Search Results (52,794)

Contamination of food with heavy metals is an important factor leading to serious health concerns. Rapid identification of these heavy metals is of utmost priority. There are several methods to identify traces of heavy metals in food. Conventional methods for the detection of heavy metal residues have their limitations in terms of cost, analysis time, and complexity. In the last decade, voltammetric analysis has emerged as the most prominent electrochemical determination method for heavy metals. Voltammetry is a reliable, cost-effective, and rapid determination method. This review provides a detailed primer on recent advances in the development and application of graphene-based electrochemical sensors for heavy metal monitoring over the last decade. We critically examine aspects of graphene modification (fabrication process, stability, cost, reproducibility) and analytical properties (sensitivity, selectivity, rapid detection, lower detection, and matrix effects) of these sensors. Furthermore, to our knowledge, meta-analyses were performed for the first time for all investigated parameters, categorized based on graphene materials and heavy metal types. We also examined the pass–fail criteria according to the WHO drinking water guidelines. In addition, the effects of heavy metal toxicity on human health and the environment are discussed. Finally, the contribution of heavy metal contamination to the seventeen Sustainable Development Goals (SDGs) stated by the United Nations in 2015 is discussed in detail. The results confirm the significant impact of heavy metal contamination across twelve SDGs. This review critically examines the existing knowledge in this field and highlights significant research gaps and future opportunities. It is intended as a resource for researchers working on graphene-based electrochemical sensors for the detection of heavy metals in food safety, with the ultimate goal of improving consumer health protection. Full article

With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering. Full article

Background/Objectives: Diffuse Large B-Cell Lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma and demands precise segmentation and classification of nuclei for effective diagnosis and disease severity assessment. This study aims to evaluate the performance of HoVerNet, a deep learning model, for nuclei segmentation and classification in CMYC-stained whole slide images and to assess its integration into a user-friendly diagnostic tool. Methods: A dataset of 122 CMYC-stained whole slide images (WSIs) was used. Pre-processing steps, including stain normalization and patch extraction, were applied to improve input consistency. HoVerNet, a multi-branch neural network, was used for both nuclei segmentation and classification, particularly focusing on its ability to manage overlapping nuclei and complex morphological variations. Model performance was validated using metrics such as accuracy, precision, recall, and F1 score. Additionally, a graphic user interface (GUI) was developed to incorporate automated segmentation, cell counting, and severity assessment functionalities. Results: HoVerNet achieved a validation accuracy of 82.5%, with a precision of 85.3%, recall of 82.6%, and an F1 score of 83.9%. The model showed powerful performance in differentiating overlapping and morphologically complex nuclei. The developed GUI enabled real-time visualization and diagnostic support, enhancing the efficiency and usability of DLBCL histopathological analysis. Conclusions: HoVerNet, combined with an integrated GUI, presents a promising approach for streamlining DLBCL diagnostics through accurate segmentation and real-time visualization. Future work will focus on incorporating Vision Transformers and additional staining protocols to improve generalizability and clinical utility. Full article

Kinesthetic motor imagery (KMI), the mental rehearsal of a motor task without its actual performance, constitutes one of the most common techniques used for brain–computer interface (BCI) control for movement-related tasks. The effect of neural injury on motor cortical activity during execution and imagery remains under investigation in terms of activations, processing of motor onset, and BCI control. The current work aims to conduct a post hoc investigation of the event-related potential (ERP)-based processing of KMI during BCI control of anthropomorphic robotic arms by spinal cord injury (SCI) patients and healthy control participants in a completed clinical trial. For this purpose, we analyzed 14-channel electroencephalography (EEG) data from 10 patients with cervical SCI and 8 healthy individuals, recorded through Emotiv EPOC BCI, as the participants attempted to move anthropomorphic robotic arms using KMI. EEG data were pre-processed by band-pass filtering (8–30 Hz) and independent component analysis (ICA). ERPs were calculated at the sensor space, and analysis of variance (ANOVA) was used to determine potential differences between groups. Our results showed no statistically significant differences between SCI patients and healthy control groups regarding mean amplitude and latency (p < 0.05) across the recorded channels at various time points during stimulus presentation. Notably, no significant differences were observed in ERP components, except for the P200 component at the T8 channel. These findings suggest that brain circuits associated with motor planning and sensorimotor processes are not disrupted due to anatomical damage following SCI. The temporal dynamics of motor-related areas—particularly in channels like F3, FC5, and F7—indicate that essential motor imagery (MI) circuits remain functional. Limitations include the relatively small sample size that may hamper the generalization of our findings, the sensor-space analysis that restricts anatomical specificity and neurophysiological interpretations, and the use of a low-density EEG headset, lacking coverage over key motor regions. Non-invasive EEG-based BCI systems for motor rehabilitation in SCI patients could effectively leverage intact neural circuits to promote neuroplasticity and facilitate motor recovery. Future work should include validation against larger, longitudinal, high-density, source-space EEG datasets. Full article

