Search Results (56)

Wound infection significantly hinders the healing process. Clinical signs and symptoms (CSS) of infection are used to assess the presence of infection and guide whether to intervene. However, CSS may not be dependable, lacking sensitivity and specificity, and may not accurately reflect bacterial load. The interpretation of CSS can be subjective and can vary between clinicians since they depend on patient characteristics, type of wound, and stage of infection. In addition, conditions such as peripheral vascular disease or diabetes can mask the signs and symptoms of infection. Inaccurate or late diagnosis of infected wounds can be costly to the patient and to healthcare systems. Fluorescence imaging (FLI) provides a safe, objective, highly sensitive approach to detect clinically significant bacterial levels in wounds. This information allows individualized treatment plans and a way to monitor bacterial burden and wound healing longitudinally. This publication reviews the evidence for point-of-care FLI as a means of improving wound identification with a high bacterial burden and the clinical and healthcare economic benefits of earlier and more accurate detection of bacteria. Full article

Concrete pavements frequently develop subsurface voids between surface and base layers during long-term service due to cyclic loading, environmental effects, and subgrade instability, which compromise structural integrity and traffic safety. Ground-penetrating radar (GPR) has been widely used as a non-destructive method for detecting such voids. However, the presence of coarse aggregates with strong electromagnetic scattering properties often introduces pseudo-reflection signals in radar images, hindering accurate void identification. To address this challenge, this study develops a high-fidelity three-phase concrete model incorporating aggregates, mortar, and the interfacial transition zone (ITZ). The Finite-Difference Time-Domain (FDTD) method is used to simulate electromagnetic wave propagation in both voided and intact structures. Simulation results reveal that aggregate-induced scattering can blur or distort reflection interfaces, generating pseudo-hyperbolic anomalies even in the absence of voids. In cases of thin-layer voids, real echo signals may be masked by aggregate scattering, leading to missed detections. GPR systems can be broadly classified into impulse, continuous-wave, and multi-frequency types. To validate the simulations, field tests using multi-frequency 2D/3D GPR systems and borehole verification were conducted. The results confirm the consistency between simulated and actual radar anomalies and validate the proposed model. This work provides theoretical insight and modeling strategies to enhance the interpretation accuracy of GPR data for subsurface void detection in concrete pavements. Full article

Clinical manifestations of salmonellosis in humans typically include acute gastroenteritis, abdominal pain, diarrhea, nausea, and fever. Diarrhea and anorexia may persist for several days. In some cases, the organisms may invade the intestinal mucosa and cause septicemia, even in the absence of significant gastrointestinal symptoms. Most clinical signs are attributed to hematogenous dissemination of the pathogen. As with other microbial infections, disease severity is influenced by the serotype of the organism, bacterial load, and host susceptibility. Serotyping analysis of Salmonella spp. using the White–Kauffmann–Le Minor scheme remains the gold standard for strain typing. However, this method is expensive, time-consuming, and requires significant expertise and visual interpretation by trained personnel, which is why it is typically restricted to regional or national reference laboratories. In this study, we evaluated a spectroscopic technique coupled with chemometrics and multivariate machine learning algorithms for its ability to discriminate the main Salmonella spp. serogroups in a clinical routine setting. We analyzed 95 isolates of Salmonella that were randomly selected, including four strains of S. Typhi. The I-dOne Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) system (Alifax S.r.l., Polverara, Italy) also shows promising potential for distinguishing Salmonella Typhi within the D serogroup. The I-dOne system enables simultaneous identification of both species and subspecies using the same workflow and instrumentation, thus streamlining the diagnostic process. Full article

This study presents a data-driven analysis of navigator stress and workload levels in simulated ship encounters within restricted waters, leveraging real-world automatic identification system (AIS) data from Makassar Port, Indonesia. Six close-quarter scenarios were recreated to reflect critical encounter geometries, and 24 Indonesian seafarers were evaluated using heart rate variability (HRV), perceived stress scale (PSS), and task load index (NASA-TLX) workload assessments. The results indicate that crossing angles, particularly 135° port and starboard encounters, significantly influence physiological stress levels, with age being a moderating factor. Although no consistent relationship was found between workload and HRV metrics, the findings underscore key human factors that may impair navigational performance under cognitively demanding conditions. By integrating AIS-derived traffic data with simulation-based human performance monitoring, this study supports the development of intelligent maritime training frameworks and adaptive decision support systems. The research contributes to broader efforts toward enhancing navigational safety and situational awareness amid increasing automation and traffic densities at sea. Full article

