Search Results (48,293)

The small dimensions of microfluidic channels allow for fast diffusive or passive mixing, which is beneficial for time-sensitive applications such as chemical reactions, biological assays, and the transport of to-be-detected species to sensors. In microfluidics, the need for fast mixing within milliseconds arises primarily because these devices are often used in fields where rapid and efficient mixing significantly impacts the performance and outcome of the processes. Active mixing with acoustics in microfluidic devices involves using acoustic waves to enhance the mixing of fluids within microchannels. Using sharp corners and wall patterns in acoustofluidic devices significantly enhances the mixing by acoustic streaming around these features. The streaming patterns around the sharp edges are particularly effective for the mixing because they can produce strong lateral flows that rapidly homogenize liquids. This work presents extensive characterizations of the effect of sharp-edged structures on acoustic mixing in bulk acoustic wave (BAW) mode in a silicon microdevice. The effect of side wall patterns in different angles and shapes, their positions, the type of piezoelectric transducer, and its amplitude and frequency have been studied. Following the patterning of the channel walls, a mixing time of 25 times faster was reached, compared to channels with smooth side walls exhibiting conventional BAW behavior. The average locally determined acoustic streaming velocity inside the channel becomes 14 times faster if sharp corners of 10° are added to the wall. Full article

This paper proposes a new algorithm for the automatic generation of toolpaths for machining complex geometric positions, such as molds used in orthosis production. The production of individualized orthoses often requires the use of multi-axis machining systems, such as five-axis machines or industrial robots. Typically, complex and expensive CAD/CAM systems are used to generate toolpaths for these machines, requiring the definition of a machining strategy for each surface. While this approach can achieve a reliable and high-quality machining process, it is very time-consuming and makes it challenging to meet the criteria for rapid production of orthopedic aids. Given that their production is a custom-made process using individual shapes as inputs, the toolpath generation process becomes even more demanding. To address these challenges, this paper proposes an algorithm suitable for the automatic generation of toolpaths for such complex positions. The proposed algorithm has been tested and has proven to be robust and applicable. Full article

Rapid detection of a biological agent is essential to anticipate a threat to the protection of biodiversity and ecosystems. Our goal is to miniaturize a magnetic pathogen detection system in order to fabricate an efficient and portable system. The detection device is based on flat, multilayer coils associated with microfluidic structures to detect magnetic nanoparticles linked to pathogen agents. One type of immunological diagnosis is based on the measurement of the magnetic sensitivity of magnetic nanoparticles (MNPs), which are markers connected to pathogens. This method of analysis involves the coupling of antibodies or antigen proteins with MNPs. Among the available magnetic techniques, the frequency mixing method has a definite advantage by making it possible to quantify MNPs. An external magnetic field composed of a low- and a high-frequency field is applied to the sample reservoir. Then, the response signal is measured and analyzed. In this paper, magnetic microcoils are implemented on a multilayer Printed Circuit Board (PCB), and a microfluidics microstructure is designed in connection with the planar coils. Simulation software, COMSOL version 5.3, provides an analytical perspective to choose the number of turns in magnetic coils and to understand the effects of changing the shape and dimensions of the microfluidics microstructure. Full article

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades. Full article

Microbially induced carbonate precipitation (MICP) technology is employed to reinforce the surface soil of a dam, aiming to prevent erosion caused by water flow and damage to the dam slope. The relationship between penetration depth, calcium carbonate content, and bonding depth was investigated at eight measuring points on the sand slope surface of a mold under different reinforcement durations. It was observed that as grouting reinforcement times increased, there was a gradual increase in calcium carbonate content but a rapid rise in penetration resistance. Moreover, the bonding depth of sand on the bio-reinforced sand slope increased with higher levels of calcium carbonate content. Microbial grouting reinforcement enhanced soil particle bonding force, requiring water flow to overcome this force for activation of sand particles. Consequently, microbial grouting reinforcement significantly improved shear strength and critical starting flow velocity on sand slope surfaces. The experimental results demonstrated that after MICP surface treatment through spraying, a dense and water-stable hard shell layer composed of bonded calcium carbonate and soil particles formed continuously on sample surfaces, effectively enhancing the strength and erosion resistance of sandy soils. These findings provide reliable evidence for silt slope reinforcement and dam erosion prevention. Full article

Bacillus cereus, a foodborne pathogen, produces resilient endospores that are challenging to detect with conventional methods. This study presents a novel Flower-Shaped PCR Scaffold-based Lateral Flow Biosensor (FSPCRS-LFB), which employs an aptamer-integrated PCR scaffold as capture probes, replacing the traditional streptavidin-biotin (SA-Bio) approach. The FSPCRS-LFB demonstrates high sensitivity and cost-efficiency in detecting B. cereus endospores, with a limit of detection (LOD) of 4.57 endospores/mL a visual LOD of 10² endospores/mL, and a LOD of 6.78 CFU/mL for endospore-cell mixtures. In chicken and tea samples, the platform achieved LODs of 74.5 and 52.8 endospores/mL, respectively, with recovery rates of 82.19% to 97.88%. Compared to existing methods, the FSPCRS-LFB offers a 3.7-fold increase in sensitivity while reducing costs by 26% over the SA-Bio strategy and 87.5% over rolling circle amplification (RCA). This biosensor provides a rapid, sensitive and cost-effective solution for point-of-care testing (POCT) of B. cereus endospores, expanding detection capabilities and offering novel approaches for pathogen detection. Full article