In the study of biomechanical models, balance represents a complex problem due to the issue of indeterminate forces while standing. In order to solve this problem, it is essential to measure the ground reaction forces (GRFs) applied to each foot independently. The present work proposes a methodology for determining the independent GRF applied to each foot while standing when only one forceplate is available. For this purpose, an analytical method is proposed to determine the distribution of vertical GRFs and the position of the independent center of pressure (CoP) in each foot. Concurrently, several neural network (NN) models are trained to improve the results obtained. This hypothesis is experimentally validated by a self-developed device that allows one to simultaneously obtain the vertical GRF and CoP location of each foot at the same time that the GRF and the global CoP location are obtained from a single forceplate. The results obtained achieve a CoP position error of less than 8% and a vertical force error of 2%. The analytical hypothesis is demonstrated to offer a satisfactory level of precision, while the NN is shown to result in considerable improvement in some cases. Full article

Automated quality control plays a critical role in modern industries, particularly in environments that handle large volumes of packaged products requiring fast, accurate, and consistent inspections. This work presents an anomaly detection system for candle jars commonly used in industrial and commercial applications, where obtaining labeled defective samples is challenging. Two anomaly detection strategies are explored: (1) a baseline model using convolutional neural networks (CNNs) as an end-to-end classifier and (2) a hybrid approach where features extracted by CNNs are fed into One-Class classification (OCC) algorithms, including One-Class SVM (OCSVM), One-Class Isolation Forest (OCIF), One-Class Local Outlier Factor (OCLOF), One-Class Elliptic Envelope (OCEE), One-Class Autoencoder (OCAutoencoder), and Support Vector Data Description (SVDD). Both strategies are trained primarily on non-defective samples, with only a limited number of anomalous examples used for evaluation. Experimental results show that both the pure CNN model and the hybrid methods achieve excellent classification performance. The end-to-end CNN reached 100% accuracy, precision, recall, F1-score, and AUC. The best-performing hybrid model CNN-based feature extraction followed by OCIF also achieved 100% across all evaluation metrics, confirming the effectiveness and robustness of the proposed approach. Other OCC algorithms consistently delivered strong results, with all metrics above 95%, indicating solid generalization from predominantly normal data. This approach demonstrates strong potential for quality inspection tasks in scenarios with scarce defective data. Its ability to generalize effectively from mostly normal samples makes it a practical and valuable solution for real-world industrial inspection systems. Future work will focus on optimizing real-time inference and exploring advanced feature extraction techniques to further enhance detection performance. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

Efficient planning of train crew rosters is a key factor in ensuring operational reliability and promoting long-term sustainability in railway transport, both economically and socially. This article presents a systematic approach to developing a crew rostering model in passenger rail transport, with a focus on the operational setting of the train crew depot in Česká Třebová, a city in the Czech Republic. The seven-step methodology includes identifying available train shifts, defining scheduling constraints, creating roster variants, and calculating personnel and time requirements for each option. The proposed roster reduced staffing needs by two employees, increased the average shift duration to 9 h and 42 min, and decreased non-productive time by 384 h annually. These improvements enhance sustainability by optimizing human resource use, lowering unnecessary energy consumption, and improving employees’ work–life balance. The model also provides a quantitative assessment of operational feasibility and economic efficiency. Compared to existing rosters, the proposed model offers clear advantages and remains applicable even in settings with limited technological support. The findings show that a well-designed rostering system can contribute not only to cost savings and personnel stabilization, but also to broader objectives in sustainable public transport, supporting resilient and resource-efficient rail operations. Full article

