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Plasma-based degradation of preservatives in wastewater: a promising approach for enhanced removal
(2025)
Wastewater treatment plants often struggle with the removal of trace substances, necessitating the implementation of additional treatment stages. This study explores the use of a plasma-based system for the removal of preservatives, using methylparaben as an exemplar. The pilot experiment demonstrated a reduction of over two-thirds in methylparaben concentration. However, the presence of undesirable by-products was observed in small amounts. The study highlights the potential for improving the reproducibility of plasma generation and enhancing energy efficiency. Further development is recommended to optimize the system's performance, control plasma intensity and automate the process for future industrial implementation. This plasma-based approach shows promise for the removal of preservatives and other trace substances in wastewater treatment, supplementing existing methods such as ozonation and activated carbon adsorption.
The integration of artificial intelligence into video-based human behavior analysis enables contactless and continuous monitoring of both motor dynamics and facial reactions. This paper proposes a dual-stream multimodal framework for synchronized modeling of facial expression dynamics and skeletal motion during physical movement from monocular RGB video. The framework consists of two coordinated streams: the motor stream, based on 2D skeletal keypoints, and the facial stream, which extracts features associated with discomfort and affective responses. Person and face detection are performed using YOLO11, while specialized deep learning models handle pose estimation and facial expression recognition. Temporal dependencies and cross-modal relationships are modeled via a bidirectional LSTM, enabling unified temporal modeling of skeletal and facial dynamics. This novel approach allows investigation of how physical movement patterns relate to facial reactions during dynamic activities. By integrating heterogeneous facial and skeletal features in a synchronized temporal model, the framework enables consistent cross-modal analysis of dynamic human behavior. The framework was trained and validated using FER2013 and AffectNet for facial expression recognition, and UI-PRMD and FineRehab for skeletal motion modeling. It achieves 91.2% accuracy in facial expression classification, 94.8% mean Intersection over Union for human detection, and an F1 score of 0.89 for multimodal state assessment. Operating in real-time at 18–28 FPS on standard GPU hardware without requiring wearable sensors, the framework supports applications in behavioral monitoring and safety analysis.
This study investigated how fruit matrices and the probiotic strain Lacticaseibacillus paracasei subsp. paracasei F19 (F19) interact with Saccharomyces cerevisiae US-05 to shape volatile formation, microbial viability, and sensory outcomes during the fermentation of Catharina sour beer. The central objective was to fill the current knowledge gap regarding dynamic flavor development in mixed fermentations, particularly in systems where probiotic LAB may contribute both functional and aromatic benefits. Microbial viability was monitored using propidium monoazide quantitative PCR (PMA-qPCR); volatile compounds were profiled by headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME/GC–MS); and sensory acceptance was evaluated with 62 untrained panelists using a 9-point hedonic scale. F19 displayed strong viability throughout fermentation (5–7 log CFU/mL), with significant increases after 58 days in the control and passion fruit formulations (p < 0.05), achieving the recommended daily intake level in 350-mL servings. HS-SPME/GC–MS identified 143 volatile compounds, including key aroma contributors, such as 3-methylbutyl acetate, ethyl hexanoate, and ethyl octanoate, as well as 23 core volatiles persistent across all stages. Sensory evaluation revealed a clear preference for the passion fruit beer (6.94 ± 2.01), linked to 18 unique volatiles including 6-methylhept-5-en-2-one and benzaldehyde, while the peach beer—despite containing honey-like 2-phenylacetaldehyde—showed lower acceptance (5.72 ± 1.91). Overall, this first integrated volatilomic, microbial, and sensory analysis demonstrates that L. paracasei F19 enhances aroma diversity and remains viable in fruit-based sour beers, with passion fruit providing the most promising sensory and functional profile for innovative probiotic craft beer development.
This paper describes a study on the acoustic features of bolted structures at several tightening torques within the SIAMIS (Systementwicklung für Intelligente und Automatisierte Mobile Inspektion von Schienenfahrzeugen) project. Acoustic data is obtained through controlled impact testing of bolted specimens, capturing both airborne and structural responses using microphones and piezoelectric accelermometers. The derived transfer functions and frequency responses reveal systematic trends in sound pressure levels, frequency shifts, and damping with changing bolt torque. These trends were analyzed using a hybrid AI-based framework that combines physical relations of contact stiffness with empirical and numerical data-driven feature extraction to identify acoustic indicators sensitive to bolt preload. This hybrid approach enables the estimation of joint conditions directly from measured sound characteristics and can be extended to larger demonstrators and multi-sensor investigations. The presented findings represent an initial step toward developing an interpretable and scalable acoustic monitoring methodology for bolted structures within SIAMIS.
