the metallurgical industry and natural dust (34.47%). The carcinogenic and non-carcinogenic risks of WSHMs were assessed for both adults and children based on the United States Environmental Protection Agency’s (U.S. EPA) assessment guidelines and the International Agency for Research on Cancer (IARC) database. Children faced higher non-carcinogenic risks (hazard index = 2.37) than adults (hazard index = 0.30), exceeding the safety threshold (hazard index = 1). The total carcinogenic risk reached 2.20 × 10⁻⁵, exceeding the threshold value (1 × 10⁻⁶) for carcinogenic risk. Water-soluble arsenic (As) dominated both carcinogenic and non-carcinogenic risks in winter and was the riskiest element. These findings provide an essential basis for controlling PM_2.5-bound WSHMs in industrialized areas. Full article

Random parametric errors (RPEs) are introduced into the model establishment of a laminated composite cantilever beam (LCCB) to demonstrate the accuracy and robustness of a recurrent neural network (RNN) in predicting the chaotic vibration of a LCCB, and a comparative analysis of training performance and generalization capability is conducted with a convolutional neural network (CNN). In the process of dynamic modeling, the nonlinear dynamic system of a LCCB is established by considering RPEs. The displacement and velocity time series obtained from numerical simulation are used to train and test the RNN model. The RNN model converts the original data into a multi-step supervised learning format and normalizes it using the MinMaxScaler method. The prediction performance is comprehensively evaluated through three performance indicators: coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results show that, under the condition of introducing RPEs, the RNN model still exhibits high prediction accuracy, with the maximum R² reaching 0.999984548634328, the maximum MAE being 0.075, and the maximum RMSE being 0.121. Furthermore, performing predictions at the free end of the LCCB verifies the applicability and robustness of the RNN model with respect to spatial position variations. These results fully demonstrate the accuracy and robustness of the RNN model in predicting the chaotic vibration of a LCCB. Full article

In this study, we investigated the insecticidal potential of Eucalyptus globulus Labill. and Cymbopogon citratus Stapf essential oils (EOs), both alone and in synergistic blends with their primary active compounds, against adult houseflies (Musca domestica L.). Toxicity assessments were also conducted on non-target organisms—dwarf honeybees (Apis florea Fabricius) and guppies (Poecilia reticulata Peters)—to evaluate environmental safety. All binary EO mixtures demonstrated superior efficacy compared to individual EOs and the synthetic pyrethroid α-cypermethrin (1% positive control). The most potent formulation, combining 2.5% (v/v) geranial with 2.5% (v/v) E. globulus EO, exhibited a synergistic effect, achieving complete fly mortality (LT₅₀: 0.06 h). This mixture’s mortality index significantly exceeded those of single-component formulations, with a mortality index of 0.22, confirming greater toxicity to flies than α-cypermethrin. Importantly, all the tested EOs and their blends were non-toxic to honeybees and guppies; in comparison, α-cypermethrin caused significant harm. These findings highlight the 2.5% (v/v) geranial + 2.5% (v/v) E. globulus EO blend as a highly effective and environmentally friendly alternative to conventional insecticides. Further research is recommended to optimize its formulation for practical use in sustainable fly management. Full article

Colostrum is a nutrient-rich fluid secreted by mammals shortly after birth, primarily to provide passive immunity and support early immune development in newborns. Among its various sources, bovine colostrum is the most widely used supplement due to its high bioavailability, safety profile, and clinically supported health benefits. Rich in immunoglobulins, lactoferrin, growth factors, and antimicrobial peptides, bovine colostrum exhibits diverse biological activities that extend beyond neonatal health. Recently, the rising prevalence of cancer—driven by environmental stressors such as radiation, processed foods, and chronic inflammation, as well as non-environmental hereditary factors including germline mutations, family history, and epigenetic inheritance—has fueled interest in natural adjunctive therapies. Scientific studies have explored the anticancer potential of bovine colostrum, highlighting its ability to modulate immune responses, inhibit tumor growth, induce apoptosis in cancer cells, and reduce inflammation. Key components including lactoferrin and proline-rich peptides have been identified as contributors to these effects. Additionally, bovine colostrum may help reduce the side effects of standard cancer treatments, such as mouth sores from chemotherapy or weakened immune systems, by helping to heal tissues and boost the body’s defenses. While large-scale clinical studies are still needed, current findings suggest that bovine colostrum holds promise as a supportive element in integrative cancer care. In conclusion, bovine colostrum represents a safe, bioactive-rich natural supplement with multifaceted therapeutic potential, particularly in oncology, owing to its key components such as lactoferrin, immunoglobulins, growth factors (e.g., IGF-1, TGF-β), and proline-rich polypeptides (PRPs), which contribute to its immunomodulatory, anti-inflammatory, and potential anticancer effects. Ongoing and future research will be crucial to fully understand its mechanisms of action and establish its role in evidence-based cancer prevention and treatment strategies. Full article

