Search Results (249)

To improve the prediction accuracy of soot load in gasoline particulate filters (GPFs) and the control accuracy during GPF regeneration, this study developed a prediction model to predict the soot mass concentration at the GPF inlet of gasoline direct injection (GDI) engines using advanced machine learning methods. Three machine learning approaches, namely, support vector regression (SVR), deep neural network (DNN), and a Stacking integration model of SVR and DNN, were employed, respectively, to predict the soot mass concentration at the GPF inlet. The input data includes engine speed, torque, ignition timing, throttle valve opening angle, fuel injection pressure, and pulse width. Exhaust gas soot mass concentration at the three-way catalyst (TWC) outlet is obtained by an engine bench test. The results show that the correlation coefficients (R²) of SVR, DNN, and Stacking integration model of SVR and DNN are 0.937, 0.984, and 0.992, respectively, and the prediction ranges of soot mass concentration are 0–0.038 mg/s, 0–0.030 mg/s, and 0–0.07 mg/s, respectively. The distribution, median, and data density of prediction results obtained by the three machine learning approaches fit well with the test results. However, the prediction result of the SVR model is poor when the soot mass concentration exceeds 0.038 mg/s. The median of the prediction result obtained by the DNN model is closer to the test result, specifically for data points in the 25–75% range. However, there are a few negative prediction results in the test dataset due to overfitting. Integrating SVR and DNN models through stacked models extends the predictive range of a single SVR or DNN model while mitigating the overfitting of DNN models. The results of the study can serve as a reference for the development of accurate prediction algorithms to estimate soot loads in GPFs, which in turn can provide some basis for the control of the particulate mass and particle number (PN) emitted from GDI engines. Full article

Mycotoxins are responsible for a multitude of diseases in both humans and animals, resulting in significant medical and economic burdens worldwide. Conventional detection methods, such as enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), are highly effective, but they are generally confined to laboratory settings. Consequently, there is a growing demand for point-of-care testing (POCT) solutions that are rapid, sensitive, portable, and cost-effective. Lateral flow assays (LFAs) are a pivotal technology in POCT due to their simplicity, rapidity, and ease of use. This review synthesizes data from 78 peer-reviewed studies published between 2015 and 2024, evaluating advances in nanoparticle-based LFAs for detection of singular or multiplex mycotoxin types. Gold nanoparticles (AuNPs) remain the most widely used, due to their favorable optical and surface chemistry; however, significant progress has also been made with silver nanoparticles (AgNPs), magnetic nanoparticles, quantum dots (QDs), nanozymes, and hybrid nanostructures. The integration of multifunctional nanomaterials has enhanced assay sensitivity, specificity, and operational usability, with innovations including smartphone-based readers, signal amplification strategies, and supplementary technologies such as surface-enhanced Raman spectroscopy (SERS). While most singular LFAs achieved moderate sensitivity (0.001–1 ng/mL), only 6% reached ultra-sensitive detection (<0.001 ng/mL), and no significant improvement was evident over time (ρ = −0.162, p = 0.261). In contrast, multiplex assays demonstrated clear performance gains post-2022 (ρ = −0.357, p = 0.0008), largely driven by system-level optimization and advanced nanomaterials. Importantly, the type of sample matrix (e.g., cereals, dairy, feed) did not significantly influence the analytical sensitivity of singular or multiplex lateral LFAs (Kruskal–Wallis p > 0.05), confirming the matrix-independence of these optimized platforms. While analytical challenges remain for complex targets like fumonisins and deoxynivalenol (DON), ongoing innovations in signal amplification, biorecognition chemistry, and assay standardization are driving LFAs toward becoming reliable, ultra-sensitive, and field-deployable platforms for high-throughput mycotoxin screening in global food safety surveillance. Full article