While mangrove ecosystems are rich in biodiversity, they are increasingly impacted by climate change and urban pollutants. The current study provides first insights into the emergence of potentially pathogenic yeasts in Hong Kong’s mangroves. Sediment and water samples were collected from ten urban and rural mangroves sites. Initial CHROMagar^TM Candida Plus screening, representing the first application of this differential medium for water and soil samples collected from a non-clinical environment, enabled the rapid, preliminary phenotypic identification of yeast isolates from mangroves. Subsequent molecular profiling (ITS and/or 28S nrDNA sequencing) and antifungal drug susceptibility tests were conducted to further elucidate yeast diversity and drug resistance. A diversity of yeasts, including 45 isolates of 18 distinct species across 13 genera/clades, was isolated from sediments and waters from Hong Kong mangroves. Molecular profiling revealed a dominance of the Candida/Lodderomyces clade (44.4%), a group of notorious opportunistic pathogens. The findings also reveal a rich biodiversity of non-Candida/Lodderomyces yeasts in mangroves, including the first reported presence of Apiotrichum domesticum and Crinitomyces flavificans. A potentially novel Yamadazyma species was also discovered. Remarkably, 14.3% of the ubiquitous Candida parapsilosis isolates displayed resistance to multiple antifungal drugs, suggesting that mangroves may be reservoirs of multi-drug resistance. Wildlife, especially migratory birds, may disseminate these hidden threats. With significant knowledge gaps regarding the environmental origins, drug resistance, and public health impacts of pathogenic yeasts, urgent surveillance is needed from a One Health perspective. This study provides an early warning that unrestrained urbanization can unleash resistant pathogens from coastal ecosystems globally. It underscores the necessity for enhanced surveillance studies and interdisciplinary collaboration between clinicians, ornithologists, and environmental microbiologists to effectively monitor and manage this environmental health risk, ensuring the maintenance of ‘One Health’. Full article

With the rapid advancements in AI technology, UAV-based inspection has become a mainstream method for intelligent maintenance of PV power stations. To address limitations in accuracy and data acquisition, this paper presents a defect detection algorithm for PV panels based on an enhanced YOLOv8 model. The PV panel dust dataset is manually extended using 3D modeling technology, which significantly improves the model’s ability to generalize and detect fine dust particles in complex environments. SENetV2 is introduced to improve the model’s perception of dust features in cluttered backgrounds. AKConv replaces traditional convolution in the neck network, allowing for more flexible and accurate feature extraction through arbitrary kernel parameters and sampling shapes. Additionally, a DySample dynamic upsampler accelerates processing by 8.73%, improving the frame rate from 87.58 FPS to 95.23 FPS while maintaining efficiency. Experimental results show that the 3D image expansion method contributes to a 4.6% increase in detection accuracy, an 8.4% improvement in recall, a 5.7% increase in mAP@50, and a 15.1% improvement in mAP@50-95 compared to the original YOLOv8. The expanded dataset and enhanced model demonstrate the effectiveness and practicality of the proposed approach. Full article

Trichodinidae, a prevalent group of protozoan ectoparasites in aquaculture, cause rapid mortality in fish hatcheries. Despite their significance, knowledge about these parasites in farmed fish in South America, especially in native species that are currently being domesticated for aquaculture, remains limited. This study morphologically characterized the Trichodinid species that are infecting Pacific fat sleeper (Dormitator latifrons) larvae and evaluated their impact on larval rearing. Four pairs of broodstock were induced with GnRHA implants and placed in tanks containing 200 L of freshwater, with a water temperature of 28 ± 1.0 °C and a dissolved oxygen level of 4.00 ± 1.23 mg L⁻¹, with partial water exchanges being performed daily. The larvae hatched 7 to 8 h after fertilization and were transferred to tanks containing water with the same quality parameters. Twelve hours post-hatching, the presence of Trichodina was observed. Every 24 h, 60 larvae per tank (n = 180 per treatment) were sedated, and larval wet mounts were prepared, air-dried at room temperature, and impregnated with silver nitrate. Infection parameters and daily mortality were calculated. Trichodina was observed to parasitize the pelvic fins, caudal fins, and heads of fish larvae, which showed lethargy and erratic swimming movements. The Trichodina species showed a daily increase in the infection parameters, and a 58% rate of larval mortality was observed at the fourth day post-hatching (dph) in the infected tanks. In captivity, D. latifrons larvae typically survive up to 7 days post-hatching (dph) before reaching their point of no return due to the lack of adequate diet and feeding regimes. However, our study indicates that Trichodina infestation accelerates mortality, causing infected larvae to die more quickly than uninfected ones. Full article

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user’s intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system’s real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system’s ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses’ algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management. Full article

