Application Fields Research Articles

ОБОСНОВАНИЕ ТИПА ПРИВОДА ЛЕНТОЧНЫХ КОНВЕЙЕРОВ ИСПОЛЬЗУЕМЫХ ПРИ ПОГРУЗКЕ ВАГОНОВ

The drive types of modern belt conveyors used for loading cars are analyzed in order to assess their efficiency and reliability. Industrially produced Belt conveyors produced in industry have significant differences in their fields of application and the limitations imposed on them, which leads to differences in configurations and technical characteristics. When analyzing drives of belt conveyors, it is necessary to take into account many factors, including the types of mechanisms used, as well as their design features, productivity, efficiency, power consumption, and others. The analysis is based on data provided by manufacturers of belt conveyors in open sources. The paper considers both domestic and foreign manufacturers of belt conveyors used for loading cars.

IRIS RECOGNITION: BIOMETRIC AUTHENTICATION FOR COCKPIT DOOR SAFETY

A face recognition system is one of the biometric types of information process. Its applicability is easier, and the working range is larger than other fingerprint, passcode, and signature. The face recognition system also enhances and improves security and privacy to a great extent, leading towards a better and modernized future. The system uses a combination of two techniques: face detection and recognition. The face detection is performed on live acquired images without any application field in mind [1,2]. The processes utilized in the system are white balance correction, skin-like region segmentation, facial feature extraction, and face image extraction on a candidate. The system is also capable of detecting and recognizing multiple faces in live acquired images. A face recognition system makes the process easier and minimizes the issues related to security [3]. Keywords: Cockpit, Iris Recognition, Smart Door, Biometric Identification, Security

Bluetooth Controlled Agrisense for Agricultural Operations

Due to high demand for sustainable agricultural practice, this project presents Bluetooth operated mobile AgriSense for irrigation and grass cutting. Equipped with Arduino Uno, Bluetooth, DC motors, and solar lithium-ion batteries, AgriSense works via an Android application; the mobile application controls water dispensation and field upkeep. This is in combination with a servo- controlled sprinkler that offers the best control of water distribution hence efficiency in the supplied water for irrigation purposes. Also, these solar panels being efficient, produce light energy, corresponding to the environment conservation acts. For optimal functionality, the proposed AgriSense comprises of individual power, control, mobility and irrigation sections. Each of the modules improves its function and flexibility in response to agricultural requirements, which makes it possible to incorporate more features into the system if necessary. Even the AgriSense implements other strategies like LoRa-based communication coverage for larger range or track for rough surface, which further helps to customize the flexible and adaptive capability of AgriSense to fulfill the roles of various farming fields. This project represents a pioneering strategy to incorporate robotics into change in sustainable farming to cope with the coming issues in water and labor scarcity in the farming industry.

Bibliometric Study of fMRI in Epilepsy Research based on Citespace in China

Objective: To conduct a visualized analysis of Chinese literature on applying functional magnetic resonance imaging (fMRI) in epilepsy research from 2010 to 2024 using CiteSpace software, revealing its research status, trends, and hotspots. Materials and Methods: Relevant literature published between 2010 and 2024 was retrieved from the China National Knowledge Infrastructure (CNKI) database. Bibliometric analysis and visualization were performed using Excel and CiteSpace. Results: A total of 255 articles were included. There were numerous author collaborations but limited inter-team cooperation. Institutional collaborations were also scarce. Prominent keywords included "epilepsy," "magnetic resonance imaging," "functional magnetic resonance imaging," and "temporal lobe epilepsy." Nine keyword clusters were identified, including "independent component analysis," "graph theory," "low-frequency amplitude," "magnetic resonance imaging," "working memory," "tuberous sclerosis," "resting-state fMRI," "brain dysfunction," and "focal epilepsy." Conclusion: The main research hotspots in the application of fMRI in epilepsy include "independent component analysis," "graph theory," "low-frequency amplitude," "magnetic resonance imaging," "working memory," "tuberous sclerosis," "resting-state fMRI," "brain dysfunction," and "focal epilepsy." Enhanced collaboration and integration of current research hotspots and frontiers are necessary to advance the field of fMRI application in epilepsy.

Manipulation of Single Nanowire and its Applications.