As vital outcomes of agricultural technological innovation, new animal breeds are not only foundational to rural revitalization but also central to preserving ecological diversity. At present, China lacks a clear and coherent legal framework of protection for new animal breeds, making it difficult to accommodate practical demands posed by modern breeding technologies such as gene editing. The results show that international models for protecting intellectual property in new animal breeds generally fall into three categories: granting patents for animal breeds, granting patents for breeding methods, and establishing sui generis rights for animal breeds. The sui generis protecting model of animal breed rights provides stronger protection and better reflects genetic specificity of such breeds. This research recommends that, on ethical review, stricter oversight of animal welfare and genetic data usage should be implemented to promote responsible innovation. On safety assessment, detailed standards should be developed for food and environmental risk assessment to ensure biodiversity and ecological sustainability. On risk balance evaluation, efforts should be made to ensure effective alignment among animal breed rights, animal welfare, and fair competition in the market, while also striking an appropriate balance of interests between breeders and other stakeholders such as farmers, who act as conservers and providers of germplasm resources. Full article

The structural reliability of agricultural machinery is critically dependent on bolted joints, with loosening being a significant and prevalent failure mode. Harsh operational environments (intense vibration, impact, corrosion) severely exacerbate loosening risks, compromising machinery performance and safety. Traditional periodic inspections are inadequate for preventing sudden failures induced by loosening, leading to impaired efficiency and safety hazards. This review comprehensively analyzes the unique challenges and opportunities in monitoring bolted joint reliability within agricultural machinery. It covers the following: (1) the status of bolted joint reliability issues (failure modes, impacts, maintenance inadequacies); (2) environmental challenges to joint integrity; (3) evaluation of conventional detection methods; (4) principles and classifications of modern detection technologies (e.g., vibration-based, acoustic, direct measurement, vision-based); and (5) their application status, limitations, and techno-economic hurdles in agriculture. This review identifies significant deficiencies in current technologies for agricultural machinery bolt loosening surveillance, underscoring the pressing need for specialized, dependable, and cost-effective online monitoring systems tailored for agriculture’s demanding conditions. Finally, forward-looking research directions are outlined to enhance the reliability and intelligence of structural monitoring for agricultural machinery. Full article

Aflatoxins (AFs), harmful secondary metabolites produced by the genus Aspergillus, particularly Aspergillus flavus and Aspergillus parasiticus, are one of the best-known potent mycotoxins, posing a significant risk to public health. The primary type, especially aflatoxin B₁ (AFB₁), is a potent carcinogen associated with liver cancer, immunosuppression, and other health problems. Environmental factors such as high temperatures, humidity, and inadequate storage conditions promote the formation of aflatoxin in staple foods such as maize, peanuts, and rice. Immunocompromised individuals, including those with HIV/AIDS, hepatitis, cancer, or diabetes, are at increased risk due to their reduced detoxification capacity and weakened immune defenses. Chronic exposure to AF in these populations exacerbates liver damage, infection rates, and disease progression, particularly in developing countries and moderate-income populations where food safety regulations are inadequate and reliance on contaminated staple foods is widespread. Biomarkers such as aflatoxin-albumin complexes, urinary aflatoxin M₁, and aflatoxin (AF) DNA adducts provide valuable insights but remain underutilized in resource-limited settings. Despite the globally recognized health risk posed by AF, research focused on monitoring human exposure remains limited, particularly among immunocompromised individuals. This dynamic emphasizes the need for targeted studies and interventions to address the particular risks faced by immunocompromised individuals. This review provides an up-to-date overview of AF exposure in immunocompromised populations, including individuals with cancer, hepatitis, diabetes, malnutrition, pregnant women, and the elderly. It also highlights exposure pathways, biomarkers, and biomonitoring strategies, while emphasizing the need for targeted interventions, advanced diagnostics, and policy frameworks to mitigate health risks in these vulnerable groups. Addressing these gaps is crucial to reducing the health burden and developing public health strategies in high-risk regions. Full article

