Conventional Techniques Research Articles

Beyond the Dusty Fog: Local Eye Drop Therapy and Potentially New Treatment Alternatives in Pseudoexfoliative Glaucoma.

Pseudoexfoliative glaucoma (PEG) is a type of secondary open-angle glaucoma characterized by the accumulation of whitish-gray material on the trabecular meshwork and lens, leading to an increase in intraocular pressure (IOP) and optic nerve damage. Local eye drop therapy is one of the first-line treatments for PEG, which include prostaglandin analogues, beta-blockers, and alpha-adrenergic agonists to lower IOP. New treatments beyond conventional techniques, however, are constantly being developed. One potential treatment proposed for PEG is based on magnetic phage display, which involves using magnetic nanoparticles conjugated to specific peptides or proteins selected using phage display techniques to remove aggregates in the anterior chamber of the eye or inflammatory cells and cytokines that contribute to PEG pathogenesis. Other potential treatments include microRNAs (miRNAs) that are involved in the regulation of gene expression at the post-transcription stages. Gene therapies, nanotechnology, immunotherapy and methods based on stem cells can also be potentially used to target and treat specific tissues and cells responsible for regulating IOP. In addition, photobiomodulation therapy (PBMT), a non-invasive procedure that utilizes low-level laser therapy to improve cellular function and promote tissue repair, can prove an interesting alternative in treating PEG. The aim of our mini-review is to provide a brief overview of these innovative methods that appear to offer potentially promising treatment options for PEG.

Event-Based Background-Oriented Schlieren.

Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding. However, conventional frame-based techniques require both high spatial and temporal resolution cameras, which impose bright illumination and expensive computation limitations. Event cameras offer potential advantages (high dynamic range, high temporal resolution, and data efficiency) to overcome such limitations due to their bio-inspired sensing principle. This article presents a novel technique for perceiving air convection using events and frames by providing the first theoretical analysis that connects event data and schlieren. We formulate the problem as a variational optimization one combining the linearized event generation model with a physically-motivated parameterization that estimates the temporal derivative of the air density. The experiments with accurately aligned frame- and event camera data reveal that the proposed method enables event cameras to obtain on par results with existing frame-based optical flow techniques. Moreover, the proposed method works under dark conditions where frame-based schlieren fails, and also enables slow-motion analysis by leveraging the event camera's advantages. Our work pioneers and opens a new stack of event camera applications, as we publish the source code as well as the first schlieren dataset with high-quality frame and event data.

Critical Review on Water Quality Analysis using IoT and Machine Learning Models

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95% accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85% accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS.) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis.

A Weighted Stacking Ensemble Model With Sampling for Fake Reviews Detection

Customers use reviews as a primary source of information to judge a product or service. Positive reviews help boost companies’ reputations, increasing their revenue by attracting new clients, and increasing the purchasing order size. On the other hand, negative reviews significantly reduce sales, which might be the case due to competitive advantage. Organizations can use fake (i.e., misleading or fraudulent) reviews to generate fast profits by deceiving customers into buying their products. Recently, various methods to assess the legitimacy of reviews have been introduced using advances in machine learning. However, existing methods fall short of achieving highly accurate detection results for unbalanced classes. We aimed to create a spam review identification model using ensemble-based learning while balancing classes using sampling techniques. This article proposes a weighted stacking ensemble model with sampling (WSEM-S) for efficient fake reviews detection. We used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$</tex-math> </inline-formula> -gram models to effectively model language data for feature retrieval. The experimental results on three customer reviews datasets: YELPNYC, Deceptive Opinion Spam Corpus (DOSC) v1.4, and Deception datasets show that the proposed model outperforms the conventional machine learning techniques Naïve Bayes, logistic regression, K-nearest neighbor (KNN), random forest, extreme gradient boosting (XGBoost), and convolutional neural network (CNN) as well as the state-of-the-art ensemble models.

Effects of sublingual colostrum application on oral and intestinal flora of extremely low birth weight infants.