Micro/nano manipulation of single nanowire has emerged as a popular direction of study in the field of nanotechnology, with promising applications in cutting-edge technologies such as device manufacturing, medical treatment, and nanorobotics. The synthesis of nanowires with controllable length and diameter makes them meet various micro/nano manipulation demands. As manipulation techniques have advanced, including the use of optical tweezers, electric and magnetic fields, mechanical control, and several more control methods, they have demonstrated unique advantages in different application fields. For instance, the application of micro/nano manipulation of single nanowire in device manufacturing, cell drug precision transport, and nanomotors has demonstrated their potential in device development, biomedicine, and precision manufacturing. However, application extension of single nanowire manipulation is still in its infancy. This review systematically sorts out the progress of nanowire synthesis and manipulation and discusses its current research status and prospects in various application fields. It aims to provide a comprehensive reference and guidance for future research and promote the innovative applications of nanowire manipulation technology in a wide range of fields.

Uncertainty quantification and propagation in surrogate-based Bayesian inference

Surrogate models are statistical or conceptual approximations for more complex simulation models. In this context, it is crucial to propagate the uncertainty induced by limited simulation budget and surrogate approximation error to predictions, inference, and subsequent decision-relevant quantities. However, quantifying and then propagating the uncertainty of surrogates is usually limited to special analytic cases or is otherwise computationally very expensive. In this paper, we propose a framework enabling a scalable, Bayesian approach to surrogate modeling with thorough uncertainty quantification, propagation, and validation. Specifically, we present three methods for Bayesian inference with surrogate models given measurement data. This is a task where the propagation of surrogate uncertainty is especially relevant, because failing to account for it may lead to biased and/or overconfident estimates of the parameters of interest. We showcase our approach in three detailed case studies for linear and nonlinear real-world modeling scenarios. Uncertainty propagation in surrogate models enables more reliable and safe approximation of expensive simulators and will therefore be useful in various fields of applications.

The development of multicolor carbon dots and their applications in the field of anti-counterfeiting.

The development of multicolor carbon dots and their applications in the field of anti-counterfeiting.

Achieving photocatalytic water reduction and oxidation over narrow bandgap FeVO4

The exploration of novel oxide photocatalysts with narrow bandgaps is highly desirable for efficient photocatalytic water splitting. However, this is rather challenging as reducing the bandgap generally leads to severe charge recombination in photocatalysts. To address these issues, the present work demonstrates, for the first time, the synthesis and application of triclinic FeVO4 with an absorption edge of 575 nm for visible-light-driven photocatalytic water reduction and oxidation. Based on it, the Cr doping strategy is implemented on the FeVO4 photocatalyst to further promote the charge separation and the photocatalytic water splitting performance, achieving an apparent quantum efficiency (AQE) of 0.26% at 420 nm (± 15 nm) for an O2 evolution reaction. Detailed analysis shows that an impurity level below the conduction band minimum originating from the Cr 3d orbital is formed after Cr doping, facilitating the prolonged absorption edge and the enhanced charge separation. This work inaugurates the application field of the narrow bandgap particulate FeVO4 photocatalyst in photocatalytic water splitting, and validates that charge separation can be promoted by Cr doping, both of which are promising to be further developed for efficient solar energy conversion.

Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis

Deep-learning-based object detection algorithms play a pivotal role in various domains, including face detection, automatic driving, monitoring security, and industrial production. Compared with the traditional object detection algorithms and the two-stage object detection algorithms, the YOLO (You Only Look Once) series improved the detection speed and accuracy. In addition, the YOLO series of object detection algorithms are widely used in the industrial fields due to their real-time and high-precision characteristics. This work summarizes the main versions of YOLO series algorithms as well as their main improving measures. Furthermore, the following is the analysis of the industrial application fields and some application examples of YOLO series algorithms. Furthermore, this work summarizes the general improvement measures for the industrial applications of the YOLO series algorithms. As for the comparison of these algorithms, this work implements the basic tests for the industrial application performance on different datasets. Finally, the development directions and challenges for YOLO series algorithms are pointed out.

Technological Innovations and Applications of Microbial Protein Production: From Genetic Engineering to Sustainable Manufacturing

Facing global climate change, resource shortages, and the urgent need for carbon neutrality goals, microbial protein production has demonstrated significant potential in the fields of food, pharmaceuticals, and industrial applications [...]