Despite advances in technique and technology, the chainsaw is still the most widely used tool in forestry. For this reason, equipment manufacturers are developing new technical solutions to make working with a chainsaw as easy and efficient as possible. Some examples of this are the development of professional battery-powered chainsaws and the development of new types of saw chains by the leading industry manufacturers. The aim of this paper was to determine the efficiency of the Stihl MSA 300C battery-powered chainsaw equipped with two different types of professional saw chains (Stihl Rapid Super and Stihl Rapid Hexa) when sawing round wood. The efficiency was determined based on measurements of electricity consumption, sawing speed, sawn wood cross-section, and wood chips and dust mass produced during sawing. The second aim was to determine whether there is a difference in measured vibration magnitude between the two tested saw chains. Fresh-fallen European beech (Fagus sylvatica L.) log, approx. 25 cm diameter without pronounced ellipticity, was used for sampling. Results indicate that although the saw chain manufacturer claims the new type of saw chain (Stihl Rapid Hexa) enables greater efficiency of the chainsaw, this was not the case. Results point to a 37% increase in mean sawing time, as well as a 23% increase in energy consumption when using the Rapid Hexa chain, with statistically significant difference (p ≤ 0.05). It should be emphasized that the manual operation of the chainsaw does not allow for a reliable determination of differences in energy consumption caused by changes in saw chain geometry. The advantages of this saw chain are that it is easier to maintain (sharpen) and significantly less wood chips and dust are produced. The measured vibration magnitude shows a statistically significant difference (p ≤ 0.05), i.e., a lower vibration total value on the front handle when using the Stihl Rapid Hexa chain. Full article

Since the beginning of the 21st century, the supercritical carbon dioxide (sCO₂) Brayton cycle has emerged as a hot topic of research in the energy field. Among its key components, the sCO₂ compressor has received significant attention. In particular, axial-flow sCO₂ compressors are increasingly being investigated as power systems advance toward high power scaling. This paper reviews global research progress in this field. As for performance characteristics, currently, sCO₂ axial-flow compressors are mostly designed with large mass flow rates (>100 kg/s), near-critical inlet conditions, multistage configurations with relatively low stage pressure ratios (1.1–1.2), and high isentropic efficiencies (87–93%). As for internal flow characteristics, although similarity laws remain applicable to sCO₂ turbomachinery, the flow dynamics are strongly influenced by abrupt variations in thermophysical properties (e.g., viscosities, sound speeds, and isentropic exponents). High Reynolds numbers reduce frictional losses and enhance flow stability against separation but increase sensitivity to wall roughness. The locally reduced sound speed may induce shock waves and choke, while drastic variation in the isentropic exponent makes the multistage matching difficult and disperses normalized performance curves. Additionally, the quantitative impact of a near-critical phase change remains insufficiently understood. As for the experimental investigation, so far, it has been publicly shown that only the University of Notre Dame has conducted an axial-flow compressor experimental test, for the first stage of a 10 MW sCO₂ multistage axial-flow compressor. Although the measured efficiency is higher than that of all known sCO₂ centrifugal compressors, the inlet conditions evidently deviate from the critical point, limiting the applicability of the results to sCO₂ power cycles. As for design and optimization, conventional design methodologies for axial-flow compressors require adaptations to incorporate real-gas property correction models, re-evaluations of maximum diffusion (e.g., the DF parameter) for sCO₂ applications, and the intensification of structural constraints due to the high pressure and density of sCO₂. In conclusion, further research should focus on two aspects. The first is to carry out more fundamental cascade experiments and numerical simulations to reveal the complex mechanisms for the near-critical, transonic, and two-phase flow within the sCO₂ axial-flow compressor. The second is to develop loss models and design a space suitable for sCO₂ multistage axial-flow compressors, thus improving the design tools for high-efficiency and wide-margin sCO₂ axial-flow compressors. Full article