The aim of this study is to analyze the effects of colostrum application on the establishment of normal flora in the intestinal tracts and oral cavities of extremely low birth weight infants (ELBWI). A prospective cohort study design was adopted following the STROBE guidelines (Supplementary File 1). Colostrum was administered immediately after obtaining maternal breast milk using a special sterile cotton swab. There were no specific treatments for infants who did not receive colostrum. This experiment was completed on day 5 post-birth and the patients were divided into the colostrum and control groups, corresponding to whether or not colostrum was administered. Throat swabs and stool samples were collected on days 1 and 5 post-birth. Using the conventional bacteria cultivation technique, the detection rate of bacteria in 98 cases of meconium at birth was 15.31%. On day 5, the detection rates of Staphylococcus in the colostrum and control groups were 36.54% and 34.78%, with no significant difference between them (P = 0.856), and that of Enterococcus was 26.92% and 13.04%, respectively, with no statistically significant difference (P = 0.089). Likewise, at birth, the detection rate of bacteria in 98 cases of throat swabs was 27.55%. On day 5, the detection rate of Streptococcus in the colostrum and control groups was 78.85% and 50.00%, respectively, recording a statistically significant difference this time (P = 0.003). Colostrum application had limited effects on intestinal flora colonization but contributes to physiological oral flora colonization.

Three Dimensional Microwave Data Inversion in Feature Space for Stroke Imaging.

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

Accurate drone corner position estimation in complex backgrounds with boundary classification

This study develops an efficient approach for precise channel frame detection in complex backgrounds, addressing the critical need for accurate drone navigation. Leveraging YOLACT and group regression, our method outperforms conventional techniques that rely solely on color information. We conducted extensive experiments involving channel frames placed at various angles and within intricate backgrounds, training the algorithm to effectively recognize them. The process involves initial edge image detection, noise reduction through binarization and erosion, segmentation of channel frame line segments using the Hough Transform algorithm, and subsequent classification via the K-means algorithm. Ultimately, we obtain the regression line segment through linear regression, enabling precise positioning by identifying intersection points. Experimental validations validate the robustness of our approach across diverse angles and challenging backgrounds, making significant advancements in UAV applications.

Revisiting the Crystallography of {225}γ Martensite: How EBSD Can Help to Solve Long-Standing Controversy

Explaining the crystallography of iron alloys martensite with a {225}γ habit plane remains a challenging task within the phenomenological theory of martensite crystallography. The purpose of this study is to re-examine the martensite formed in a Fe-8Cr-1.1C alloy using EBSD, which has a better angular resolution than the conventional transmission electron diffraction techniques previously used. The results show that the single morphological plates, which hold a near {225}γ habit plane, are bivariant composites made up of two twin-related variants. It is shown that a {113}γ plane is systematically parallel to one of the three common 112α planes between the two twin-related crystals. This observation suggests that the lattice invariant strain of transformation occurs through a dislocation glide on the {113}γ ⟨110⟩γ system, rather than through twinning as is commonly accepted. Based on this assumption, the predictions of Bowles and Mackenzie’s original theory are in good agreement with the crystallographic features of {225}γ martensite. Unexpectedly, it is the high shear solution of the theory that gives the most accurate experimental predictions.

Image and video analysis using graph neural network for Internet of Medical Things and computer vision applications

AbstractGraph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification. The authors explore in depth the applications of GNNs in computer vision, including their design considerations, architectural challenges, applications, and implementation concerns. While conventional convolutional neural networks (CNNs) excel at object recognition in images and videos, GNN architectures offer a novel method for addressing various image and video comprehension challenges. A novel deep neural network‐based model for image and video analysis is proposed, which combines a neural network with fully connected layers on a graph. The proposed architecture extracts highly discriminative information from images and videos by leveraging the graph structure. Also, the investigation focuses on the enhancement of underlying connection network estimation using cutting‐edge graph learning algorithms. Experimental results on real‐world datasets demonstrate that the proposed GNN model is preferable to existing state‐of‐the‐art methods. It obtains a remarkable 96.63% accuracy on the ImageNet dataset, outperforming heuristic approaches, artificial neural networks, and conventional CNN techniques. From the results, we can see that GNNs are a potent instrument for graph data analysis and pave the way for machines to achieve human‐level visual intuition.

