Search Results (19)

This paper proposes an automated system integrating infrared thermography (IRT) and computer vision for air leak detection and localization in end-of-line (EOL) testing stations. This system consists of (1) a leak tester for detection and quantification of leaks, (2) an infrared camera for real-time thermal image acquisition; and (3) an algorithm for automatic leak localization. The python-based algorithm acquires thermal frames from the camera’s streaming video, identifies potential leak regions by selecting a region of interest, mitigates environmental interferences via image processing, and pinpoints leaks by employing pixel intensity thresholding. A closed circuit with an embedded leak system simulated relevant leakage scenarios, varying leak apertures (ranging from 0.25 to 3 mm), and camera–leak system distances (0.2 and 1 m). Results confirmed that (1) the leak tester effectively detected and quantified leaks, with larger apertures generating higher leak rates; (2) the IRT performance was highly dependent on leak aperture and camera–leak system distance, confirming that shorter distances improve localization accuracy; and (3) the algorithm localized all leaks in both lab and industrial environments, regardless of the camera–leak system distance, mostly achieving accuracies higher than 0.7. Overall, the combined system demonstrated great potential for long-term implementation in EOL leakage stations in the manufacturing sector, offering an effective and cost-effective alternative for manual inspections. Full article

Traditional centralized water systems are facing sustainability challenges due to climate and socioeconomic changes, extreme weather events, and aging infrastructure and their uncertainties. The energy sector has addressed similar challenges using the microgrid approach, which involves decentralized energy sources and their supply, improving system resilience and sustainable energy supply. This study investigated the resilience effects of water microgrids, which feature operational interactions between centralized and local systems for sustainable water supply. A lab-scale water distribution model was tested to demonstrate centralized, decentralized, and microgrid water systems under the disruption scenarios of pump shutdown, pump rate manipulation, and pipe leaks/bursts. The water microgrids integrate centralized and local systems’ operations, while the decentralized system operates independently. Then, functionality-based resilience and its attributes were evaluated for each disruption scenario. The results reveal that, overall, the microgrid configuration, with increased water supply redundancy and flexible operational adjustment based on system conditions, showed higher resilience, robustness, and recovery rate and a lower loss rate across disruption scenarios. The resilience effect of water microgrids was more evident with longer and more severe disruptions. Considering global challenges in water security under climate and socioeconomic changes, the findings suggest insights into a hybrid water system as a strategy to enhance resilience and water use efficiency and provide adaptive operations for sustainable water supply. Full article

This paper presents a novel centrifugal microfluidic approach (so-called lab-on-a-CD) for magnetic circulating tumor cell (CTC) separation from the other healthy cells according to their physical and acquired chemical properties. This study enhances the efficiency of CTC isolation, crucial for cancer diagnosis, prognosis, and therapy. CTCs are cells that break away from primary tumors and travel through the bloodstream; however, isolating CTCs from blood cells is difficult due to their low numbers and diverse characteristics. The proposed microfluidic device consists of two sections: a passive section that uses inertial force and bifurcation law to sort CTCs into different streamlines based on size and shape and an active section that uses magnetic forces along with Dean drag, inertial, and centrifugal forces to capture magnetized CTCs at the downstream of the microchannel. The authors designed, simulated, fabricated, and tested the device with cultured cancer cells and human cells. We also proposed a cost-effective method to mitigate the surface roughness and smooth surfaces created by micromachines and a unique pulsatile technique for flow control to improve separation efficiency. The possibility of a device with fewer layers to improve the leaks and alignment concerns was also demonstrated. The fabricated device could quickly handle a large volume of samples and achieve a high separation efficiency (93%) of CTCs at an optimal angular velocity. The paper shows the feasibility and potential of the proposed centrifugal microfluidic approach to satisfy the pumping, cell sorting, and separating functions for CTC separation. Full article

A transparent and adhesive film-based enclosing and sealing method is here presented for out-of-cleanroom-based open-form microfluidic devices. The commercially available polyester flexible film known as Microseal ‘B’ is presented in this paper as a cover seal for open-form microfluidic devices. This film is adaptable to high working temperatures and is biocompatible. The quality of the sealing film was investigated by leak tests, fluorescence tests, and contact angle measurements. The investigations revealed its sealing strength, fluorescence detection compatibility, and surface wettability. It was found that the proposed sealing polyester film on the 3D-printed device could sustain a gauge pressure of 2.7 atm at a flow rate of 4 mL/min without any leaks. It also provided fluorescence detection compatibility and an intensity-to-background ratio in the range of 2.3 to 4.5 for particle sizes of 5 μm and 15 μm, respectively, which is comparable with the performances of other sealing materials. The film’s hydrophobicity is comparable to other polymers used in microfluidics. This paper concludes by showcasing some applications of such transparent tops in classical microfluidic devices used for droplet generation and fluid mixing, in order to demonstrate the prospects of this fabrication technique in lab-on-a-chip devices. Full article

