Attractive Field Research Articles

Stage-based colorectal cancer prediction on uncertain dataset using rough computing and LSTM models

Artificial intelligence (AI) is an attractive field of Computer Science that helps to classify and to predict various real-time applications. Perhaps AI has a major role in predicting diseases at an early stage based on history. As cancer is one of the most harmful diseases where the mortality rate is high, it is now essential to utilize the benefits of AI to have an early diagnosis of cancer. Among various cancers, Colorectal cancer (CRC) is a common form of gastrointestinal cancer, and its treatment is lengthy and costly, with a high recurrence rate and high fatality rate. Initial disease analysis and prognosis are required to improve the patient’s treatment with a better survival analysis. However, the disease prediction process depends on the collected data, where the data may contain uncertainty. Uncertain data leads to wrong predictions. Thus, it is essential to utilize rough computing, a mathematical tool to deal with uncertainty. This paper has made an effort, to handle uncertainty using a rough set of fuzzy approximation space as pre-processing and utilized Unidirectional and Bidirectional LSTM for the classification and prediction process. Thus, to demonstrate improved predictive accuracy, the proposed model adapted the optimizers and evaluated using benchmarking techniques in predicting stage-based survival rate. The comparative analysis shows that the proposed model performs well against the state-of-the-art models and can help the medical practitioner to detect CRC at an early stage and reduce the mortality rate among human beings.

Spintronics in GaN‐Based Semiconductors: Research Progress, Challenges and Perspectives

AbstractSpintronics, exploiting the spin degree of electrons as the information vector, is an attractive field for implementing the beyond‐complementary metal‐oxide‐semiconductor (CMOS) devices. GaN‐based semiconductors, characterized by weak spin‐orbit coupling, long spin relaxation time, and Curie temperature higher than room temperature, are considered ideal materials for advancing spintronics. In addition, GaN‐based semiconductors possess a variety of heterostructures, and different properties can be combined through energy band engineering, this enables addressing the limitation of GaN‐based semiconductors. Nevertheless, there are still challenges in practical applications; for instance, the mechanism of spin relaxation in GaN‐based semiconductors is still unclear, and efficient spin gating has not yet been realized. This review examines the progress of spintronics in GaN‐based semiconductors, and systematically summarizes the advancements in spin injection, transport, manipulation, and device application. The current challenges and future perspectives on the studies of spintronic devices based on GaN‐based semiconductors are also highlighted.

Path Planning for Mobile Robots Based on the Improved DAPF-QRRT* Strategy

The rapidly exploring random tree star (RRT*) algorithm is widely used to solve path planning problems. However, the RRT* algorithm and its variants fall short of achieving a balanced consideration of path quality and safety. To address this issue, an improved discretized artificial potential field-QRRT* (IDAPF-QRRT*) path planning strategy is introduced. Initially, the APF method is integrated into the Quick-RRT* (Q-RRT*) algorithm, utilizing the attraction of goal points and the repulsion of obstacles to optimize the tree expansion process, swiftly achieving superior initial solutions. Subsequently, a triangle inequality-based path reconnection mechanism is introduced to create and reconnect path points, optimize the path length, and accelerate the generation of sub-optimal paths. Finally, by refining the traditional APF method, a repulsive orthogonal vector field is obtained, achieving the orthogonalization between repulsive and attractive vector fields. This places key path points within the optimized vector field and adjusts their positions, thereby enhancing path safety. Compared to the Q-RRT* algorithm, the DPF-QRRT* algorithm achieves a 37.66% reduction in the time taken to achieve 1.05 times the optimal solution, and the IDAPF-QRRT* strategy nearly doubles generated path safety.