The aim of this work is to analyse the effectiveness of the medical use of the Cavitron Ultrasonic Surgical Aspirator (CUSA) in microsurgical treatment of Intramedullary Spinal Cord Tumors (IMSCTs), with a focus on the thermo-mechanical effects on neighbouring tissues to assess any potential damage. Indeed, CUSA emerges as an innovative solution, minimally invasive tumor excision technique, enabling controlled and focused operations. This study employs a Finite Element Analysis (FEA) to simulate the vibratory and thermal interactions occurring during CUSA application. A computational model of a vertebral column segment affected by an IMSCT was developed and analysed using ANSYS 2024 software. The simulations examined strain distribution, heat generation, and temperature propagation within the biological tissues. The FEA results demonstrate that the vibratory-induced strain remains highly localised to the application site, and thermal effects, though measurable, do not exceed the critical safety threshold of 46 °C established in the literature. These findings suggest that CUSA can be safely used within defined operational parameters, provided that energy settings and exposure times are carefully managed to mitigate excessive thermal accumulation. These conclusions contribute to the understanding of the thermo-mechanical interactions in ultrasonic tumour resection and aim to assist medical professionals in optimising surgical protocols. Full article

This review summarises the possible applications and basic methodologies for the synthesis of six-membered polyazo heterocycles, namely, diazines, triazines, and tetrazines. The time period covered by the analysed works ranges from the beginning of the 20th century to the present day. This period was chosen because it was during this time that synthetic chemistry, as defined by physicochemical research methods, became capable of solving such complex problems as efficiently as possible. The first part of the review describes the applications of polyazo heterocyclic compounds, whose frameworks are found in the composition of drugs, dyes, and functional molecules for materials chemistry, as well as in a wide variety of natural compounds and their synthetic analogues. The review also systematises the methods for assembling six-membered aromatic polyazo heterocycles, including intramolecular and sequential cyclisation, which determine the possible structural and functional diversity based on the presence and arrangement of nitrogen atoms and the position of the corresponding substituents. Full article

Flexible work arrangements have the potential to enhance work–life balance and contribute to more sustainable work environments. However, they may also increase fatigue and lead to greater work–life conflict (WLC). This study offers a novel contribution by examining the relationship between flexible work arrangements—focusing in particular on the cognitive demands of flexible work (CDFW), which encompass the task structuring, scheduling of working times, planning of working place, and coordination with others—and WLC. Specifically, the study investigates the mediating role of workload in this relationship. Furthermore, it also explores whether perceived organizational support (POS) moderates the indirect relationships between CDFW and WLC, within the framework of the Job Demands-Resources (JD-R) model. Data were collected from a sample of 419 employees in the Italian public sector. The study also controls for potential confounding variables, such as age, gender, duration of employment in public administration, and weekly working hours, to account for their influence on work–life balance and workload. The results highlight a significant positive relationship between planning of the working place and WLC. Additionally, workload plays a mediating role between CDFW subdimensions and WLC. However, POS does not moderate the mediated relationship between CDFW and WLC. Full article

Electric propulsion requires engines and transmission systems that run at higher speeds compared to combustion engines. For improving sustainability and environmental protection, biodegradable oils are suggested for the lubrication of high-speed gears that require particularly quick wetting of the steel surfaces. Newly developed promising candidates include short-chained polyalphaolefins. In the present work, a study on the applicability of such oil is presented and discussed with respect to different aging levels based on biodegradable properties. It focuses on the wettability of metallic surfaces investigated through time-resolved contact angle measurements. Carbon steels with different carbon contents and microstructures are selected as the most commonly used materials for gears. Effects of steel composition, surface roughness and oil oxidation are studied. The results show that in most cases, the application of biodegradable polyalphaolefins is not critical; however, a combination of steels with inhomogeneous microstructure, high surface roughness and aged oil can be critical because of limited wetting. Full article

