Current Methods Research Articles

Revealing defect centers in PbWO4 single crystals using thermally stimulated current measurements

The trap centers have a significant impact on the electronic properties of lead tungstate (PbWO4), suggesting their crucial role in optoelectronic applications. In the present study, we investigated and revealed the presence of shallow trap centers in PbWO4 crystals through the utilization of the thermally stimulated current (TSC) method. TSC experiments were performed in the 10–280 K range by applying a constant heating rate. The TSC spectrum showed the presence of a total of four peaks, two of which were overlapped. As a result of analyzing the TSC spectrum using the curve fit method, the activation energies of revealed centers were found as 0.03, 0.11, 0.16, and 0.35 eV. The trapping centers were associated with hole centers according to the comparison of TSC peak intensities recorded by illuminating the opposite polarity contacts. Our findings not only contribute to the fundamental understanding of the charge transport mechanisms in PbWO4 crystals but also hold great promise for enhancing their optoelectronic device performance. The identification and characterization of these shallow trap centers provide valuable insights for optimizing the design and fabrication of future optoelectronic devices based on PbWO4.

Review of the method of Talmud instruction at Yeshivat Maʿalot

The background of the Maʿlot article is the method of Talmud instruction at the Volozyhn Yeshiva and Yeshivat Maʿalot and investigating the claim of the management of Yeshivat Maʿalot, the head of the yeshiva, and the teaching staff that the yeshiva is a direct continuation of the famed Volozhyn Yeshiva that operated in 19th-century Europe. This claim can be examined from many angles, but the aim of the current article is to focus on one major angle common to the entire yeshiva world over the generations in Israel and abroad, that is, the method of Talmud instruction. The research setting is based on historical sources for teaching Talmud in yeshivot, physical attendance during lessons, interviews with teachers and students, and criticism brought by them and by the authors. The research methods include describing, comparing and criticising the method of instruction at Yeshivat Maʿalot. The research results led to a conclusion regarding the question of whether the above claim is justified. The article fits the scope of the journal because it reveals the teaching methods at Yeshivat Maʿalot.Contribution: The contribution of the article lies in its being the first to offer a critique of the Talmud instruction method employed at Yeshivat Maʿalot, with the aim of rethinking the current instruction method and creating a possible avenue for changing it and adapting it as much as possible to the students’ needs and abilities.

Research of ordinal model fusion based apple grading

Near infrared spectrum has been applied for the rapid non-destructive prediction and classification of the internal soluble solids content (SSC) of apples, due to its rapid, non-destructive and non-polluting nature. However, current apple grading methods do not make full use of the orderliness relationship existing in the apple grading labels. Therefore, ordinal model is introduced in the issue of apples grading based on the internal SSC. In details, the orderliness in the classification model is considered and ordinal regression is combined with apple classification model to establish ordered partition neural network and ordinal regression extreme learning machine, respectively. Meanwhile, to address the problems of poor applicability of single prediction model and the grading uncertainty associated with compulsory segmentation of grading boundaries, a Gaussian mass function generating method is proposed based on the distance between the predicted ordinal class labels and the real grading boundaries, and the multiple models can be fused through the Dempster combination rule, making a fuller description of the uncertainty problem on ordinal class labels prediction, providing a research basis for online non destructive testing grading of apples. Finally, the multiple model fusion process is fully demonstrated by simulation examples, the grading accuracy of Yantai Red Fuji apples is also improved than single ordinal model.

Feed Error Prediction and Compensation of CNC Machine Tools Based on Whale Particle Swarm Backpropagation Neural Network

Current modeling methods of machine tool feed error are challenging to meet the demand of high-precision machining when facing complex machining conditions. To enhance the model’s predictive accuracy and the effectiveness of actual compensation, the Whale Particle Swarm Optimization (WPSO) algorithm is proposed to optimize the Backpropagation Neural Network (BPNN). Subsequently, the optimized network incorporates screw elongation and feed position as inputs to establish a feed-error prediction model. Ultimately, the established model was compared with other models and applied to real-time compensation experiments. The research results show that the proposed prediction model outperforms the BPNN model, the particle swarm-optimized BPNN model, and the whale-optimized BPNN model in various indicators. The accuracy of the prediction model was 93.12%, and the errors ranged from −3.80 μm to 4.57 μm with an average error of −0.30 μm. Under different operating conditions, the maximum backward and forward errors are reduced by 33.21% and 87.21%, and the average backward and forward errors are reduced by 57.15% and 84.37%, respectively. The error range is reduced by 67.41%. Beyond elevating prediction accuracy and compensation efficacy, the proposed model offers robust theoretical guidance for practical production.