Skyrmion dynamics in attractive and repulsive local magnetic fields

The study of the behavior of magnetic skyrmions in local magnetic fields’ nanometric length-scale has gained increasing interest in recent years due to the theoretical proposal of magnetic skyrmion–superconducting vortex pairs as potential hosts for topologically protected bound states, which hold high promise for applications in quantum computing. From a magnetic interaction point-of-view, the key interest lies in understanding the skyrmion dynamics triggered by the magnetic energy landscape generated by the superconducting vortex. Here, we present a micromagnetic study of the dynamics of nanometric skyrmions inside a Gaussian magnetic field profile, which is used as a simplified version of the vortex magnetic flux. On the one hand, our calculations show that local non-linear magnetic fields can be very effective in controlling the dynamics of magnetic skyrmions; in particular, they offer the appealing possibility to manipulate skyrmions in a two dimensional space. On the other hand, they also show that the dynamics of a skyrmion in a local magnetic field can be manipulated via a uniform external magnetic field without any change in the magnetic field gradient. An analytical expression for the skyrmion velocity is given, and the corresponding microscopic dynamics are confirmed by the micromagnetic simulations. This work is expected to motivate more theoretical and experimental studies of the behavior of magnetic skyrmions in proximity to superconducting vortices.

Peranan medan magnet dan campuran etanol-biodiesel minyak jelantah pada pembakaran droplet terhadap perilaku api dan emisi gas buang

Petroleum reserves are increasingly depleting, causing a scarcity of petroleum fuel caused by the rapid progress of transportation and the manufacturing industry. This condition forces researchers to search for and develop new renewable energy source. The purpose of research is to understanding and determines role of north-south (U-S) and south-south (S-S) magnetic fields to blend waste cooking oil biodiesel-ethanol on flame evolution, flue gas emissions, and temperature during droplet combustion. Waste cooking oil biodiesel and ethanol were used in this research by adding variations in the direction of repulsive and attractive magnetic fields with an intensity of 11000 gauss. Diameter of droplets tested was 0.3 mm and was placed on a type K thermocouple wire with diameter of 0.1 mm. This research found the role of attractive magnetic field (U-S) in blend waste cooking oil biodiesel-ethanol 20% to produce the shortest flame evolution of 704 ms, lowest CO of 165 ppm, and highest temperature of 828.5 oC. This happens because ethanol has a low flash point and large oxygen content, causing the combustion reaction to occur rapidly. The attractive of magnetic field (U-S) plays a role on attracting oxygen around flame to enter combustion reaction, while the H2O resulting from combustion is pumped out of flame.

A Novel Parameter-Variabled and Coupled Chaotic System and Its Application in Image Encryption with Plaintext-Related Key Concealment.

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance.

Propagation of wave insights to the Chiral Schrödinger equation along with bifurcation analysis and diverse optical soliton solutions

In this study, the modified Sardar sub-equation method is capitalised to secure soliton solutions to the (1+1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(1+1)$$\\end{document}-dimensional chiral nonlinear Schrödinger (NLS) equation. Chiral soliton propagation in nuclear physics is an extremely attractive field because of its wide applications in communications and ultrafast signal routing systems. Additionally, we perform bifurcation analysis to gain a deeper understanding of the dynamics of the chiral NLS equation. This highlights the complex behaviour of the system and exposes the conditions under which various types of bifurcations occur. Additionally, a sensitivity analysis is performed to assess how small changes in initial conditions and parameters influence the solutions, offering valuable perspectives on the stability and dependability of the acquired solutions. By employing the above-mentioned methodology, we derive a variety of exact solutions, including periodic, singular, dark, bright, mixed trigonometric, exponential, hyperbolic, and rational wave solutions. The study’s findings advance our theoretical knowledge of chiral NLS equations and have potential applications in optical communication and related fields.

Path Planning Method and Control of Mobile Robot with Uncertain Dynamics Based on Improved Artificial Potential Field and Its Application in Health Monitoring

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments.

Car-following model based on artificial potential field with consideration of horizontal curvature in connected vehicles environment

Connected vehicles (CVs) will gradually replace traditional vehicles to become the main components of traffic flow. Studying the car-following behavior characteristics is crucial for improving traffic flow stability and safety in CVs environment. Additionally, the radius of road curvature significantly impacts vehicle driving behavior, making it necessary to consider it for the car-following models of CVs. The artificial potential field (APF) theory can more accurately and comprehensively depict various microscopic driving behaviors, offering a new approach for modeling vehicle microscopic behavior. Firstly, this paper constructs the attractive and repulsive potential fields considering horizontal curve curvature based on a road coordinate transformation model. Secondly, an Artificial Potential Field-Based Car-Following Model Considering Curvature (APFCCM) in connected vehicles environment is proposed. Finally, the model is calibrated and validated using the Hangzhou - Xifu Freeway dataset from the Tongji Road Trajectory Sharing (TJRD TS) platform, and compared with the full velocity difference model(FVDM), the Intelligent Driver Model (IDM) and the Driving Risk Potential Field Model (DRPFM). The results show that the APFCCM performs well in trajectory simulation, model accuracy, and scenario adaptability, and it has the lowest mean absolute error(MAE) and root mean square error(RMSE) in position, speed, and acceleration metrics.