The incorporation of Cannabis sativa L. seeds into barley wort was investigated to enhance the functional profile of beer. Hemp seeds (cv. Henola) were malted via controlled steeping, germination, and kilning, then added to barley malt at 10% and 30% (w/w) in both malted and unmalted forms. Standard congress mashing produced worts whose physicochemical parameters (pH, extract, colour, turbidity, filtration and saccharification times) were assessed, alongside profiles of fermentable sugars, polyphenols, B-group vitamins, and cannabinoids. Addition of hemp seeds reduced extract yield without impairing saccharification or filtration and slightly elevated mash pH and turbidity. Maltose and glucose levels declined significantly at higher hemp dosages, whereas sucrose remained stable. Wort enriched with 30% unmalted seeds exhibited the highest levels of trans-ferulic (20.61 µg/g), gallic (5.66 µg/g), trans-p-coumaric (3.68 µg/g), quercetin (6.07 µg/g), and trans-cinnamic (4.07 µg/g) acids. Malted hemp addition enhanced thiamine (up to 0.302 mg/mL) and riboflavin (up to 178.8 µg/mL) concentrations. Cannabinoids (THCA-A, THCV, CBDV, CBG, CBN) were successfully extracted at µg/mL levels, with the total cannabinoid content peaking at 14.59 µg/mL in the 30% malted treatment. These findings demonstrate that hemp seeds, particularly in malted form, can enrich barley wort with bioactive polyphenols, vitamins, and non-psychoactive cannabinoids under standard mashing conditions, without compromising key brewing performance metrics. Further work on fermentation, sensory evaluation, stability, and bioavailability is warranted to realise hemp-enriched functional beers. Full article

Improving the corrosion resistance of magnesium (Mg) alloys is a long-term challenge, especially when cost-effectiveness is taken into account. In this work, Mg-1Bi and Mg-1Sn extruded alloys are prepared, and the effects of cost-effective Bi and Sn on the corrosion behavior of Mg alloys are comparatively studied. The corrosion resistance of the Mg-1Sn alloy (P_H: 2.83 ± 0.19 mm y⁻¹) is better than that of the Mg-1Bi alloy (P_H: 13.75 ± 1.12 mm y⁻¹), being about five times greater. In addition to the relatively low dislocation density in Mg-1Sn alloy, the difference in corrosion resistance is mainly attributed to two aspects of influence brought about by the addition of Sn and Bi. The Mg₂Sn phase introduced by the addition of Sn has a potential difference (PD) of ~30 mV, which is significantly lower than that (~90 mV) of the Mg₃Bi₂ phase introduced by adding Bi, thereby weakening the micro-couple corrosion tendency of the Mg-1Sn alloy. The addition of Bi has little effect on the corrosion film, while the addition of Sn makes the corrosion film on the Mg-1Sn alloy contain SnO₂, improving the compactness of the corrosion film and thereby enhancing the corrosion protection effect. Full article

Material Extrusion (MEX) process is increasingly used to fabricate components for structural applications, driven by the availability of advanced materials and greater industrial adoption. In these contexts, understanding the mechanical performance of printed parts is crucial. However, conventional methods for assessing anisotropic elastic behavior often rely on expensive equipment and time-consuming procedures. The aim of this study is to evaluate the applicability of the impulse excitation of vibration (IEV) in characterizing the dynamic mechanical properties of a 3D-printed composite material. Tensile tests were also performed to compare quasi-static properties with the dynamic ones obtained through IEV. The tested material, Nylon 12CF, contains 35% short carbon fibers by weight and is commercially available from Stratasys. It is used in the fused deposition modeling (FDM) process, a Material Extrusion technology, and exhibits anisotropic mechanical properties. This is further reinforced by the filament deposition process, which affects the mechanical response of printed parts. Young’s modulus obtained in the direction perpendicular to the deposition plane (E₃₃), obtained via IEV, was 14.77% higher than the value in the technical datasheet. Comparing methods, the Young’s modulus obtained in the deposition plane, in an inclined direction of 45 degrees in relation to the deposition direction (E₄₅), showed a 22.95% difference between IEV and tensile tests, while Poisson’s ratio in the deposition plane (v₁₂) differed by 6.78%. This data is critical for designing parts subject to demanding service conditions, and the results obtained (orthotropic elastic properties) can be used in finite element simulation software. Ultimately, this work reinforces the potential of the IEV method as an accessible and consistent alternative for characterizing the anisotropic properties of components produced through additive manufacturing (AM). Full article