Rock slopes, composed of intact rock masses and relatively weak discontinuities, exhibit stability primarily governed by the spatial distribution of these discontinuities. Under the framework of structural control theory, acquiring discontinuity information is a fundamental prerequisite for rock slope stability analysis. However, advancements in measurement methods have significantly enhanced slope modeling precision while paradoxically reducing the efficiency of discontinuity data acquisition. To address this challenge, this study proposes a novel discontinuity identification method on the basis of high-precision UAV (unmanned aerial vehicle) point clouds, integrating principal component analysis (PCA), multi-channel gradient fusion, and cascaded edge detection techniques. Applying this approach, a high-resolution UAV-derived 3D model was constructed, and surface discontinuities were systematically identified for a slope case study in the North Qinling Belt, Shanxi Province, China. Results demonstrate that the proposed method achieves effective discontinuity identification performance, cumulatively detecting 1401 discontinuities. Statistical analysis of the identified discontinuities reveals three dominant orientation groups: I: S085° E/80°, II: S015° W/15°, and III: S005° W/85°. Full article

The increasing demand for smaller batch sizes and mass customisation in production poses considerable challenges to logistics and manufacturing efficiency. Conventional methodologies are unable to address the need for expeditious, cost-effective distribution of premium-quality products tailored to individual specifications. Additionally, the reliability and resilience of global logistics chains are increasingly under pressure. Additive manufacturing is regarded as a potentially viable solution to these problems, as it enables on-demand, on-site production, with reduced resource usage in production. Nevertheless, there are still significant challenges to be addressed, including the assurance of product quality and the optimisation of production processes with respect to time and resource efficiency. This article examines the potential of integrating digital twin methodologies to establish a fully digital and efficient process chain for on-site additive manufacturing. This study focuses on wire arc additive manufacturing (WAAM), a technology that has been successfully implemented in the on-site production of naval ship propellers and excavator parts. The proposed approach aims to enhance process planning efficiency, reduce material and energy consumption, and minimise the expertise required for operational deployment by leveraging digital twin methodologies. The present paper details the current state of research in this domain and outlines a vision for a fully virtualised process chain, highlighting the transformative potential of digital twin technologies in advancing on-site additive manufacturing. In this context, various aspects and components of a digital twin framework for wire arc additive manufacturing are examined regarding their necessity and applicability. The overarching objective of this paper is to conduct a preliminary investigation for the implementation and further development of a comprehensive DT framework for WAAM. Utilising a real-world sample, current already available process steps are validated and actual missing technical solutions are pointed out. Full article

Succenturiate placenta is a rare anatomical variant characterized by one or more accessory lobes connected to the main placental mass by fetal vessels. While frequently asymptomatic, this condition can lead to serious maternal–fetal complications if not diagnosed prenatally. Early detection through advanced ultrasonographic techniques plays a critical role in guiding obstetric management and reducing adverse outcomes. Objective: To describe and analyze the prenatal diagnosis, sonographic characteristics, clinical management, and maternal–fetal outcomes of succenturiate placenta cases diagnosed over a ten-year period at a tertiary care center. Methods: We conducted a retrospective observational study of nine pregnancies diagnosed with succenturiate placenta between 2014 and 2024. Data collected included maternal demographics, ultrasound findings, type of cord insertion, presence of associated anomalies such as velamentous cord insertion or vasa previa, vaginal or cesarean delivery, complications, and neonatal outcomes. Ultrasound evaluation was scored based on a four-point checklist assessing key diagnostic steps. Results: Five of the nine cases (55.6%) presented isolated succenturiate placenta, while four (44.4%) were associated with velamentous cord insertion. No cases of vasa previa were identified. Obstetric outcomes included three vaginal deliveries (33.3%), two instrumental (22.2%), and four cesarean sections (44.4%), one of which was emergent due to fetal distress. Complications occurred in 44.4% of cases, with intrapartum bradycardia being the most common. One neonatal death was reported due to placental abruption. The quality of the ultrasound diagnosis was high in most cases, though transvaginal scanning was inconsistently applied. Conclusions: Prenatal identification of succenturiate placenta via detailed ultrasound, including color Doppler and targeted assessment of cord insertion, is essential to minimize risks associated with this condition. Standardized diagnostic protocols can improve detection rates and enable timely clinical decisions, ultimately improving maternal and neonatal outcomes. Full article