Layered double hydroxide assembled on a metal–organic framework coupled with polyaniline as pseudocapacitive anode for phosphorus capture

Excessive phosphorus is detrimental to supporting the proper function of aquatic ecosystems. Capacitive deionization (CDI) is an emerging, highly efficient, eco-friendly electrochemical technology that eliminates phosphorus from wastewater. In this work, a novel LDH@MOF/PANI composite was successfully fabricated by utilizing polyaniline (PANI) as a conductive substrate and metal–organic framework (MOF) as a self-sacrificial template for layered double hydroxide (LDH). The results demonstrated that LDH and PANI are favorable for improving the pore structure features, enriching the phosphorus binding sites, and strengthening the electrochemical performance of the electrode materials. Accordingly, the LDH@MOF/PANI electrode presented a superior phosphorus capture capacity than LDH@MOF and MOF. The isotherm and kinetic experiments revealed that the phosphorus capture by LDH@MOF/PANI is a chemisorption-dominated process associated with heterogeneous adsorption. The potential interference with phosphorus uptake was also assessed by focusing on several vital factors. Eventually, the possible mechanism suggested that ligand exchange, anion exchange, hydrogen bond, electrostatic attraction, and electric field assistance participate in the phosphorus uptake process. This work presents promising insight into the assembly design and rational construction of LDH-based pseudocapacitive anode for phosphorus capture.

Kyrtos: A methodology for automatic deep analysis of graphic charts with curves in technical documents

Deep Understanding of Technical Documents (DUTD) has become a very attractive field with great potential due to large amounts of accumulated documents and the valuable knowledge contained in them. In addition, the holistic understanding of technical documents depends on the accurate analysis of its particular modalities, such as graphics, tables, diagrams, text, etc. and their associations. In this paper, we introduce the Kyrtos methodology for the automatic recognition and analysis of charts with curves in graphics images of technical documents. The recognition processing part adopts a clustering based approach to recognize middle-points that delimit the line-segments that construct the illustrated curves. The analysis processing part parses the extracted line-segments of curves to capture behavioral features such as direction, trend and etc. These associations assist the conversion of recognized segments’ relations into attributed graphs, for the preservation of the curves’ structural characteristics. The graph relations are also are expressed into natural language (NL) text sentences, enriching the document’s text and facilitating their conversion into Stochastic Petri-net (SPN) graphs, which depict the internal functionality represented in the chart image. Extensive evaluation results demonstrate the accuracy of Kyrtos’ recognition and analysis methods by measuring the structural similarity between input chart curves and the approximations generated by Kyrtos for charts with multiple functions.

Time series features and fuzzy memberships combination for time series classification

Time series classification is an increasingly attractive field with the appearance of new problems in an expanding digitalized world. Most of the proposals in the state-of-the-art have focused just on improving the results’ performance, leaving interpretability on a secondary level. The available interpretable proposals do not provide competitive results, which is an issue to be addressed. This paper introduces a new fuzzy feature-based time series classification method, which joins the ability of time series features to capture essential information about the time series with Fuzzy logic. This proposal allows the fuzzy-based approach to incorporate global information about the behavior of time series in the membership calculation with the aim of improving the performance and interpretability of the results by using an interpretable classifier. The proposed method has been evaluated over the 112 state-of-the-art time series classification datasets from the UCR repository, and the results obtained show a better performance. Furthermore, the combination of time series features and fuzzy memberships has also increased the interpretability of final models.

Glutamate, Gangliosides, and the Synapse: Electrostatics at Work in the Brain.