This study validates a Spanish-language instrument designed to assess self-efficacy, digital competence, and anxiety in data-driven decision-making (DDDM) among pre-service teachers. Based on the 3D-MEA and the Beliefs about Basic ICT Competencies scale, the instrument was culturally adapted for Chile and Spain. A sample of 512 participants underwent exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Given the ordinal nature of the data and the assumption of non-normality, appropriate estimation methods were utilized. Results supported a well-defined four-factor structure: Interpretation and Application, Technology, Identification, and Anxiety. Factor loadings ranged from 0.678 to 0.869, and internal consistency was strong (α = 0.802–0.888). The CFA confirmed good model fit (χ² (129) = 189.25, p < 0.001; CFI = 0.985; TLI = 0.981; RMSEA = 0.041; SRMR = 0.061). Measurement invariance was established across gender and nationality, reinforcing the validity of cross-group comparisons. The study is framed within an educational context aligned with socioformative principles and sustainable education goals, which support reflective and ethical data use. This validated tool addresses the lack of culturally adapted and psychometrically validated instruments for assessing DDDM competencies in Spanish-speaking contexts, offering a culturally and linguistically relevant instrument with strong internal consistency and a well-supported factor structure. It supports the design of formative strategies in teacher education, enabling the identification of training needs and promoting evidence-based pedagogical decision-making in diverse Hispanic contexts. Future studies should test factorial invariance across additional contexts and explore longitudinal applications. Full article

The contact condition of circuit breaker contacts directly affects their operational reliability, while circuit resistance, as a key performance indicator, reflects physical changes such as wear, oxidation, and ablation. Traditional offline measurement methods fail to accurately represent the real-time operating state of equipment due to large errors and high randomness, limiting their effectiveness for state awareness and precision maintenance. To address this, a non-contact multi-sensor fusion method for the online monitoring of circuit breaker circuit resistance is proposed, aimed at enhancing operational state awareness in power systems. The method integrates Hall effect current sensors, infrared temperature sensors, and electric field sensors to extract multiple sensing signals, combined with high-precision signal processing algorithms to enable the real-time identification and evaluation of circuit resistance changes. Experimental validation under various scenarios—including normal load, overload impact, and high-temperature and high-humidity environments—demonstrates excellent system performance, with a fast response time (≤200 ms), low measurement error (<1.5%), and strong anti-interference capability (SNR > 60 dB). In field applications, the system successfully identifies circuit resistance increases caused by contact oxidation and issues early warnings, thereby preventing unplanned outages and demonstrating a strong potential for application in the fault prediction and intelligent maintenance of power grids. Full article

The assessment of air pollution is an important and relevant issue that requires continuous monitoring and control, especially in urban spaces. However, using instrumental air quality measurement techniques and deploying meters throughout the city is extremely expensive, so a biological alternative can be used—a bioindicator, i.e., a species whose vital functions or morphological structure can reveal the qualitative state of the environment. In this work, the lichen Hypogymnia physodes L. was used to analyze air pollution in areas of the provincial city of Opole, southern Poland. Microscope and chemotaxonomy methods were used in the laboratory to confirm field identification of lichens (atlases and keys). The selected elements, Mn, Fe, Ni, Cu, Zn, Cd, and Pb, were determined using atomic absorption spectrometry, and direct mercury analyzer was used to analyzed Hg concentration. Factor analysis (FA) was performed to associate elements with possible sources of air pollution. The highest concentrations of analytes were found at measurement points close to railway roads (Fe = 5131 mg/kg) and streets with heavy traffic (Pb = 101 mg/kg). Statistically significant differences (p < 0.001) were found between the concentrations of individual elements, which have positive correlation coefficients higher than 0.65. Based on the research carried out, different anthropogenic and traffic-related activities can be considered as one of the main sources of air pollution in Opole City based on the results of FA. Using an additional lichen scale, it can be concluded that the areas surveyed in the town of Opole can be classified as zone IV—characterized by an increase in the number of leaf lichens (additionally co-occurring lichens of the Polycauliona candelaria species), i.e., an area with an average level of air pollution (based also on contamination factor [CF] and pollution load index [PLI]). Accumulation concentrations of heavy metals in lichen were metal-specific and varied spatially, thus reflecting local differences in heavy metal deposition. The research presented here proves that low-cost passive biomonitoring can effectively support classical methods of assessing air pollution in urban spaces. Full article