Research Progress on the Enhancement of Immobilized Enzyme Catalytic Performance and Its Application in the Synthesis of Vitamin E Succinate

Vitamin E succinate is a more mature vitamin E derivative, and its chemical stability and many effects have been improved compared with vitamin E, which can not only make up for the shortcomings of vitamin E application but also broaden the application field of vitamin E. At present, in developed countries such as Europe, America, and Japan, vitamin E succinate is widely used in health foods, and due to its good water solubility and stability, the vitamin E added to most nutritional supplements (tablets and hard capsules) is vitamin E succinate. At the same time, vitamin E succinate used in the food and pharmaceutical industries is mainly catalyzed by enzymatic catalysis. In this paper, Candida rugosa lipase (CRL) was studied. Chemical modification and immobilization were used to improve the enzymatic properties of CRL, and immobilized lipase with high stability and high activity was obtained. It was applied to the enzymatic synthesis of vitamin E succinate, and the reaction conditions were optimized to improve the yield and reduce the production cost. The review covered the research progress of the methods for enhancing the catalytic performance of immobilized enzymes and discussed its application in the synthesis of vitamin E succinate, providing new ideas and technical support for the catalytic performance enhancement of immobilized enzymes and its application in the synthesis of vitamin E succinate and promoting the production and application of vitamin E succinate.

Magnetic Actuation Under DC and AC Fields of Thermoplastic Polyurethane With Strontium Hexaferrite

AbstractThe growing field of applications where magnetic actuation is required relies on significant developments in materials science, particularly using polymer matrices to enhance the design and multifunctionality of small‐sized devices through additive manufacturing techniques. Thermoplastic polyurethane (TPU) is widely used for fused deposition modeling 3D printing technology and, as an elastomer, confers the degrees of freedom necessary to develop soft actuators capable of complex shape deformations. Such actuators can be triggered through different stimuli, with magnetic actuation standing out for its capacity to induce fast deformations of remotely actuated systems. Magnetic TPU composites with strontium hexaferrite (SrFe12O19) were produced through a solvent‐free melt‐mixing technique. These composites display a magnetomechanical actuation mechanism, being able to deform (bend and twist) through DC and AC magnetic fields, showcasing their potential as a promising material for the fabrication of future magnetic actuators.

Real estate decision-making: precision in price prediction through advanced machine learning algorithms

Purpose This study aims to enhance real estate price prediction accuracy using advanced machine learning models, minimizing biases and inconsistencies inherent in traditional appraisal methods. By leveraging support vector regression (SVR) and gradient boosting machine (GBM), this study provides a data-driven approach to property valuation, improving decision-making for buyers, sellers and policymakers. This study also seeks to bridge the gap in machine learning applications for emerging markets like Jordan. This study’s research’s broader goal is to offer a transparent, efficient and reliable tool for property valuation that improves market efficiency and reduces transaction uncertainty. Design/methodology/approach This study uses machine learning techniques – SVR and GBM – to predict real estate prices in Amman, Jordan. Data was collected from the Department of Lands and Survey, covering residential property sales from March 2023 to December 2023. The data set underwent preprocessing, including one-hot encoding for categorical variables and logarithmic normalization for skewed data. Hyperparameter tuning was performed using grid search, and an ensemble approach compared multiple algorithms. Performance was evaluated using root mean squared error (RMSE), mean absolute percentage error (MAPE) and MAE. The findings were implemented into a user-friendly “PRICE IT” application for real-world application. Findings The results demonstrate that SVR outperforms GBM in predicting real estate prices, achieving the lowest RMSE (0.31) and MAPE (25%). The most influential factors in price determination are property area, location and apartment type. The study highlights that machine learning models provide superior accuracy compared to traditional appraisal methods. The findings support the integration of data-driven valuation techniques in real estate markets, reducing reliance on subjective human judgment. A user-friendly application was developed to enable nontechnical users to estimate property prices, making the research practical and impactful. Originality/value This study contributes to the growing field of machine learning applications in real estate by demonstrating the effectiveness of SVR and GBM in an emerging market context. Unlike previous research, it focuses on Amman, Jordan, where limited studies have explored advanced machine-learning models for price prediction. The study offers a practical, user-friendly valuation tool that real estate stakeholders can widely adopt. This research enhances decision-making and market efficiency by providing a transparent and objective alternative to traditional appraisal methods.

