Exploring the Effects of Liquid Marbles' Deformation on Their Rolling Resistance.

Liquid marbles (LMs) are nonwetting droplets manufactured by encapsulating droplets with micro- or nanoscale particles. These marbles are widely used as transport carriers for digital microfluidics due to their rapid displacement velocity and leak-free transport. An improved understanding of the resistance mechanism of rolling LMs is crucial for their transport and manipulation. In this study, we investigated the rolling resistance of LMs obtained with different powders and volumes using a high-speed camera. Our findings suggest that the deformation of liquid marbles would hinder their rolling by a resistance torque. To depict this resistance effect, we propose a theoretical model (f∼λ(ε-12Bo1/2ε2+14Boε3)), where f is the rolling resistance of marbles, λ is the deflection coefficient, Bo is the Bond number, and (ε is the contact surface deformation) that accurately predicts the relationship between deformation and rolling resistance, which is supported by our experimental results. To further validate our theoretical model, we conducted three independent experiments: shape detection of prepared LMs, measuring the elastic force of LMs, and detecting the diffusive motion of the encapsulating particles. Furthermore, we discuss three factors that affect the rolling resistance: the volume of the marbles, the type and size of the encapsulating particles, and the substrate roughness. This comprehensive study not only generalizes the mechanism of deformation hindering the rolling of liquid marbles but also provides a theoretical framework to predict the relationship between the deformation and rolling resistance. These findings have practical implications for improving the manipulation efficiency and advancing the use of LMs as microfluidic carriers.

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods.

Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense. An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense, remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.

Surface Acoustic Wave Propagation of GaN/Sapphire Integrated with a Gold Guiding Layer.

Gallium nitride (GaN), widely known as a wide bandgap semiconductor material, has been mostly employed in high power devices, light emitting diodes (LED), and optoelectronic applications. However, it could be exploited differently due to its piezoelectric properties, such as its higher SAW velocity and strong electromechanical coupling. In this study, we investigated the affect of the presence of a guiding layer made from titanium/gold on the surface acoustic wave propagation of the GaN/sapphire substrate. By fixing the minimum thickness of the guiding layer at 200 nm, we could observe a slight frequency shift compared to the sample without a guiding layer, with the presence of different types of surface mode waves (Rayleigh and Sezawa). This thin guiding layer could be efficient in transforming the propagation modes, acting as a sensing layer for the binding of biomolecules to the gold layer, and influencing the output signal in terms of frequency or velocity. The proposed GaN/sapphire device integrated with a guiding layer could possibly be used as a biosensor and in wireless telecommunication applications.

Formulation and Characterization of Fe3O4@PEG Nanoparticles Loaded Sorafenib; Molecular Studies and Evaluation of Cytotoxicity in Liver Cancer Cell Lines.

Iron oxide nanoparticles are one of the nanocarriers that are suitable for novel drug delivery systems due to low toxicity, biocompatibility, loading capacity, and controlled drug delivery to cancer cells. The purpose of the present study is the synthesis of coated iron oxide nanoparticles for the delivery of sorafenib (SFB) and its effects on cancer cells. In this study, Fe3O4 nanoparticles were synthesized by the co-precipitation method, and then sorafenib was loaded onto PEG@Fe3O4 nanoparticles. FTIR was used to ensure polyethylene glycol (PEG) binding to nanoparticles and loading the drug onto the nanoshells. A comparison of the mean size and the crystalline structure of nanoparticles was performed by TEM, DLS, and X-ray diffraction patterns. Then, cell viability was obtained by the MTT assay for 3T3 and HepG2 cell lines. According to FT-IR results, the presence of O-H and C-H bands at 3427 cm-1 and 1420 cm-1 peak correlate with PEG binding to nanoparticles. XRD pattern showed the cubic spinel structure of trapped magnetite nanoparticles carrying medium. The magnetic properties of nanoparticles were examined by a vibrating-sample magnetometer (VSM). IC50 values at 72 h for treatment with carriers of Fe3O4@PEG nanoparticle for the HepG2 cell line was 15.78 µg/mL (p < 0.05). This study showed that Fe3O4 nanoparticles coated by polyethylene glycol and using them in the drug delivery process could be beneficial for increasing the effect of sorafenib on cancer cells.