Acute central nervous system (CNS) infections, such as meningitis and encephalitis, represent medical emergencies that require rapid identification of the causative pathogen to guide appropriate therapeutic interventions. The QIAstat-Dx^® Meningitis/Encephalitis (QIA/ME) is a molecular syndromic panel that enables the simultaneous detection of multiple pathogens and provides the visualization of cycle threshold (Ct) values, offering rapid results for prompt clinical management. This study retrospectively tested, with the QIA/ME panel, 170 cerebrospinal fluid (CSF) samples from patients with CNS infections, confirmed through routine diagnostic workflows. The results were compared with those obtained from bacterial culture and singleplex PCR for viral detection. The QIA/ME demonstrated 100% concordance with reference methods for bacterial and yeast infections. For viral infections, the overall detection rate was 85.9%. Specifically, when singleplex PCR results exceeded 250 copies/mL for DNA viruses and 500 copies/mL for the RNA virus, the concordance rate with the QIA/ME was 96.8%. In contrast, when PCR values were below these thresholds, the concordance rate dropped to 43.8%. A strong overall correlation was observed between the viral load measured by singleplex PCR and Ct values from the QIA/ME (ρ = −0.83, p < 0.001). Only for enterovirus a weak correlation was found (ρ = −0.40, p = 0.056). The QIA/ME panel is an effective diagnostic tool for viral CNS infections, allowing for the visualization of Ct values that reflect pathogen load in samples and which could be useful in guiding clinical decision-making and patient management. Full article

There is very limited research in the literature investigating the way acoustic emission signals change when polymer materials are undergoing different fracture modes. This study investigates the capability of acoustic emission to recognize the fracture mode through acoustic emission parameter analysis, and can be considered the first-ever study which examines the impact of different loading conditions, i.e., fracture mode I, mode II, and mode III, on the acoustic emission parameters in polymer materials. To accomplish this, prism-like pre-cracked polymer specimens were tested under the three different fracture modes. Acoustic emission parameters appeared sensitive to the different loading conditions of the pre-cracked specimens, indicating that acoustic emission can be used to distinguish the three fracture modes in polymer materials. Both frequency and time parameters reflect changes in the stress states at the crack tip. The duration and rise time of the waveforms were found to be the most sensitive acoustic emission parameters for identifying the fracture mode, while the average frequency variation can be employed to differentiate between in-plane and out-of-plane fracture modes. In order to interpret the experimental results in relation to wave mechanics, numerical wave propagation simulations for longitudinal and shear excitations were performed to simulate tensile and shear fracture modes and the corresponding emitted waves. An interesting correlation between the experimental and numerical results exists, showcasing acoustic emission’s potential for fracture identification. Full article

With the deepening of national efforts toward green energy transformation, the power system is evolving into one characterized by “double high”—a high proportion of new energy integration and a high level of power electronic systems. This results in a more complex system topology, necessitating improvements in various prevention and control measures. Traditional model-based methods for locating power oscillation disturbance sources in power systems are no longer sufficient to meet the operational demands of modern power systems. With the rapid development of wide-area measurement systems (WAMS), there is growing interest in disturbance source localization using system response data. System dynamics provide a wealth of easily extractable data that can accurately reflect the power system’s behavior under normal conditions. This paper proposes a numerical method for locating disturbance sources, combining energy functions with normal distribution identification, based on power oscillation mechanisms and system response data. The method identifies potential disturbance sources, including small random load fluctuations and large forced power oscillations. The innovation lies in the introduction of a 3 Sigma value criterion to pinpoint the disturbance source location, addressing the limitations of traditional energy function methods that require manual intervention. By quantifying the localization of power oscillation disturbance sources, this method significantly improves both efficiency and accuracy. Full article

This paper introduces a novel approach to assessing structural safety, specifically aimed at evaluating the safety of existing structures. Firstly, a point cloud model of the existing commercial complex was captured utilizing three-dimensional (3D) laser scanning technology. Subsequently, an intelligent method for identifying holes within the point cloud model was proposed, built upon a YOLO v5-based framework, to ascertain the dimensions and locations of holes within the commercial complex. Secondly, Poisson surface reconstruction, coupled with partially self-developed algorithms, was employed to reconstruct the surface of the structure, facilitating the three-dimensional geometric reconstruction of the commercial complex. Lastly, a finite element model of the framed structure with holes was established using the reconstructed 3D model, and a safety analysis was conducted. The research findings reveal that the YOLO v5-based intelligent hole identification method significantly enhances the level of intelligence in point cloud data processing, reducing manual intervention time and boosting operational efficiency. Furthermore, through Poisson surface reconstruction and the self-developed algorithms, we have successfully achieved automated surface reconstruction, where the resulting geometric model accurately reflects the dimensional information of the commercial complex. Additionally, the maximum uniformly distributed surface load that the floor slabs within the framed structure with holes can withstand should not exceed 17.7 kN/m², and its vertical deformation resistance stiffness is approximately 71.6% of that of a frame without holes. Full article