Introduction: The self-expanding, resheathable, repositionable transcatheter aortic heart valve Portico is being used successfully for transcatheter aortic valve implantation procedures (TAVI) in patients with severe aortic stenosis. The aim of this study was to evaluate outcomes at 2 years after TAVI with the Portico valve. Methods: Multicenter registry of clinical, echocardiographic and survival data from consecutive patients treated with the Portico TAVI system (Abbott, Chicago, IL, USA) in three cath labs in Northern Greece and Epirus during 2017–2020. The primary end point was all-cause mortality at 24 months. Secondary end points included procedural outcomes (efficacy and safety) and echocardiographic measurements. Results: A total of 90 patients (81 ± 6 years, 50% females, mean age 81 ± 6 years) were included in the registry. The indication for implantation was severe, symptomatic aortic stenosis (NYHA III, IV) in eighty-two (91.1%) and degeneration of a prosthetic aortic valve in eight (8.9%) patients. All patients were categorized as high surgical risk (mean Logistic Euroscore 25.9 ± 10, Euroscore II 7.7 ± 4.4 and STS score 10.8 ± 8.9). The procedure was performed transfemorally in all patients, under general anesthesia in 95.6%, under TOE guidance in 21.1%, with native valve predilatation in 46.7%, and the “resheath” option was used in 31.1% of the cases. The implantation was successful in 97.8% and there was a need for a second valve in 2.2% of the cases. Complications included permanent pacemaker implantation (16.7%), access cite complications (15.6%), arrythmias (23.3%), paravalvular leak (moderate 7.8%, severe 1.1%), acute kidney injury (7.8%), no strokes and one death during the procedure. Aortic valve peak velocity, peak and mean pressure gradients, were significantly reduced after the procedure. All-cause mortality at 1, 12 and 24 months was 4.4%, 6.7% and 7.8%, respectively. Conclusions: TAVI with the Portico system comprises an effective and safe solution for the management of severe, symptomatic aortic stenosis in high-risk surgical patients. Full article

As is the case for many others in the world, Mexican seas face complex pollution challenges; two of the contaminants that require special attention for their prevalence, possible chemical interactions, and relation to the country’s economy are leaked petroleum and microplastics (MP). This research assessed the sorption of total petroleum hydrocarbons (TPH) as fuel oil on microplastics in laboratory and field scenarios. Preliminary tests allowed the development and validation of a methodology to measure the sorbed fuel oil by Soxhlet extraction, with a 99.65% recovery rate. The amount of TPH sorbed in the lab followed the order LDPE > PS > PP > PVC > PET > HDPE, with the highest concentration found on LDPE. The sorption of fuel oil on microplastics is correlated to the surface area of the plastic particles and could also be related to the crystallinity of plastics. Sorption, for all plastics, was consistent with a second-order kinetic model. The analysis of field samples collected on beaches of the Gulf of Mexico varied from 1660 to 35,258 mg/kg MP. It must be noticed that, unlike others, this research quantified a family of contaminants, which could explain the high concentrations observed on microplastics. Full article

Bubbles are a common cause of microfluidic malfunction, as they can perturb the fluid flow within the micro-sized features of a device. Since gas bubbles form easily within warm cell culture reagents, degassing is often necessary for biomicrofluidic systems. However, fabrication of a microscale degasser that can be used modularly with pre-existing chips may be cumbersome or challenging, especially for labs not equipped for traditional microfabrication, and current commercial options can be expensive. Here, we address the need for an affordable, accessible bubble trap that can be used in-line for continuous perfusion of organs-on-chip and other microfluidic cultures. We converted a previously described, manually fabricated PDMS degasser to allow scaled up, reproducible manufacturing by commercial machining or fused deposition modeling (FDM) 3D printing. After optimization, the machined and 3D printed degassers were found to be stable for >2 weeks under constant perfusion, without leaks. With a ~140 µL chamber volume, trapping capacity was extrapolated to allow for ~5–20 weeks of degassing depending on the rate of bubble formation. The degassers were biocompatible for use with cell culture, and they successfully prevented bubbles from reaching a downstream microfluidic device. Both degasser materials showed little to no leaching. The machined degasser did not absorb reagents, while the FDM printed degasser absorbed a small amount, and both maintained fluidic integrity from 1 µL/min to >1 mL/min of pressure-driven flow. Thus, these degassers can be fabricated in bulk and allow for long-term, efficient bubble removal in a simple microfluidic perfusion set-up. Full article