Tool path planning of ball-end milling of free-form surfaces as a search algorithm

This paper introduces an innovative approach for generating three-axis CNC tool paths for machining free-form surfaces. The method is designed to minimize variations in the effective tool diameter, addressing a common challenge encountered when using ball-end tools for machining free-form surfaces. These surfaces exhibit varying inclinations, leading to fluctuations in the tool’s working diameter from one point to another, resulting in inconsistent cutting speeds and milling parameters despite a constant spindle speed. Consequently, the machined surface tends to lack uniformity. In contrast to conventional tool path planning techniques, the proposed method calculates the working diameter at each adjacent point and guides the tool’s movement towards the point where the smallest change in working diameter is anticipated. This approach reduces fluctuations in cutting speed and promotes the generation of a more homogeneous surface.

Mimicking Motor Proteins: Wall-Guided Self-Navigation of Microwheels.

Untethered micro/nanorobots (MNRs) show great promise in biomedicine. However, high-precision targeted in vivo navigation of MNRs into both deep and tiny microtube networks comes with big challenges because the present medical imaging cannot simultaneously meet the requirements of high resolution, high penetration depth, and high real-time performance. Inspired by intracellular motor proteins that transport cargo along cytoskeletal tracks, this study proposed a microtube inwall-guided targeted self-navigation strategy of magnetic microwheels (μ-wheels) that relies only on interactions with a microtube inwall, compared to conventional techniques that rely on real-time imaging and tracking of MNRs. By presetting the direction of the rotating magnetic field, the μ-wheel realized targeted navigation along the inwall. The propulsion principles behind it are elaborated. The targeted self-navigation of the μ-wheels in three-dimensional microtube networks, a spiral microtube, and an intrahepatic bile duct of a pig was conducted. Lastly, based on the strategy, a practical tumor early detection method was proposed and verified by means of magnetic resonance imaging. The microtube inwall-guided targeted self-navigation strategy reduces the dependence of in vivo targeted navigation of MNRs on the real-time performance of medical imaging technology and greatly contributes to the development of MNRs in biomedical applications.

Development of a high‐pressure infiltration process for phenol–formaldehyde matrix composites

AbstractPhenol–formaldehyde (phenolic) thermosets are known for excellent heat and chemical resistance, high flame retardance, and good mechanical performance. However, phenolics are also known for their high brittleness, and tendency to form voids, due to a condensation reaction forming water during curing. These voids can decrease the mechanical performance of the resultant phenolic composite and introduce undesirable performance characteristics. This work aims to develop a technique that uses high‐pressure infiltration to obtain dense phenolic matrix composites, with commercially available resin and fiber reinforcement. The high‐pressure system developed in this work is compared to a conventional low‐pressure resin infusion technique, and the porosity after each infusion is analyzed. A model of the low‐ and high‐pressure systems was developed, and the predicted time of infiltration was compared to the experimental results. The high‐pressure system had 97% less open porosity after infusion than the low‐pressure technique, suggesting that it can produce a higher‐quality and better‐performing phenolic composite than conventional techniques. Also, the mechanical test performed indicates improved performance for the high‐pressure injection with a 30.73% increase in ultimate tensile strength in comparison to low pressure, indicating better mechanical performance. © 2024 Society of Industrial Chemistry.