Smart windows based on liquid crystal (LC) have made significant advancements over the past decade. As critical mediators of outdoor light entering indoor spaces, these windows can dynamically and rapidly adjust their transmittance to adapt to changing environmental conditions, thereby enhancing living comfort. To further improve device performance, reduce energy consumption, and ensure greater safety for everyday use, scientists have recently focused on reducing driving voltage and enhancing contrast ratio, achieving notable progress in these areas. This article provides a concise overview of the fundamental principles and major applications of LC smart windows. It systematically reviews recent advancements over the past two years in improving these two key optical properties for variable transmittance LC smart windows, both internally and externally, and highlights the remaining challenges alongside potential future directions for development. Full article

To study the impact of the psychological and behavioral characteristics of people, fire environment, and evacuation routes on fire evacuation efficiency, this study focuses on a university library as the research subject. A fuzzy logic algorithm is employed to analyze how psychological and behavioral traits influence initial evacuation speed during a fire. Also, fire data simulated using PyroSim software is integrated, with gas temperature, CO concentration, and visibility quantified through empirical formulas to adjust the reduction factor of evacuation speed, examining the effects of fire-generated products on evacuation performance. By incorporating fire environment factors into the heuristic function and refining pheromone update rules through iterative strategies, the ant colony algorithm is enhanced to achieve path planning. Results show that the psychological–environmental-route correction method improves evacuation efficiency by 16.2% compared to traditional methods without correction. This demonstrates that the proposed correction method can improve the efficiency of building fire evacuation and provides theoretical support and technical solutions for future library fire safety management. Full article

Mining activities have long-term impacts on the environment even after the active stage. Historical mines developed in the 19th and 20th centuries for tin, copper, and mainly iron ore are located in the Pitkäranta area (Karelia, Russia). These objects are considered in our research as natural–anthropogenic sites of long-term water–rock interaction. Waters from flooded mines are the subject of this research. Redox conditions, pH, dissolved oxygen content, conductivity, and water temperature were determined during field work. The chemical composition of natural waters was determined by ICP-MS, ICP-AES, ion chromatography, potentiometric titration, and spectrophotometry. Our investigation showed that the mine waters are fresh and predominantly calcium–magnesium hydrocarbonate; most samples showed elevated sulfate ion contents. Circumneutral pH values and the absence of extremely high concentrations of heavy metals indicate neutral mine drainage. However the calculation of the accumulation coefficient showed the highest levels for siderophile elements relative to the corresponding data of the geochemical regional background. Moreover, zinc has the highest content in the series of heavy metal(loid)s considered. The maximum concentration of zinc was determined in the water of one of the shafts of the Lupikko mine, i.e., 5205 µg/L. The accumulation of heavy metals occurs in the process of long-term interaction of water–rock–organic matter under conductive redox conditions. Overall, the research highlighted the relevance of investigating the geochemistry of historical mines in the Pitkäranta area both from the perspective of environmental safety and the preservation of mining sites for scientific and educational purposes. Full article

Dam displacement serves as a critical visual indicator for assessing structural integrity and stability in dam engineering. Data-driven displacement forecasting has become essential for modern dam safety monitoring systems, though conventional approaches—including statistical models and basic machine learning techniques—often fail to capture comprehensive feature representations from multivariate environmental influences. To address these challenges, a bidirectional gated recurrent unit (BiGRU)-enhanced neural network is developed, incorporating sliding window mechanisms to model time-dependent hysteresis characteristics. The BiGRU’s architecture systematically integrates historical temporal patterns through overlapping window segmentation, enabling dual-directional sequence processing via forward–backward gate structures. Validated with four instrumented measurement points from a major concrete gravity dam, the proposed model exhibits significantly better performance against three widely used recurrent neural network benchmarks in displacement prediction tasks. These results confirm the model’s capability to deliver high-fidelity displacement forecasts with operational stability, establishing a robust framework for infrastructure health monitoring applications. Full article