Social media is not only an essential platform for the dissemination of public health-related information, but also an important channel for people to communicate during the COVID-19 pandemic. However, social bots can interfere with the social media topics that humans follow. We analyzed and visualized Twitter data during the prevalence of the Wuhan lab leak theory and discovered that 29% of the accounts participating in the discussion were social bots. We found evidence that social bots play an essential mediating role in communication networks. Although human accounts have a more direct influence on the information diffusion network, social bots have a more indirect influence. Unverified social bot accounts retweet more, and through multiple levels of diffusion, humans are vulnerable to messages manipulated by bots, driving the spread of unverified messages across social media. These findings show that limiting the use of social bots might be an effective method to minimize the spread of conspiracy theories and hate speech online. Full article

Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak detection operations in water distribution systems (WDSs). However, studies on leakage detection based on on-site leak signals are limited compared to studies on lab-scale leak detection. The on-site leak signals have stronger interference and randomness, while leak signals in the laboratory are relatively simpler. To better assist on-site leak detection operations, the present paper develops and compares three ML-based models. For this purpose, many on-site tests were carried out, and tens of thousands of sets of on-site leak detection signals were collected. More than 6000 sets of these signals were marked and the signal features were extracted and analyzed from a statistical point of view. It was found that features such as the main frequency, the spectral roll-off rate, the spectral flatness, and one-dimensional (1-D) Mel Frequency Cepstrum Coefficient (MFCC) could well distinguish the leakage signals from non-leakage signals. After training the decision tree model, the performances of the random forest and Adaboost models were thoroughly compared. It was found that the false positive rates of the three models were 9.80%, 8.27% and 7.35%, all lower than 10%. In particular, the Adaboost model had the lowest false positive rate of 7.35%. The recall rate of the random forest and Adaboost models were 100% and 99.52%. Full article

The adoption of digital contact-tracing apps to limit the spread of SARS-CoV-2 has been sup-optimal, but studies that clearly identify factors associated with the app uptake are still limited. In April 2021, we administered a questionnaire to healthcare university students to investigate their attitudes towards and experiences of the IMMUNI app. A multivariable logistic regression model was built to identify app download predictors. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were calculated. We surveyed 247 students. Most respondents (65.6%) had not downloaded IMMUNI, reporting as the main reason the perceived app uselessness (32.7%). In the multivariable analysis, being advised to use the app (aOR: 3.21, 95%CI: 1.80–5.73), greater fear of infecting others (aOR: 1.50, 95%CI: 1.01–2.23), and greater trust in the institutional response to the emergency (aOR: 1.33, 95%CI: 1.00–1.76) were positively associated with the outcome, whereas greater belief in the “lab-leak theory” of COVID-19 was a negative predictor (aOR: 0.75, 95%CI: 0.60–0.93). Major technical issues were reported by app users. Targeted strategies aimed at improving awareness of digital health applications should be devised. Furthermore, institutions should invest in the development of these technologies, to minimize technical issues and make them accessible to the entire population. Full article

Chemicals and industrial gases endanger both human health and the environment. The inhalation of colourless ammonia gas (NH₃) can cause organ damage or even death in humans. Colourimetric materials are becoming more popular in the search for smart textiles for both fashion and specific occupational applications. Colourimetric textile sensors based on indicator dyes could be very useful for detecting strong gaseous conditions and monitoring gas leaks. In this study, black carrot extract (BCE) as a natural indicator dye and polyurethane (PU) polymer were used to develop a colourimetric sensor by electrospinning. The properties of the BCE/PU nanofibrous mats were characterized by the Fourier transform infrared spectrum (FTIR) and a scanning electron microscope (SEM). The BCE caused a change in the morphology of the PU nanofibrous mat. To evaluate the colour shift due to NH₃ vapour, the BCE/PU nanofibrous mats were photographed by a camera, and software was used to obtain the quantitative colour data (CIE L*a*b). The BCE/PU nanofibrous exhibited a remarkable colour change from pink–red to green–blue under NH₃ vapour conditions with a fast response time (≤30 s). These findings showed that colourimetric nanofibrous textile sensors could be a promising in situ material in protective clothing that changes colour when exposed to harmful gases. Full article