Comparative evaluation of electrical resistance tomography, positron emission particle tracking and high-speed imaging for analysing horizontal particle-liquid flow in a pipe

We evaluate three experimental techniques - electrical resistance tomography (ERT), positron emission particle tracking (PEPT) and high-speed imaging (HSI) – for analysing the local particle velocity field and spatial distribution in a horizontal particle-liquid pipe flow under varying conditions of solid concentration. A new ERT methodology is devised for estimating particle velocity, circumventing the limitations of the conventional cross-correlation technique. Furthermore, an enhanced HSI approach is introduced and systematically compared with PEPT and ERT. Results show that, under all conditions, PEPT provides the most accurate particle velocity field followed by HSI, whilst ERT yields the most accurate concentration field, followed by HSI. The enhanced HSI emerges as a simple cost-effective option compared to PEPT and ERT. A combined measurement approach using PEPT for local particle velocity and ERT for local concentration, however, delivers the best comprehensive two-phase flow characterization, highlighting potential synergies between these methods for complex flow studies.

In situ three-dimensional visualization of Bursaphelenchus xylophilus inoculated in Pinus thunbergii using X-ray micro-computed tomography

Pine wilt disease, a devastating infectious disease of pine trees, is caused by the pinewood nematode, Bursaphelenchus xylophilus. It is important to understand the spatial dispersal processes of B. xylophilus within host-tree tissues to assess its pathogenic mechanism. However, this is not feasible with conventional microscopy-imaging techniques. In this study, we showed that X-ray micro-computed tomography (micro-CT) imaging can be a powerful tool for visualizing infected nematodes within host-tree tissues. We visualized 161 nematodes and 11 eggs within a Pinus thunbergii stem section, 47.3 mm3 in volume, using an appropriate segmentation of the micro-CT images. Quantitative measurements of the segmented region corresponding to the nematodes allowed for the calculation of the longitudinal length and a-value, which were similar to previous morphological descriptions of B. xylophilus. The technique adopted in this study can aid in understanding the behavior of and obtaining quantitative information on B. xylophilus within tree tissues.

Parametric Study of Die Sinking EDM of H13 Steel using Taguchi Techniques

As for H13 die steel, it is distinguished by a high hardness and a unique surface characteristics that must be created during machining using non-standard methods. One of such non conventional manufacturing techniques involving the use of Electrical Discharge Machining (EDM) for machining complex or hard material parts. It indicates that EDM has progressed from a series of a tool and die process to an alternative micro-scale application machining process. The paper is aimed at elaborating mathematical models defining the link between the MRR and SR to measured input parameters (current, pulse-on time, pulse-off time, and Reference voltage) in hot work steel EDM. With a L9 orthogonal involving four variables at three levels were used. The values of the model quality factors analyzed by the ANOVA procedure show that the selected mathematical models describe the process performance within successfully studied range with high level of precision. To determine the optimal condition, Taguchi method was used to measure the performance impact of different process parameters and find out best mixing. Minitab V 21 software has been used for the analysis and explanations. A good agreement between experimental value and the predicted one was found.

Ohmic heating of chicken sausage: electrical conductivities, heating characteristics, temperature prediction, and the comparison with conventional heating methods

Abstract In this study, chicken sausages were prepared with different levels of salt (1.50 or 1.125 %) and sodium nitrite (0, 80, 125, or 250 ppm) for electrical conductivity determination. Three different heating methods were applied: smokehouse, water bath, and ohmic. The main objectives were to investigate: (1) the electrical conductivity of sausages with different formulations; (2) the heating pattern of sausages during ohmic heating compared to the conventional techniques; and (3) the accuracy of mathematical models for sample temperature prediction. The results demonstrated that the electrical conductivity values of sausage samples were suitable for ohmic heating. In ohmic heating experiments, the heating rate in the sausage center was greater than on the surface. The sausage temperature was higher than the surrounding liquid at the end of ohmic heating experiment which was opposite to the conventional heating. Overall, the most accurate model for sausage temperature prediction was basic model case iii.

Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.

The Impact of Mobile Applications on Quran Education: A Survey of Student Performance and Satisfaction.