This paper applies a semi-quantitative approach to review the diversity, environmental controls, detection methods, human health risks, and mitigation of cyanotoxins in drinking water systems (DWSs). It discusses the environmental factors controlling the occurrence of cyanotoxins, presents the merits and limitations of emerging methods of their detection (qPCR, liquid chromatography–mass spectrometry, and electrochemical biosensors), and outlines the human exposure pathways and health outcomes with identification of high-risk groups and settings. High-risk groups include (1) communities relying on untreated drinking water from unsafe, polluted water sources and (2) low-income countries where cyanotoxins are not routinely monitored in DWSs. The fate and behavior processes are discussed, including removing cyanotoxins in DWSs based on conventional and advanced treatment processes. The available methods for cyanotoxin removal presented in this paper include (1) polymer-based adsorbents, (2) coagulation/flocculation, (3) advanced oxidation processes, (4) ultra- and nanofiltration, and (5) multi-soil layer systems. Future research should address (1) detection and fate in storage and conveyance facilities and at the point of consumption, (2) degradation pathways and toxicity of by-products or metabolites, (3) interactive health effects of cyanotoxins with legacy and emerging contaminants, (4) removal by low-cost treatment techniques (e.g., solar disinfection, boiling, bio-sand filtration, and chlorination), (5) quantitative health risk profiling of high-risk groups, and (6) epidemiological studies to link the prevalence of human health outcomes (e.g., cancer) to cyanotoxins in DWSs. Full article

Volatile organic compounds (VOCs) in urine headspace are potential biomarkers for different medical conditions, as canines can detect human diseases simply by smelling VOCs. Because dogs can detect disease-specific VOCs, gas chromatography–mass spectrometry (GC–MS) systems may be able to differentiate medical conditions with enhanced accuracy and precision, given they have unprecedented efficiency in separating, quantifying, and identifying VOCs in urine. Advancements in instrumentation have permitted the development of portable GC–MS systems that analyze VOCs at the point of care, but these are designed for environmental monitoring, emergency response, and manufacturing/processing. The purpose of this study is to repurpose the HAPSITE^® ER portable GC–MS for identifying urinary VOC biomarkers. Method development focused on optimizing sample preparation, off-column conditions, and instrumental parameters that may affect performance. Once standardized, the method was used to analyze a urine standard (n = 10) to characterize intra-day reproducibility. To characterize inter-day performance, n = 3 samples each from three volunteers (and the standard) were analyzed each day for a total of four days (n = 48 samples). Results showed the method could detect VOC signals with adequate reproducibility and distinguish VOC profiles from different volunteers with 100% accuracy. Full article

Polyacrylonitrile (PAN) is renowned for its excellent physical and chemical properties, making it a promising candidate for producing high-performance and energetic materials. However, traditional high-molecular-weight PAN suffers from poor solubility and low reactivity, which limits its application as a precursor for advanced materials. To overcome these issues, this study successfully synthesized low-molecular-weight PAN (M_η: 6.808 kDa) using an environmentally friendly aqueous precipitation polymerization method, utilizing ammonium persulfate (6 wt% relative to the monomer mass) as the initiator and isopropanol (400 wt%) as the chain transfer agent. The structures and properties of the synthesized low-molecular-weight PAN were analyzed in depth. The morphology and chain structure of PAN were characterized using field-emission scanning electron microscopy (FE-SEM), Fourier-transform infrared spectroscopy (FT-IR), and nuclear magnetic resonance hydrogen spectroscopy (¹H NMR). The thermal properties were assessed using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Additionally, the state changes during the heating process of PAN with different molecular weights were directly observed using a visual melting point analyzer for the first time. Furthermore, the influence of molecular weight on PAN’s solubility was investigated in detail. Based on that, a linear regression between the viscosity average molecular weight (M_η) and the number average molecular weight (M_n) was established, providing simple and rapid access to the molecular weight of the synthesized PAN via viscosity measurements. Our study employed CTA-controlled aqueous precipitation polymerization to prepare low-molecular-weight PAN, which possesses significant potential in producing tetrazole-based energetic materials. Full article