The COVID-19 pandemic has been one of the most disruptive and painful phenomena of the last few decades. As of July 2021, the origins of the SARS-CoV-2 virus that caused the outbreak remain a mystery. This work analyzes the prevalence in news media articles of two popular hypotheses about SARS-CoV-2 virus origins: the natural emergence and the lab-leak hypotheses. Our results show that for most of 2020, the natural emergence hypothesis was favored in news media content while the lab-leak hypothesis was largely absent. However, something changed around May 2021 that caused the prevalence of the lab-leak hypothesis to substantially increase in news media discourse. This shift has not been uniformed across media organizations but instead has manifested itself more acutely in some outlets than others. Our structural break analysis of daily news media usage of terms related to the laboratory escape hypothesis provides hints about potential sources for this sudden shift in the prevalence of the lab-leak hypothesis in prestigious news media. Full article

In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness. Full article

Reservoir-based drug delivery microsystems have enabled novel and effective drug delivery concepts in recent decades. These systems typically comprise integrated storing and pumping components. Here we present a stand-alone, modular, thin, scalable, and refillable microreservoir platform as a storing component of these microsystems for implantable and transdermal drug delivery. Three microreservoir capacities (1, 10, and 100 µL) were fabricated with 3 mm overall thickness using stereolithography 3D-printing technology, enabling the fabrication of the device structure comprising a storing area and a refill port. A thin, preformed dome-shaped storing membrane was created by the deposition of parylene-C over a polyethylene glycol sacrificial layer, creating a force-free membrane that causes zero forward flow and insignificant backward flow (2% of total volume) due to membrane force. A septum pre-compression concept was introduced that enabled the realization of a 1-mm-thick septa capable of ~65000 leak-free refill punctures under 100 kPa backpressure. The force-free storing membrane enables using normally-open micropumps for drug delivery, and potentially improves the efficiency and precision of normally-closed micropumps. The ultra-thin septum reduces the thickness of refillable drug delivery devices, and is capable of thousands of leak-free refills. This modular and scalable device can be used for drug delivery in different laboratory animals and humans, as a sampling device, and for lab-on-a-chip and point-of-care diagnostics applications. Full article

In situ, continuous and real-time monitoring of respiration (R) and respiratory quotient (RQ) are crucial for identifying the optimal conditions for the long-term storage of fresh produce. This study reports the application of a gas sensor (RMS88) and a modular respirometer for in situ real-time monitoring of gas concentrations and respiration rates of strawberries during storage in a lab-scale controlled atmosphere chamber (190 L) and of Pinova apples in a commercial storage facility (170 t). The RMS88 consisted of wireless O₂ (0% to 25%) and CO₂ sensors (0% to 0.5% and 0% to 5%). The modular respirometer (3.3 L for strawberries and 7.4 L for apples) consisted of a leak-proof arrangement with a water-containing base plate and a glass jar on top. Gas concentrations were continuously recorded by the RMS88 at regular intervals of 1 min for strawberries and 5 min for apples and, in real-time, transferred to a terminal program to calculate respiration rates ( ${\mathrm{R}}_{{\mathrm{O}}_2}$ and ${\mathrm{R}}_{{\mathrm{CO}}_2}$ ) and RQ. Respiration measurement was done in cycles of flushing and measurement period. A respiration measurement cycle with a measurement period of 2 h up to 3 h was shown to be useful for strawberries under air at 10 °C. The start of anaerobic respiration of strawberries due to low O₂ concentration (1%) could be recorded in real-time. ${\mathrm{R}}_{{\mathrm{O}}_2}$ and ${\mathrm{R}}_{{\mathrm{CO}}_2}$ of Pinova apples were recorded every 5 min during storage and mean values of 1.6 and 2.7 mL kg⁻¹ h⁻¹, respectively, were obtained when controlled atmosphere (CA) conditions (2% O₂, 1.3% CO₂ and 2 °C) were established. The modular respirometer was found to be useful for in situ real-time monitoring of respiration rate during storage of fresh produce and offers great potential to be incorporated into RQ-based dynamic CA storage system. Full article