Application of machine learning in higher education to predict students’ performance, learning engagement and self-efficacy: a systematic literature review

PurposeIn recent years, studies have shown that machine learning significantly improves student performance and retention and reduces the risk of student dropout and withdrawal. However, there is a lack of empirical research reviews focusing on the application of machine learning to predict student performance in terms of learning engagement and self-efficacy and exploring their relationships. Hence, this paper conducts a systematic research review on the application of machine learning in higher education from an empirical research perspective.Design/methodology/approachThis systematic review examines the application of machine learning (ML) in higher education, focusing on predicting student performance, engagement and self-efficacy. The review covers empirical studies from 2016 to 2024, utilizing a PRISMA framework to select 67 relevant articles from major databases.FindingsThe findings show that ML applications are widely researched and published in high-impact journals. The primary functions of ML in these studies include performance prediction, engagement analysis and self-efficacy assessment, employing various ML algorithms such as decision trees, random forests, support vector machines and neural networks. Ensemble learning algorithms generally outperform single algorithms regarding accuracy and other evaluation metrics. Common model evaluation metrics include accuracy, F1 score, recall and precision, with newer methods also being explored.Research limitations/implicationsFirst, empirical research literature was selected from only four renowned electronic journal databases, and the literature was limited to journal articles, with the latest review literature and conference papers published in the form of conference papers also excluded, which led to empirical research not obtaining the latest views of researchers in interdisciplinary fields. Second, this review focused mainly on the analysis of student grade prediction, learning engagement and self-efficacy and did not study students’ risk, dropout rates, retention rates or learning behaviors, which limited the scope of the literature review and the application field of machine learning algorithms. Finally, this article only conducted a systematic review of the application of machine learning algorithms in higher education and did not establish a metadata list or carry out metadata analysis.Originality/valueThe review highlights ML’s potential to enhance personalized education, early intervention and identifying at-risk students. Future research should improve prediction accuracy, explore new algorithms and address current study limitations, particularly the narrow focus on specific outcomes and lack of interdisciplinary perspectives.

The current state of clinical recommendations development in occupational health

Clinical recommendations (CR) are one of the tools for introducing the principles of evidence-based medicine into healthcare practice, the application of which will allow practitioners to provide high-quality and safe medical care using optimal clinical practices. The special features of clinical recommendations in the field of occupational health are their focus not only on the diagnosis, prevention, treatment and rehabilitation of occupational diseases, but also on highlighting issues related to the organization of medical care, the examination of professional suitability and the connection of the disease with the profession. The analysis of the current state of the development of clinical recommendations on the main nosological forms of occupational diseases is carried out. Clinical recommendations that are at different stages of development are presented, both in the form of independent reviews and as sections on the occupational etiology of various nosological forms in general clinical recommendations. The prospects of creating clinical recommendations in the field of occupational medicine, their approval and further application are presented. The authors noted the main stages of the development and implementation of clinical recommendations, which are the main tool for supporting decision-making and ensuring the proper quality of medical care. Experts have identified the need to develop local regulations in medical organizations that define the procedure for implementing clinical recommendations, algorithms for the actions of medical personnel in the process of their application, protocols for treatment, diagnosis, and rehabilitation, including criteria for the quality of medical care.

دور الحوكمة المؤسسية في تحسين كفاءة الإدارة التربوية في مدارس المرحلة الأساسية من وجهة نظر مديري المدارس

This study aimed to identify the role of institutional governance in its dimensions (participation, transparency, accountability, efficiency, justice) in improving the efficiency of educational administration in primary schools from the point of view of school principals. The descriptive analytical approach was used, as we used the descriptive approach in the theoretical aspect that works to clarify the study variables, while using the analytical approach in the field study because it works to analyze and link the results and interpret them. This study resulted in an increase in the "level of application of institutional governance" in primary schools in the Kingdom of Saudi Arabia from the point of view of school principals, and an increase in the level of dimensions (participation, transparency, accountability, efficiency, justice). This result is consistent with the results of the study (Al-Khawaldeh, 2018), and an increase in the "level of efficiency of educational administration" in primary schools in the Kingdom of Saudi Arabia from the point of view of school principals. The study recommended the need to work to maintain the distinguished level of application of institutional governance in primary schools in the Kingdom of Saudi Arabia by providing all methods and means that contribute to identifying all developments and methods used in the field of governance application.

Road extraction model based on stripe expansion attention module and multi-stage features

ABSTRACT Road extraction from remote sensing imagery is a popular and frontier research focus, since road information plays an essential role in application fields. However, extracting high-quality road information is challenging due to complex backgrounds, occlusions, and interference from similar non-road areas. To address these issues, this study proposes SADNet, an advanced U-Net-based road extraction framework. First, an Attention-enhanced Dilated Strip Convolution Module (AEDSCM) was integrated between the encoder and decoder to improve the network’s feature extraction abilities and multi-scale extraction of road features by combining stripe convolutions and attentional feature fusion mechanism. Additionally, the Semantic and Detail Injection (SDI) module integrates the semantic information and feature details from different levels of features to enrich the feature graph at each level. SADNet applies innovative jump connections to make all levels of features with enhanced semantic features and complex details. The enhanced features are then sent to the decoder for further processing and segmentation. Extensive experiments on two public datasets demonstrate that SADNet achieves IoU improvements of 0.90–4.03% compared to state-of-the-art semantic segmentation methods. These results validate its effectiveness in preserving road details, improving connectivity, and advancing road extraction performance across diverse datasets.