The synapse is a piece of information transfer machinery replacing the electrical conduction of nerve impulses at the end of the neuron. Like many biological mechanisms, its functioning is heavily affected by time constraints. The solution selected by evolution is based on chemical communication that, in theory, cannot compete with the speed of nerve conduction. Nevertheless, biochemical and biophysical compensation mechanisms mitigate this intrinsic weakness: (i) through the high concentrations of neurotransmitters inside the synaptic vesicles; (ii) through the concentration of neurotransmitter receptors in lipid rafts, which are signaling platforms; indeed, the presence of raft lipids, such as gangliosides and cholesterol, allows a fine tuning of synaptic receptors by these lipids; (iii) through the negative electrical charges of the gangliosides, which generate an attractive (for cationic neurotransmitters, such as serotonin) or repulsive (for anionic neurotransmitters, such as glutamate) electric field. This electric field controls the flow of glutamate in the tripartite synapse involving pre- and post-synaptic neurons and the astrocyte. Changes in the expression of brain gangliosides can disrupt the functioning of the glutamatergic synapse, causing fatal diseases, such as Rett syndrome. In this review, we propose an in-depth analysis of the role of gangliosides in the glutamatergic synapse, highlighting the primordial and generally overlooked role played by the electric field of synaptic gangliosides.

Kinematika Enam Derajat Kebebasan Pada Robot Bawah Air Otonom Menggunakan Pendekatan Analisa Geometri

Along with the expansion of Indonesian waters, maritime technology and robotics have grown significantly, making it an attractive field of technology. Autonomous Underwater Vehicle (AUV) is an autonomous underwater robot that can operate and maneuver independently based on given commands. AUVs are used in a variety of applications such as environmental monitoring, underwater infrastructure maintenance, and scientific research. To optimize AUV movement, this research develops and applies kinematics with six degrees of freedom (6-DOF) movement, namely surge, heave, sway, pitch, roll and yaw. The focus of this research is on the HYDROShips robot from the PPNS robotics team which uses 7 thruster motors to control movement in two types of kinematics: horizontal and vertical. A geometric approach is used to integrate translational and rotational movements, simplify the input given to the robot, and maximize the output on each motor. The research results show that this approach improves the efficiency and stability of the HYDROShips robot's movements, enabling the navigation of complex underwater routes with high precision. This research is expected to make a significant contribution to the development of autonomous underwater robots and improve the operational efficiency and effectiveness of AUVs in various applications.

Guidelines for biomimetic 3D/4D printing in drug delivery

Four-dimensional printing, sometimes known as 4DP, is becoming an increasingly popular area of study. Utilization of “shrewd” materials in 3D printing considers the making of items that answer non-mechanical boosts (like dampness, electric or attractive fields, UV, temperature, pH, or particle arrangement) by progressively changing their shape after some time, as modified. These things could be printed with a shape-moving component that is constrained by a PC. Gadgets made with 4D printing innovation exploit time as the final aspect in their activity. 4D shrewd designs have been concentrated on in the logical writing for a really long time, much before the coming of 3D printing. Medicine delivery at the nano-, miniature, and full-scale scales have all been investigated utilizing ideas from shape development and self-gathering. This study lays forward a far-reaching set of rules for utilizing biomimetic 3D/4D printing techniques in drug delivery frameworks. Biomimetic 3D/4D printing has arisen as a game-changing method in the field of medicine delivery, considering unmatched accuracy and command over drug discharge qualities. In particular, this study looks at the manners by which 3D and 4D printing are being utilized as prescription delivery techniques and with regards to customized medication in the pharmaceutical business.

Epigenetics: Unraveling Hidden Phenotypic Diversity in Livestock

The missing link between nature and nurture i.e. epigenetics is the process of turning genes on and off or can be explained as a phenomenon that includes all the process that affects phenotypic expressions without causing any alteration in the genetic sequence. Epigenetic regulation can be done by post-translational modification of histones but is also linked to the regulation of gene expression by non-coding RNAs, genome instabilities or any other force that could modify a phenotype. Epigenomic patterns in various tissues reveal the regulatory functions of epigenetic mechanisms in livestock growth and health. DNA methylation modifications can explain poor offspring performance due to maternal stressors like metabolic disorder, heat stress and negative energy balance. It is an attractive field for livestock breeding, as it can help identify missing causality and heritability of traits and diseases. Certain methylation patterns are more likely to increase in specific environments, explaining phenotypic diversity between individuals. Accurate recording of the environment and epigenomes can provide more accurate and precise calculations of phenotypic variance. Methods to estimate the contribution of epigenetics to covariance between relatives have been developed which can be beneficial for obtaining favorable methylation patterns that impact disease resistance and economic traits.