Atopic dermatitis (AD) is a chronic, relapsing inflammatory skin disorder characterized by immune dysregulation and epidermal barrier dysfunction. Advances in understanding the interplay of genetic predisposition, cytokine signaling, and environmental triggers have led to the emergence of targeted therapies. Although biologic agents such as dupilumab, tralokinumab, and lebrikizumab have revolutionized AD management, their high costs, injectable administration, and limited global accessibility highlight the need for alternative options. Small molecule therapies are gaining momentum as they target intracellular pathways central to AD pathogenesis and offer oral or topical administration routes. This review provides a comprehensive analysis of key agents including Janus kinase (JAK) inhibitors (upadacitinib, abrocitinib, baricitinib, ruxolitinib, delgocitinib), phosphodiesterase 4 (PDE4) inhibitors (crisaborole, difamilast, roflumilast, apremilast), as well as STAT6 degraders (KT621, NX3911), aryl hydrocarbon receptor modulators, histamine H4 receptor antagonists (adriforant, izuforant), and sphingosine-1-phosphate receptor modulators (etrasimod, BMS-986166). We summarize their mechanisms of action, pharmacological profiles, and pivotal clinical trial data, emphasizing their potential to address unmet therapeutic needs. Finally, we discuss safety concerns, long-term tolerability, and future directions for integrating small molecule therapies into precision treatment strategies for moderate-to-severe AD. Full article

Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time parameter estimation are essential for ensuring safety, efficiency, and reliability. Traditional empirical and black-box data-driven approaches often fail to account for the underlying physical mechanisms, thereby limiting interpretability and generalizability. To address this, we propose a unified framework that integrates physics-informed scenario-based modeling with data-driven parameter inversion. In the first stage, critical system parameters—such as friction coefficients in drill string movement or contact forces in rail–wheel interactions—are explicitly formulated based on mechanical theory, leveraging symmetries and boundary conditions to improve model structure and reduce computational complexity. In the second stage, model parameters are identified or updated through inverse modeling using historical or real-time field data, enhancing predictive performance and engineering insight. The proposed methodology is demonstrated through two representative cases. The first involves friction estimation during tripping operations in the SU77-XX-32H5 ultra-long horizontal well of the Sulige Gas Field, where a mechanical load model is constructed and field-calibrated. The second applies the framework to rail transit systems, where wheel–rail friction is estimated from dynamic response signals to support condition monitoring and wear prediction. The results from both scenarios confirm that incorporating physical symmetry and data-driven inversion significantly enhances the accuracy, robustness, and interpretability of engineering analyses across domains. Full article

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm significantly outperforms traditional methods in both single-fault and mixed-fault identification, achieving a fault identification accuracy rate of 99.4% on the test set. It maintains high accuracy even in real-world scenarios with superimposed noise and environmental sounds, fully validating its generalization capability and interference resistance, and providing effective technical support for the intelligent maintenance of hydroelectric generator units. Full article

This article investigates the combined effects of different common defects on the structural integrity and operational and environmental safety in the operation of an existing Light Cycle Oil (LCO) storage tank. This study correlates all the tank defects (like corrosion and local plate thinning, deformations, and local stress concentrators) against the loads and their combinations that occur during the tank’s lifetime. All the information gathered by various inspection techniques is used together to create a digital twin of the equipment that will be further analyzed by Finite Element Analysis. A tank condition assessment is a complex activity, and it is based on the experience of the engineer performing it. Since there are multiple methods for performing a comprehensive analysis, starting from the basic visual inspection (which is the most important) and some measurements followed by analytical calculations, up to full wall thickness measurements, 3D scan of deformations and FEA analysis of the tank digital twin, it depends on the engineer performing the evaluation to chose the best method for each particular case from technical and economical point of views. The goal of this article is to demonstrate that analytical and FEA methods have the same result and also to establish a well-determined standard calculation model for future applications. Full article