Conventional Techniques Research Articles

Early Detection of Colorectal Cancer: Conventional Techniques and Current Biomarkers

Abstract: Colorectal cancer (CRC) is the third most common cancer worldwide, and the incidence of CRC seems to increase gradually. The survival of CRC varies in different countries, attributed to the screening program. Generally, diagnostic approaches for CRC can be divided into visual detection methods and laboratory methods. Colonoscopy, sigmoidoscopy, and computed tomography colonography are considered visual methods widely used in cancer detection. Although visual methods provide some benefits, some disadvantages such as late detection, are present, making them useless in rapidly progressing CRC patients. On the other hand, laboratory tests are developed to compensate for the disadvantages of visual methods. More recent progression in laboratory tests makes them able to superfine detection of CRC. For instance, molecular and genetic methods based on the components of cancer cells, like nucleic acid and proteins, can prognosticate further cancer development in susceptible patients. Alongside new therapeutic approaches developed within decades, the number of CRC detection methods has increased, which aims to reduce the duration between cancer initiation and detection. This review sought to survey the CRC detection methods, including conventional and recently-developed methods, to provide better insight into CRC screening.

Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning.

Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be replaced by machine learning approaches. This paper explores the feasibility of using deep reinforcement learning (DRL) in this context, starting with the single-electrode use-case of deep brain stimulation (DBS). We propose a DRL approach based on deep Q-learning where the states represent the electrode trajectory and associated information, and actions are the possible motions. Deep neural networks allow to navigate the complex state space derived from MRI data. The chosen reward function emphasizes safety and accuracy in reaching the target structure. The results were compared with a reference (segmented electrode) and a conventional technique. The DRL approach excelled in navigating the complex anatomy, consistently providing safer and more precise electrode placements than the reference. Compared to conventional techniques, it showed an improvement in accuracy of 2.3% in average proximity to obstacles and 19.4% in average orientation angle. Expectedly, computation times rose significantly, from 2 to 18min. Our investigation into DRL for DBS electrode trajectory planning has showcased its promising potential. Despite only delivering modest accuracy gains compared to traditional methods in the single-electrode case, its relevance for problems with high-dimensional state and action spaces and its resilience against local optima highlight its promising role for complex scenarios. This preliminary study constitutes a first step toward the more challenging problem of multiple-electrodes planning.

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

Microfluidics-mediated Liposomal Nanoparticles for Cancer Therapy: Recent Developments on Advanced Devices and Technologies.

Liposomes, spherical particles with phospholipid double layers, have been extensively studied over the years as a means of drug administration. Conventional manufacturing techniques like thin-film hydration and extrusion have limitations in controlling liposome size and distribution. Microfluidics enables superior tuning of parameters during the self-assembly of liposomes, producing uniform populations. This review summarizes microfluidic methods for engineering liposomes, including hydrodynamic flow focusing, jetting, micro mixing, and double emulsions. The precise control over size and lamellarity afforded by microfluidics has advantages for cancer therapy. Liposomes created through microfluidics and designed to encapsulate chemotherapy drugs have exhibited several advantageous properties in cancer treatment. They showcase enhanced permeability and retention effects, allowing them to accumulate specifically in tumor tissues passively. This passive targeting of tumors results in improved drug delivery and efficacy while reducing systemic toxicity. Promising results have been observed in pancreatic, lung, breast, and ovarian cancer models, making them a potential breakthrough in cancer therapy. Surface-modified liposomes, like antibodies or carbohydrates, also achieve active targeting. Overall, microfluidic fabrication improves reproducibility and scalability compared to traditional methods while maintaining drug loading and biological efficacy. Microfluidics-engineered liposomal formulations hold significant potential to overcome challenges in nanomedicine-based cancer treatment.

Endoscopic Evacuation of Putaminal Hemorrhage Using the Trans-Middle Temporal Gyrus Approach: Technical Notes and Case Presentations.

The development of minimally invasive endoscopic neurosurgery has enabled widespread application of endoscopic surgery via the ipsilateral transfrontal approach for putaminal hematoma evacuation. However, this approach is unsuitable for putaminal hematomas that extend into the temporal lobe. We adopted the endoscopic trans-middle temporal gyrus approach, instead of the conventional surgical approach, for the management of these complicated cases and determined its safety and feasibility. Twenty patients with putaminal hemorrhage underwent surgical treatment at the Shinshu University Hospital between January 2016 and May 2021. Of these, two patients with left putaminal hemorrhage that extended into the temporal lobe underwent surgical treatment using the endoscopic trans-middle temporal gyrus approach. The procedure entailed the use of a thinner transparent sheath to reduce the technique's invasiveness, a navigation system to determine the location of the middle temporal gyrus and the sheath's trajectory, and an endoscope with a 4K camera for higher image quality and utility. The sylvian fissure was compressed superiorly using our novel "port retraction technique" (i.e., by tilting the transparent sheath superiorly) to avoid damage to the middle cerebral artery and Wernicke's area. The endoscopic trans-middle temporal gyrus approach allowed sufficient hematoma evacuation and hemostasis under endoscopic observation without any surgical complexities or complications. The postoperative course was uneventful in both patients. The endoscopic trans-middle temporal gyrus approach for putaminal hematoma evacuation helps avoid damage to normal brain tissue, which could result from the wide range of motion of the conventional technique, particularly when the hemorrhage extends to the temporal lobe.

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.

Dynamic Reconfiguration for Resilient State Estimation Against Cyber Attacks

The increasing complexity and connectivity of critical infrastructures makes it increasingly likely that they will be subject to malicious attacks that compromise operation. Recent studies have shown that these systems are vulnerable to a wide range of cyber-attacks, including False Data Injection (FDI) attacks that bypasses conventional detection techniques. Conventional security monitoring, protection, and control tools are based primarily on passive defense strategies. In this paper, we propose an approach for active defense that improves system security and the detection rate of FDI attacks. The key insight for this approach is that emerging micro-grids can utilize distributed energy resources to dynamically reconfigure the system (e.g. power flow paths), across multiple acceptable configurations. Instead of using information from only a single static configuration to detect FDI attacks, our proposed approach uses <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dynamic reconfiguration</i> to compare measured and estimated states under multiple configurations to accurately detect FDI attacks. We describe an implementation and supporting infrastructure for a secure reconfigurable microgrid. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic reconfiguration</i> also makes it more difficult for attackers to gain knowledge on the system's parameters, which increases the difficulty for attackers to construct hidden attack vectors. We evaluate our approach in the specific scenario of emerging micro-grids. We develop a novel technique for state estimation using multiple configurations and demonstrate that this approach significantly improves FDI detection accuracy.

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.