In the field of Quranic education, mobile applications have become cutting edge instruments that offer chances to improve instruction and successfully involve learners. With a thorough survey-based analysis, this study seeks to evaluate how mobile applications affect Quranic teaching. The research methodology comprised creating a structured questionnaire to collect information on a range of topics, such as application usage trends, efficacy evaluations, contributions to bettering education, perceived effects on students' comprehension and retention of Quranic knowledge, improvement of student performance, and contrasts between traditional and mobile application-based learning approaches. Students enrolled in online university programs with an emphasis on Quranic education made up the population for this study. A sample size of 60 respondents provided insightful information. Descriptive statistics and inferential techniques were included in quantitative data analysis, which allowed for a detailed investigation of survey responses and the correlations between variables. The results show that respondents place a high value on mobile applications for improving Quranic education, and the usage patterns of these applications vary widely. Participants generally held favorable opinions about the usefulness of mobile applications in raising student achievement and comprehension of Quranic knowledge. Furthermore, it became evident that mobile application-based learning was preferred over conventional techniques. The study identifies opportunities for further research in this area and emphasizes the significance of using mobile technology to enhance Quranic instruction. In general, the study advances our understanding of how mobile applications fit into Quranic education and influences pedagogical strategies used in online learning environments.

Reducing contact resistance of MoS2-based field effect transistors through uniform interlayer insertion via atomic layer deposition.

Molybdenum disulfide (MoS2), a semiconducting two-dimensional layered transition metal dichalcogenide (2D TMDC), with attractive properties enables the opening of a new electronics era beyond Si. However, the notoriously high contact resistance (RC) regardless of the electrode metal has been a major challenge in the practical applications of MoS2-based electronics. Moreover, it is difficult to lower RC because the conventional doping technique is unsuitable for MoS2 due to its ultrathin nature. Therefore, the metal-insulator-semiconductor (MIS) architecture has been proposed as a method to fabricate a reliable and stable contact with low RC. Herein, we introduce a strategy to fabricate MIS contact based on atomic layer deposition (ALD) to dramatically reduce the RC of single-layer MoS2 field effect transistors (FETs). We utilize ALD Al2O3 as an interlayer for the MIS contact of bottom-gated MoS2 FETs. Based on the Langmuir isotherm, the uniformity of ALD Al2O3 films on MoS2 can be increased by modulating the precursor injection pressures even at low temperatures of 150 °C. We discovered, for the first time, that film uniformity critically affects RC without altering the film thickness. Additionally, we can add functionality to the uniform interlayer by adopting isopropyl alcohol (IPA) as an oxidant. Tunneling resistance across the MIS contact is lowered by n-type doping of MoS2 induced by IPA as the oxidant in the ALD process. Through a highly uniform interlayer combined with strong doping, the contact resistance is improved by more than two orders of magnitude compared to that of other MoS2 FETs fabricated in this study.

Dual-Modified Hyaluronic Acid for Tunable Double Cross-Linked Hydrogel Adhesives.

Conventional techniques for the closure of wounds, such as sutures and staples, have significant drawbacks that can negatively impact wound healing. Tissue adhesives have emerged as promising alternatives, but poor adhesion, low mechanical properties, and toxicity have hindered their widespread clinical adoption. In this work, a dual modified, aldehyde and methacrylate hyaluronic acid (HA) biopolymer (HA-MA-CHO) has been synthesized through a simplified route for use as a double cross-linked network (DCN) hydrogel (HA-MA-CHO-DCN) adhesive for the effective closure and sealing of wounds. HA-MA-CHO-DCN cross-links in two stages: initial cross-linking of the aldehyde functionality (CHO) of HA-MA-CHO using a disulfide-containing cross-linker, 3,3'-dithiobis (propionic hydrazide) (DTPH), leading to the formation of a self-healing injectable gel, followed by further cross-linking via ultraviolet (UV) initiated polymerization of the methacrylate (MA) functionality. This hydrogel adhesive shows a stable swelling behavior and remarkable versatility as the storage modulus (G') has shown to be highly tunable (103-105 Pa) for application to many different wound environments. The new HA-MA-CHO-DCN hydrogel showed excellent adhesive properties by surpassing the burst pressure and lap-shear strength for the widely used bovine serum albumin-glutaraldehyde (BSAG) glue while maintaining excellent cell viability.