Traveling around the Sun: A Project to Study Planetary Motion in an Attractive Central Force Field with the help of MATHEMATICA

The present work focuses on the creation of an interactive environment of active learning of an exploratory type, by exploiting the capabilities of the MATHEMATICA software, for the study of the planetary motion around the central star. Through the software environment, students are given the opportunity to experiment by implementing interdisciplinary projects in the fields of Physics, Mathematics and Informatics. The results of the pilot research point to the understanding of the cognitive subject by the learners, as well as to the strengthening of skills, such as communication, collaboration, problem solving, critical thinking, reflection and creativity, showing the direction of the main research that follows.

Iron-catalysed nitrene transfer reaction to form C[sbnd]N bonds

Direct CN bond forming reaction by utilizing the non-precious transition metals is an attractive field of research in organic synthesis. Unlike the conventional use of precious metals like rhodium, palladium, ruthenium, and iridium that are commonly used in these reactions, the use of non-precious metals, particularly those are non-toxic and high abundant, holds distinct attention owing to the increasing demand of sustainable chemical processes. Among them, iron catalysis offers notable advantages for sustainable chemical synthesis. Iron-catalysed nitrene transfer reactions have emerged as robust and eco-friendly methods for constructing various valuable CN bonds. In this review, we provide a comprehensive overview of the advancements in iron catalysis for the nitrene transfer reactions aimed at synthesizing diverse CN bonds.

Switchable dual ultra-broadband perfect absorber based on graphene and vanadium dioxide hybrid metamaterials

The phase transition property of vanadium dioxide (VO2) makes it an attractive field in temperature-controlled chips. In this paper, a microstructure based on a graphene disk and a ring-shaped VO2 hybrid metamaterial is proposed to achieve switchable dual ultra-broadband perfect absorption in the terahertz region, which is analyzed by the phase transition property of VO2 and the dynamic electrical regulation of graphene. When the graphene disk is not present, the absorption intensity can reach up to 97.07%. When the graphene disk is present and respectively interacts with the VO2 metallic and insulated phases, the results exhibit an ultra-broadband perfect absorption (>90%) from 1.620 THz to 4.533 THz and from 1.506 THz to 3.576 THz, respectively, where the bandwidths are as high as 2.913 THz and 2.070 THz, respectively. Adjusting the Fermi level of graphene and the VO2 conductivity allows the absorption intensity and bandwidth to be effectively controlled, where the fractional bandwidth from 81.46% to 94.69% and a high modulation depth of 95.09% can be achieved. These results suggest that dual ultra-broad perfect absorption can be dynamically switched within a single absorber and has various modulation means, which are expected to be developed in applying multifunctional modulators.

Visible Light-promoted Preparation of 2,3-Dihydrobenzofurans and Coumaranones

Abstract: 2,3-Dihydrobenzofuran and coumaranone are readily available in numerous naturally occurring compounds. They mostly exist in plenty of food plants and medicinal plants. Such compounds constitute a series of flavor components and bioactive molecules. Their preparation has been an attractive field of research. In the past few decades, great efforts have been made in the preparation of the 2,3-dihydrobenzofuran structure through both metal-catalyzed and organocatalyzed ways. Visible light-promoted reactions sprang up in the early 21st century and represent a green manner of transformations. Under the irradiation of visible light, radicals could be generated under milder conditions. Thus, visible lightpromoted reactions spread widely in the field of chemical synthesis. In recent years, visible light-promoted preparation of 2,3-dihydrobenzofuran and coumaranone has been developed by different groups, including both intramolecular and intermolecular reactions. The benign reaction conditions allow better functional group-tolerance and lead to diverse structures. Several reviews on the synthesis of 2,3- dihydrobenzofuran have been reported. However, visible light-promoted approaches to such structures have not been well reviewed. Our review will cover the literature that has been reported on the discovery of 2,3- dihydrobenzofuran in food and visible light-promoted preparation of 2,3-dihydrobenzofuran, attempting to summarize the existing methods and provide guidance to the chemists on the present challenges.