Xolography is a volumetric 3D printing technology utilizing two different wavelengths of light to produce objects with micrometer resolution and excellent surface quality within minutes. Dual-color photoinitiators (DCPIs) are crucial for spatial control of polymerization. However, their performance has mainly been assessed indirectly by evaluating printed objects. In this study, we present a methodology using in situ Raman spectroscopy to characterize the dual-color curing response of a representative spiropyran-based DCPI in a methacrylate-based resin composition. A dual-wavelength irradiation setup has been developed to monitor the degree of curing (DoC) as a function of the applied light doses, providing quantitative, real-time insights into bond conversion and polymerization kinetics, as well as enabling the Xolography process window to be derived. The DCPI exhibited a contrast of 89%, showing improved performance compared to first generation systems, while its efficiency was benchmarked against a benzophenone reference. Spatially resolved Raman mapping of volumetric test prints revealed DoC gradients ranging from 40% to 80% directly after printing. We demonstrate that subsequent posttreatments can eliminate these nonuniformities, increasing the DoC to > 90%. This approach provides a framework for evaluating novel dual-color photoinitiator systems, facilitating the rational design of resins, illumination protocols and optimized configurations for volumetric 3D printing.
Indium gallium nitride (InGaN) nanowire structures were investigated as photoelectrochemical transducers for bioanalytical applications. Chopped-light voltammetry in HEPES buffer (pH 7.0) revealed a light intensity-dependent anodic photocurrent that varies with applied potential. In the presence of hypoxanthine unmodified InGaN showed no photocurrent changes when electrode potentials around 0 mV vs Ag/AgCl or below have been applied. The three-dimensional surface of the nanowires was then used to adsorb the enzyme xanthine dehydrogenase (XDH). After this modification, the InGaN electrode exhibited a distinct increase in anodic photocurrent in the presence of hypoxanthine. The photocurrent showed a clear concentration-dependent behavior. This is indicative for a direct electron transfer from XDH to the semiconductor material. These findings highlight the suitability of InGaN nanowires for coupling with redox enzymes and their potential for developing light-driven biosensing platforms.
Background
Leishmaniasis is a vector-borne parasitic disease caused by Leishmania protozoa. The disease manifests in several clinical presentations including cutaneous, mucocutaneous, and visceral leishmaniasis. The diagnosis of leishmaniasis is complex and often requires a combination of clinical assessment, microscopy, serological tests, and molecular techniques especially in immunocompromised cases. However, traditional diagnostic methods have limitations in terms of accuracy, sensitivity, and the expertise required, leading to an urgent need for advanced, automated diagnostic tools. The aim of this research is to develop a deep learning-based decision-support system for the microscopic examination of tissue samples to support non-experts with the live diagnosis of the disease.
Methods
Tissue samples from lesions were collected from patients diagnosed with cutaneous leishmaniasis in Libya and Palestine for the purpose of preparing microscopic slides. The samples were then visualized using a high-performance laboratory microscope and a mobile, low-cost device. The captured images were subsequently used to train the object detection framework YOLOv8 with the aim of identifying Leishmania parasites. A graphical user interface was developed for the application of the deep learning model, which enables real-time detection of the parasites using a microscope camera, as well as recognition from previously generated images and videos.
Results
The deep learning YOLOv8 framework was successfully trained using data generated by the advanced microscope and employed for the detection of Leishmania parasites. Subsequent finetuning with a combined set containing the aforementioned data and microscopic images generated with the low-cost device resulted in a considerable improvement in accuracy. The efficacy of the model was demonstrated through its successful operation on previously unseen data. Object detection yielded a mean average precision of 0.78 for the combined datasets. The evaluation process for determining the presence of parasites in an image resulted in 91% accuracy, 91% sensitivity, 90% specificity and 94% precision on the test data.
Conclusions
Deep learning-based YOLOv8 achieved accurate Leishmania detection in tissue samples, enhancing decision-support for non-experts via real-time graphical user interface support. This innovation can simplify diagnostics by addressing traditional method limitations, enabling early, accessible leishmaniasis detection in resource-limited settings, and potentially inspiring similar applications in other parasitic diseases.