This study investigates the impact of etching trimming parameters on the multiple harmonics of the mass distribution in hemispherical resonators and proposes a novel 1st harmonic trimming scheme. As mass balancing technology advances, the extension of identification and trimming from frequency split to multiple harmonics remains a challenge. Initially, a multi-harmonic identification scheme based on spurious mode detection was established, considering the influence of the first three harmonics of the mass distribution on the dynamic characteristics of hemispherical resonators. Finite element method modeling and analysis revealed that common structural geometric errors significantly introduce the 1st harmonic. By integrating a rectangular pulse function into the mass distribution function to simulate etching grooves, spectral analysis revealed that groove depth and width determine the amplitude and gradient of introduced harmonics. This research introduces an innovative discrete trimming scheme aimed at addressing the frequency split and mode mismatch issues associated with traditional single-point trimming of the 1st harmonic. By decomposing the trimming task into primary and auxiliary etching grooves, the 4th harmonic introduced by the primary etching is compensated by the secondary 4th harmonic introduced by the auxiliary etching, achieving decoupling of the 1st harmonic from frequency split during the trimming process. The scheme was verified through finite element simulations and experimental testing. Results demonstrate that, for a similar reduction in the 1st harmonic, the variation in frequency split during the discrete trimming process is only 11% of that observed in single-point trimming, facilitating efficient and low-damage trimming of the 1st harmonic. Full article

Severe earthquakes, extreme floods, tragic accidents, mega-fires, and even viruses belong to disasters that can destroy the economic, social, or cultural life of people. Due to the climate crisis, disasters will likely become more frequent and intense over the years. Unmanned aerial vehicles (UAVs/drones) have obtained an increasing role in disaster management, which was particularly evident during the COVID-19 pandemic. However, lack of social acceptability remains a limiting factor of drone usage. Drones as a means of state surveillance—possibly mass surveillance—are subject to certain limits since their advanced monitoring technology, including Artificial Intelligence, may affect human rights, such as the right to privacy. Due to the severity of the pandemic, which has been described as the “ideal state of emergency”, despite the rising use of drones, such privacy concerns have been underestimated so far. At the same time, the existing approach of the European Court of Human Rights (ECtHR) and the Court of Justice of the European Union (CJEU) regarding the COVID-19 health crisis and human rights during emergencies seems rather conservative and, thus, setting limits between conflicting rights in such exceptional circumstances remains vague. Under these conditions, the fear that the COVID-19 pandemic may have become a starting point for transitioning to a world normalizing the exception is evident. Such fear in terms of privacy implies a world with a narrowed scope of privacy; thus, setting questions and exploring the challenges about the future of drone regulation, especially in the European Union, are crucial. Full article