Carbon Fiber Reinforced Polymers (CFRP) have become increasingly significant in real-world applications due to their superior strength-to-weight ratio, corrosion resistance, and high stiffness. These properties make CFRP an ideal material for reinforcing concrete structures, particularly in scenarios where weight reduction is crucial, such as in bridges and high-rise buildings. The transformative potential of CFRP lies in its ability to enhance the durability and load-bearing capacity of concrete structures while minimizing maintenance costs and extending the lifespan of the infrastructure. This research explores the impact of reinforcing structural elements with advanced composite materials on the strength and durability of concrete and reinforced concrete structures. By integrating Carbon Fiber Reinforced Polymer (CFRP) reinforcements, we subjected both rectangular and T-section concrete beams to comprehensive three-point bending tests, revealing a substantial increase in flexural strength by 45% and crack resistance due to CFRP reinforcement. The study revealed that CFRP reinforcement increased the flexural strength of concrete beams by 45% and improved crack resistance significantly. Additionally, the load-bearing capacity of the beams was enhanced by 40% compared to unreinforced specimens. These improvements were validated through finite element simulations, which showed a close alignment with the experimental data. Furthermore, an innovative simulation study was conducted using a finely tuned finite element numerical model within the Abaqus calculation code. This model accurately replicated the laboratory specimens in terms of shape, dimensions, and loading conditions. The simulation results not only validated the experimental observations but also provided deeper insights into the stress distribution and failure mechanisms of the reinforced beams. Novel aspects of this study include the identification of specific failure patterns unique to CFRP-reinforced beams and the introduction of an enhanced interaction model that more accurately reflects the composite behavior under load. In CFRP-reinforced beams, specific failure patterns were identified, including flexural cracks in the tension zone and debonding of the CFRP sheets. These patterns indicate the points of maximum stress concentration and potential weaknesses in the reinforcement strategy. The study revealed that while CFRP significantly improves the overall strength and stiffness, careful attention must be given to the bonding process and the quality of the adhesive used to ensure optimal performance. These findings contribute significantly to the understanding of material interactions and structural performance, offering new pathways for the design and optimization of composite-reinforced concrete structures. This research underscores the transformative potential of composite materials in elevating the structural integrity and longevity of concrete infrastructures. Full article

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example. Full article

Accurate assessment of dough kneading is pivotal in pasta processing, where both under-kneading and over-kneading can detrimentally impact dough quality. This study proposes an innovative approach utilizing a cost-effective current sensor to ascertain the optimal kneading time for dough. Throughout the kneading process, the dough’s tensile resistance gradually increases, reflecting the evolution of properties such as the gluten network. This leads to a discernible ascending phase in dough quality, evident through an increase in the load current of the mixing machine, succeeded by a subsequent decline beyond a certain threshold. The identification of this peak point enables the achievement of optimal dough consistency, thereby enhancing the overall quality of both the dough and subsequent pasta products. After the final product quality assessment, this novel method promises to be a valuable tool in optimizing pasta processing and ensuring consistent product quality. Full article

During a heavy traffic flow featuring a substantial number of vehicles, the data reflecting the strain response of asphalt pavement under the vehicle load exhibit notable fluctuations with abnormal values, which can be attributed to the complex operating environment. Thus, there is a need to create a real-time anomalous-data diagnosis system which could effectively extract dynamic strain features, such as peak values and peak separation from the large amount of data. This paper presents a dynamic response signal data analysis method that utilizes the DBSCAN clustering algorithm and the findpeaks function. This method is designed to analyze data collected by sensors installed within the pavement. The first step involves denoising the data using low-pass filters and other techniques. Subsequently, the DBSCAN algorithm, which has been improved using the K-Dist method, is used to diagnose abnormal data after denoising. The refined findpeaks function is further implemented to carry out the adaptive feature extraction of the denoised data which is free from anomalies. The enhanced DBSCAN algorithm is tested via simulation and illustrates its effectiveness while detecting abnormal data in the road dynamic response signal. The findpeaks function enables the relatively accurate identification of peak values, thus leading to the identification of strain signal peaks of complex multi-axle lorries. This study is valuable for efficient data processing and effective information utilization in pavement monitoring. Full article