Reliable identification of deceased individuals may be difficult when conventional biometric methods such as facial recognition, fingerprint analysis, or DNA profiling cannot be applied. In such cases, medical imaging records acquired during a person’s lifetime may serve as an alternative source of identifying information. Certain anatomical structures visible in computed tomography (CT), including the sphenoid sinus, exhibit considerable inter-individual variability while remaining relatively stable within the same individual. This study investigates the feasibility of using sphenoid sinus morphology as an anatomical biometric for automated identification from head CT scans. Identification is formulated as a ranking problem in which a query CT examination is compared with a reference database using geometric descriptors derived from segmentation masks, reducing dependence on CT intensity values. The dataset consisted of CT scans from 816 individuals acquired in two patient positioning modes: Head First Supine (HFS) and Head First Prone (HFP). Several deep learning architectures, including YOLOv8 variants, YOLO11L-seg, UNet++, DeepLabV3+, HRNet, and SegFormer-B2, were evaluated for sphenoid sinus segmentation. Based on F1-score performance and cross-mode stability, YOLO11L-seg was selected and further trained to construct a database of binary masks representing individual sphenoid sinus anatomy. Identification was performed using pairwise mask comparison based on the Intersection over Union (IoU) metric. To reduce the influence of segmentation artifacts and slice-level variability, the final similarity score for each candidate was computed as the average of the four highest IoU values across slice comparisons. Individuals were ranked according to similarity, and identification was considered successful if the correct subject appeared among the top five candidates and exceeded a predefined similarity threshold. The proposed approach achieved Top-5 identification accuracies of 97.27% for HFP and 87.67% for HFS acquisitions. These results demonstrate the feasibility of using sphenoid sinus geometry as a stable anatomical biometric for automated identification. The key contribution of this study is the introduction of a ranking-based identification framework that utilizes anatomical biometrics derived from CT data for reliable patient matching.
Additive manufacturing enables the development of low-cost, self-built robotic systems; however, their performance is typically not characterized by validated metrics. The paper presents a photogrammetric concept intended for system-independent application to characterize planar positioning accuracy and repeatability without access to internal controller data. The method is based on a Raspberry Pi 4 camera system, image processing in Python 3.12.0 and OpenCV 4.12.0, and a universal additively manufactured robot tool attachment. Two position estimation strategies are investigated: a marker-based approach using ArUco markers and a markerless blob-analysis method based on a ruby sphere. Camera calibration is evaluated using different patterns, with a compact CharUco board exhibiting the lowest RMS reprojection error (~1 px). Experimental validation follows selected elements of ISO 9283:1998 and comprises 30 repetitions at five target poses for linear and axial motion strategies. The results show lower positional deviations for marker-based methods compared to the markerless approach, with a two-marker configuration yielding the lowest mean deviation under the investigated conditions. Sub-millimeter positioning accuracy and repeatability are achieved, and linear motion exhibits lower repeatability deviations than axial motion. The proposed approach provides a cost-effective and flexible solution for external robot characterization, particularly suited for self-built and resource-constrained systems.
Electrospinning is a versatile technique for producing polymer nanofibers with high ratios of surface area to volume and tunable porosity. Conventional approach to the optimization of processing parameters such as voltage and flow rate frequently encounters limitations in reproducibility and scalability. This review proposes a comprehensive framework that integrates macromolecular design principles with established electrohydrodynamic theories. We analyze how intrinsic molecular traits, specifically chain entanglement density, molecular weight distribution (MWD), topological architecture, and polymer–solvent thermodynamic interactions, define the boundaries of jet stability and solidification. Key findings highlight that while molecular weight establishes a baseline for spinnability, the MWD dictates the dynamic response under extreme deformation. Notably, high-molecular-weight fractions act as elastic load-bearers that suppress capillary breakup. Furthermore, we discuss here how molecular architecture and solvent-mediated segmental mobility determine whether molecular orientation is kinetically trapped or relaxed during the nanosecond timescales of jet flight. By establishing a hierarchical design logic prioritizing molecular and formulation variables over processing parameters, this framework provides a robust strategy to overcome challenges in scalability and reproducibility, positioning electrospinning as a sensitive probe for macromolecular dynamics under extreme elongation.