Longitudinal HbA1c trajectory modelling reveals the association of HbA1c and risk of hospitalization for heart failure for patients with type 2 diabetes mellitus.

BACKGROUND: Inconsistent conclusions in past studies on the association between poor glycaemic control and the risk of hospitalization for heart failure (HHF) have been reported largely due to the analysis of non-trajectory-based HbA1c values. Trajectory analysis can incorporate the effects of HbA1c variability across time, which may better elucidate its association with macrovascular complications. Furthermore, studies analysing the relationship between HbA1c trajectories from diabetes diagnosis and the occurrence of HHF are scarce. METHODS: This is a prospective cohort study of the SingHealth Diabetes Registry (SDR). 17,389 patients diagnosed with type 2 diabetes mellitus (T2DM) from 2013 to 2016 with clinical records extending to the end of 2019 were included in the latent class growth analysis to extract longitudinal HbA1c trajectories. Association between HbA1c trajectories and risk of first known HHF is quantified with the Cox Proportional Hazards (PH) model. RESULTS: 5 distinct HbA1c trajectories were identified as 1. low stable (36.1%), 2. elevated stable (40.4%), 3. high decreasing (3.5%), 4. high with a sharp decline (10.8%), and 5. moderate decreasing (9.2%) over the study period of 7 years. Poorly controlled HbA1c trajectories (Classes 3, 4, and 5) are associated with a higher risk of HHF. Using the diabetes diagnosis time instead of a commonly used pre-defined study start time or time from recruitment has an impact on HbA1c clustering results. CONCLUSIONS: Findings suggest that tracking the evolution of HbA1c with time has its importance in assessing the HHF risk of T2DM patients, and T2DM diagnosis time as a baseline is strongly recommended in HbA1c trajectory modelling. To the authors' knowledge, this is the first study to identify an association between HbA1c trajectories and HHF occurrence from diabetes diagnosis time.

Elasticity and damping ratio measurement of droplets on super-hydrophobic surfaces.

The measurement of the droplets' elasticity is vitally important in microfluidic and ink-jet printing. It refers to the ability of the droplet to restore its original shape and strong robustness. This study investigated a novel method to measure elasticity. The plate coated with super-hydrophobic layers pressed on a droplet and the elastic force was recorded by an electronic balance. Meanwhile, a mathematical model was constructed to calculate the changes of the droplet area under the force. The measurement showed that external work mainly converts into surface energy and the damping ratio increases from 0.068 to 0.261 with the increase of mass fraction from 0 to 80 wt%. It also indicates that the novel method can accurately and efficiently measure the elasticity of droplets.

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19.

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Coalescence-Induced Droplet Jumping.

When two or more droplets coalesce on a superhydrophobic surface, the merged droplet can jump spontaneously from the surface without requiring any external energy. This phenomenon is defined as coalescence-induced droplet jumping and has received significant attention due to its potential applications in a variety of self-cleaning, anti-icing, antifrosting, and condensation heat-transfer enhancement uses. This article reviews the research and applications of coalescence-induced droplet jumping behavior in recent years, including the influence of droplet parameters on coalescence-induced droplet jumping, such as the droplet size, number, and initial velocity, to name a few. The main structure types and influence mechanism of the superhydrophobic substrates for coalescence-induced droplet jumping are described, and the potential application areas of coalescence-induced droplet jumping are summarized and forecasted.

Polarization-independent and high-efficiency broadband optical absorber in visible light based on nanostructured germanium arrays.

A broadband optical absorber based on the nanostructured germanium (Ge) film composed of single-sized circular nanodisk-nanohole arrays is proposed, which demonstrates high efficiency, strong polarization independence, and large viewing angle. Due to the electric and magnetic resonance absorption modes excited by the nanostructure arrays in highly lossy Ge film, the absorber obtains a high absorptivity, reaching above 90% over the full visible wavelength, and it can be maintained well at a large viewing angle over ±50°. Based on the geometrical symmetry, the absorber is proved to be polarization independent. Moreover, the simple single-sized nanostructure within a certain size tolerance decreases the design and fabrication complexity. The structural configuration with a slight Ge sidewall formed in the nanofabrication process could enhance the overall light absorption. These results indicate that the proposed broadband absorber has great potential in various applications such as anti-reflective coating and perfect cloaking.