Planning Reliability Assurance Tests for Autonomous Vehicles Based on Disengagement Events Data

Artificial intelligence (AI) technology has become increasingly prevalent and transforms our everyday lives. One important application of AI technology is the development of autonomous vehicles (AVs). However, the reliability of an AV needs to be carefully demonstrated via an assurance test so that the product can be used with confidence in the field. To plan for an assurance test, one needs to determine how many AVs need to be tested for how many miles and the standard for passing the test. Existing research has made great efforts in developing reliability demonstration tests in the other fields of applications for product development and assessment. However, statistical methods have not been utilized in AV test planning. This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data. We explore the relationship between multiple criteria of interest in the context of planning AV reliability assurance tests. Specifically, we develop two test planning strategies based on homogeneous and non-homogeneous Poisson processes while balancing multiple objectives with the Pareto front approach. We also offer recommendations for practical use. The disengagement events data from the California Department of Motor Vehicles AV testing program is used to illustrate the proposed assurance test planning methods.

First Principles Study on The Effect of Single Beryllium and Magnesium Adatom on Germanane Monolayer Surface

Theoretical calculations predict that unlike the planar structure of graphene, the germanene also has stable, two-dimensional (2D), low-buckled, honeycomb structure which make it more interested in the field of optoelectronic applications recently, but the major issue with germanene are larger germanium-germanium (Ge-Ge) interatomic distance and zero energy band gap which become a great research gap. In this study, the effect of alkaline earth metal (AEM) Magnesium (Mg) and Beryllium (Be) adsorption on germanane monolayer within the density functional theory as implemented in Quantum ESPRESSO code has been investigated, Our calculated equilibrium hexagonal lattice constant a and the buckling height δ are found to be 4.047 Å and 0.689 Å respectively, among the chosen adsorptions sites (H, B and T) B side is found to be the most favourable side for both Be and Mg absorptions, due to the less adsorption energy and AEM-Ge distances. For the electronic properties. The Be and Mg adsorptions lead to semiconducting behaviour with direct gap of about 0.206 eV 0.629 eV for Be and Mg adsorption respectively. The obtained results are in agreement with many reported theoretical results.

Broadband frequency-doubling of a swept-source laser from 1550 nm to 775 nm using a fan-out crystal and application in 2kHz LiDAR ranging

Swept-source lasers have achieved significant success in sensing, imaging and microscopy. A special type of these lasers is the Fourier-domain mode-locked (FDML) laser, which operates at sweep rates in the megahertz (MHz) range while maintaining high instantaneous monochromaticity. By combining an FDML with an electro optical modulator (EOM) and master oscillator power amplifier (MOPA), a fast wavelength-swept pulsed laser with pulse peak powers in the kilowatt range can be constructed. An extension from the typical near-infrared (NIR) spectral range into the visible (VIS) range could enable a much wider field of applications. A direct approach is not feasible due to the lack of a suitable amplifier medium for MOPAs. A proven alternative is frequency-doubling through phase matching or quasi-phase matching (QPM) from the NIR. However, efficient frequency-doubling of low to mid-power lasers requires longer crystal lengths, limiting the input spectral bandwidth and hence is typically not feasible for broadband swept-source lasers. To overcome this limitation, here we describe a new approach using a periodically poled lithium niobate (PPLN) crystal with a special fan-out QPM structure. Spatial separation of the spectrally distinct pulses using an optical grating is used to obtain ideal QPM conditions for all wavelengths. This new concept allows broadband frequency-doubling and thus the generation of efficient swept laser sources in the NIR and VIS range. In this study we present the frequency-doubling of a 1550 nm FDML-MOPA laser to 775 nm with a pulse peak power of up to 35 W and a spectral span of 10 nm around 775 nm. We show application in ultrafast time-stretch LiDAR with 3D acquisitions of 2000 scans per second. This new laser technology opens up new possibilities for high speed and high bandwidth imaging and spectroscopy.