Methanol/diesel hybrid−powered vessels represent a significant advancement in green and low−carbon innovation in the maritime transportation sector and have been widely adopted across various shipping markets. However, the dual−fuel power system modifies the fire load within the engine room compared to traditional vessels, thereby significantly influencing the fire safety of methanol/diesel−powered ships. In this study, anhydrous methanol and light−duty diesel (with 0 °C pour point) were used as fuels to investigate the mixed combustion characteristics of these immiscible fuels in circular pools with diameters of 6, 10, 14, and 20 cm at various mixing ratios. By analyzing the fuel mass loss rate, flame morphology, and heat transfer characteristics, it was determined that methanol and diesel exhibited distinct stratification during combustion, with the process comprising three phases: pure methanol combustion phase, transitional combustion phase, and pure diesel combustion phase. Slopover occurred during the transitional combustion phase, and its intensity decreased as the pool diameter or methanol fuel quantity increased. Based on this conclusion, a quantitative relationship was established between slopover intensity, pool diameter, and the methanol/diesel volume ratio. Additionally, during the transitional combustion phase, the average flame height exhibited an exponential coupling relationship with the pool diameter and the methanol/diesel volume ratio. Therefore, a modification was made to the classical flame height model to account for these effects. Moreover, a prediction model for the burning rate of methanol/diesel pool fires was established based on transient temperature variations within the fuel layer. This model incorporated a correction factor related to pool diameter and fuel mixture ratio. Additionally, the causes of slopover were analyzed from the perspectives of heat transfer and fire dynamics, further refining the physical interpretation of the correction factor. Full article

Beryllium (Be) is one of the most toxic non-radioactive elements on the periodic table, and its presence or intake can negatively impact both the environment and human health. Classified as a carcinogen, Be is dangerous even at trace concentrations, stressing the necessity of developing reliable methods for quantifying it at very low levels. Spectrometric techniques for quantifying Be vary in sensitivity and applicability, with inductively coupled plasma mass spectrometry (ICP-MS) being the most sensitive for ultra-trace analysis. Flame atomic absorption spectrometry (FAAS) is suitable for higher Be concentrations, but preconcentration techniques can significantly lower detection limits. Electrothermal atomic absorption spectrometry (ETAAS) provides enhanced sensitivity for low-level Be quantification, further optimized using pyrolytically coated graphite tubes and chemical modifiers such as Mg(NO₃)₂ or Pd(NO₃)₂. Effective separation and preconcentration techniques are essential for reliable Be quantification in complex matrices. Liquid-liquid extraction (LLE), including single-drop microextraction (SDME) and dispersive liquid-liquid microextraction (DLLME), have evolved to reduce the use of hazardous solvents. When combined with ETAAS, surfactant-assisted DLLME using agents like cetylpyridinium ammonium bromide (CPAB) and dioctyl sodium sulfosuccinate (AOT) achieves preconcentration factors of approximately 25, reducing LOD to 1 ng/L. Vesicle-mediated DLLME coupled with ETAAS further enhances sensitivity, allowing detection limits as low as 0.01 ng/L in seawater. Cloud-point extraction (CPE), often employing Triton X-114, facilitates Be extraction using complexing agents or nanomaterials like graphene oxide. These advancements are critical for accurately quantifying Be at ultra-trace levels in diverse environmental and biological samples, overcoming challenges posed by low analyte concentrations and matrix interferences. Full article

Introduction: Salivary biomarkers have been extensively studied in relation to oral disease, such as periodontal disease, oral cancer, and dental caries, as well as systemic conditions including diabetes, cardiovascular diseases, and neurological disorders. Literature Review: A systematic literature review was conducted, analyzing recent advancements in salivary biomarker research. Databases such as PubMed, Scopus, and Web of Science were searched for relevant studies published in the last decade. The selection criteria included studies focusing on the identification, validation, and clinical application of salivary biomarkers in diagnosing oral and systemic diseases. Various detection techniques, including enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), mass spectrometry, and biosensor technologies, were reviewed to assess their effectiveness in biomarker analysis. Specific biomarkers, such as inflammatory cytokines, oxidative stress markers, and microRNAs, have been identified as reliable indicators of disease progression. Current Trends and Future Perspectives: Advances in proteomics, genomics, and metabolomics have significantly enhanced the ability to analyze salivary biomarkers with high sensitivity and specificity. Despite the promising findings, challenges remain in standardizing sample collection, processing, and analysis to ensure reproducibility and clinical applicability. Conclusions: Future research should focus on developing point-of-care diagnostic tools and integrating artificial intelligence to improve the predictive accuracy of salivary biomarkers. Full article