Given the ongoing COVID-19 pandemic, there is a need to identify SARS-CoV-2 and to differentiate it from other respiratory viral infections, especially influenza A and B, in various critical settings. Since their introduction, the use of rapid antigen tests has spread worldwide, but there is variability in their diagnostic accuracy. In the present study, we evaluated the clinical performance of the ID NOW™ COVID-19 2.0, a molecular point-of-care test (POCT) based on enzymatic isothermal amplification for the differential diagnosis of SARS-CoV-2 and influenza A/B in a pediatric emergency setting. A cohort of pediatric patients admitted between December 2022 and February 2023 were simultaneously tested with the POCT and standard laboratory molecular assay. Our findings showed high negative agreement of the POCT assay across the different age groups for SARS-CoV-2, influenza A, and influenza B (more than 98.0%), while its positive agreement varied significantly for the abovementioned viral species from 50.0% to 100%. These results highlight the potential of the ID NOW™ COVID-19 2.0 POCT assay as a reliable and rapid tool for excluding SARS-CoV-2 and influenza A/B infections in symptomatic pediatric patients, although its variable positive agreement suggests a need for confirmatory RT-qPCR testing in certain clinical and epidemiological settings in order to ensure accurate diagnosis and appropriate patient management. Full article

Conducting polymers are emerging as promising alternatives to rare and expensive platinum for counter electrodes in dye-sensitized solar cells; due to their ease of synthesis, they can be chemically tuned and are suitable for roll-to-roll production. Among these, poly (3,4-ethylenedioxythiophene) (PEDOT)-based counter electrodes have shown leading photovoltaic performance. However, certain conductivity issues remain that affect the effectiveness of these counter electrodes. In this study, we present an electropolymerized PEDOT and poly(N-alkylated-carbazole) copolymer as an efficient electrocatalyst for the reduction in ${\mathrm{I}}_3^{-}$ in dye-sensitized solar cells. Copolymerization with N-alkylated carbazoles significantly increases the conductivity of the polymer film and facilitates rapid charge transport at the interface between the polymer electrode and the electrolyte. The length of the alkyl substituents also plays a crucial role in this improvement. Electrochemical analysis showed a reduction in charge transport resistance from 3.31 Ω·cm² for PEDOT to 2.26 Ω·cm² for the PEDOT:poly(N-octylcarbazole) copolymer, which is almost half the resistance of a platinum-based counter electrode (4.12 Ω·cm²). Photovoltaic measurements showed that the solar cell with the PEDOT:poly(N-octylcarbazole) counter electrode achieved an efficiency of 8.88%, outperforming both PEDOT (7.90%) and platinum-based devices (7.57%). Full article

Food packaging, which is typically made of paper/cardboard, glass, metal, and plastic, is essential for protecting and preserving food. However, the impact of conventional food packaging and especially the predominant use of plastics, due to their versatility and low cost, bring serious environmental and health problems such as pollution by micro and nanoplastics. In response to these challenges, biotechnology emerges as a new way for improving packaging by providing biopolymers as sustainable alternatives. In this context, bacterial cellulose (BC), a biodegradable and biocompatible material produced by bacteria, stands out for its mechanical resistance, food preservation capacity, and rapid degradation and is a promising solution for replacing plastics. However, despite its advantages, large-scale application still encounters technical and economic challenges. These include high costs compared to when conventional materials are used, difficulties in standardizing membrane production through microbial methods, and challenges in optimizing cultivation and production processes, so further studies are necessary to ensure food safety and industrial viability. Thus, this review provides an overview of the impacts of conventional packaging. It discusses the development of biodegradable packaging, highlighting BC as a promising biopolymer. Additionally, it explores biotechnological techniques for the development of innovative packaging through structural modifications of BC, as well as ways to optimize its production process. The study also emphasizes the importance of these solutions in promoting a circular economy within the food industry and reducing its environmental impact. Full article

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system’s high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry. Full article

In recent years, with the rapid advancement of automotive lightweight technology, the mechanical clinching process between aluminum alloy and ultra-high-strength steel sheets has received extensive attention. However, the low ductility of ultra-high-strength steel sheets often results in conventional mechanical clinching processes producing joints that either fail to establish effective interlocks or cause the steel sheets to fracture. To address this issue, a novel mechanical clinching process is presented, called center-punching mechanical clinching (CPMC). This innovative process employs a method of punching, flanging, and bulging gradation to achieve the mechanical clinching of aluminum alloy and ultra-high-strength steel sheets in a single step. In order to determine the effects of different parameters on the quality and strength of the joint, an experimental study was carried out for various die depths and diameters based on the condition of constant punch size. Based on tensile and shear tests, the static strength and failure modes of CPMC joints were analyzed. The results indicated that the CPMC process significantly enhances the connectivity of joints for AA5052 aluminum alloy and DP980 ultra-high-strength steel. Optimal tensile and shear strengths of 1264 and 2249 N, respectively, were achieved at a die depth of 2.2 mm and a diameter of 10.4 mm. The CPMC process provides new ideas for the mechanical clinching of aluminum alloy and ultra-high-strength steels. Full article