Current Status of Machine Learning and Artificial Intelligence in Cervical Cancer Screening and Diagnosis: A Systematic Review

Background: Cervical cancer poses a substantial global health challenge, predominantly affecting underprivileged countries. The limitations of current screening methods, such as cytology and visual examination, underscore the need for improved techniques. Artificial intelligence (AI) and machine learning (ML), particularly convolutional neural networks, offer promising solutions in this regard. Methodology: Fifteen studies meeting the inclusion criteria were examined. The PRISMA criteria guided the exploration of cervical cancer screening studies employing AI, ML, and deep learning on PubMed/MEDLINE and Google Scholar. The search focused on "artificial intelligence" and "Pap smear." The investigation specifically delves into English-language studies post-2019 that pertain to the machine learning and deep learning classification of cervical cancer using mobile devices. Histology, animal research, and pre-2019 investigations are excluded. Titles and abstracts were carefully reviewed for any discrepancies and subsequently discussed. The process of data extraction involved compiling information from the selected articles. Result: The systematic review investigates the impact of AI and ML on cervical cancer detection, screening, and diagnosis. Our review reveals enhanced accuracy and efficiency in innovative technologies such as CytoBrain and computer-aided diagnostic systems employing Compact VGG and ResNet101. ML techniques like logistic regression, MLP, SVM, KNN, and naive Bayes prove beneficial for managing complex datasets, particularly when combined with class-balancing procedures. The promising aspects include the application of deep learning for automation and AI-assisted digital microscopy. These findings signify a transformative shift in cervical cancer screening, underscoring the potential of ML and AI technologies to elevate diagnostic accuracy and accessibility. Conclusion: Our study demonstrates advancements in both accuracy and responsiveness. Despite recognizing scientific and ethical considerations, the study underscores the potential of AI to enhance cervical cancer care. This systematic review advocates for policymakers and healthcare practitioners to use ongoing research for informed decision-making in this rapidly evolving field.

A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information

In recent years, the installed capacity of wind power has rapidly increased. And the wind power prediction is the foundation for ensuring large-scale wind power grid connection. The current short-term prediction methods of wind farm cluster (WFC) are difficult to sufficiently extract spatiotemporal features to achieve high-precision prediction. The article proposes a short-term power prediction method for WFC based on deep attention embedded graph clustering-TimesNet (DAEGC-TimesNet). Firstly, the directed power curves of WFC are proposed to analyze wind data. Then, a graph attention network is constructed based on geographic location and numerical weather prediction (NWP) information to guide clustering algorithms to achieve effective cluster partitioning. Finally, the input of model is constructed based on the feature information from various sub-clusters of WFC and the prediction result is obtained through the TimesNet. The method is applied to WFCs in Inner Mongolia, Jilin province and Yunnan province of China. The result shows that the RMSE reduces 0.0155 and the MAE reduces 0.0156 as well as the coefficient of determination and accuracy rate are highest comparing with comparative algorithms averagely based on above three WFCs. The simulation results are superior to the comparison algorithms, which makes greater contributions to ensure regional power supply.

Gaze analysis: A survey on its applications

The examination of ocular movements has a wide range of applications due to the current developments in sensors that are now able to collect this biometric. This type of investigation is known as “gaze analysis”. The gaze has successfully examined a subject's physical and mental status in the past. As a result, over the last few decades, a large and diverse amount of literature on this subject has been generated and presented. The aim of this study is to collect and debate current gaze analysis methods based on their application field. Due to the context-specific needs for performance and efficiency, the eye movements under research are frequently evaluated from completely distinct perspectives. As a result, a collection of data, methods, and discussions ranging from the medical community to virtual and augmented reality, as well as human computer interface and remote learning, has been produced. In addition to providing a peek of novel observation on the issue of gaze analysis, the gaps between and within areas are also discussed to provide points for researchers to pursue.

A Baring Fault Diagnosis Method under Small Sample Conditions Based on the Fractional Order Siamese Deep Residual Shrinkage Network

A bearing fault is one of the major causes of rotating machinery faults. However, in real industrial scenarios, the harsh and complex environment makes it very difficult to collect sufficient fault data. Due to this limitation, most of the current methods cannot accurately identify the fault type in cases with limited data, so timely maintenance cannot be conducted. In order to solve this problem, a bearing fault diagnosis method based on the fractional order Siamese deep residual shrinkage network (FO-SDRSN) is proposed in this paper. After data collection, all kinds of vibration data are first converted into two-dimensional time series feature maps, and these feature maps are divided into the same or different types of fault sample pairs. Then, a Siamese network based on the deep residual shrinkage network (DRSN) is used to extract the features of the fault sample pairs, and the fault type is determined according to the features. After that, the contrastive loss function and diagnostic loss function of the sample pairs are combined, and the network parameters are continuously optimized using the fractional order momentum gradient descent method to reduce the loss function. This improves the accuracy of fault diagnosis with a small sample training dataset. Finally, four small sample datasets are used to verify the effectiveness of the proposed method. The results show that the FO-SDRSN method is superior to other advanced methods in terms of training accuracy and stability under small sample conditions.

Recent Advances in Biosensors for Diagnosis of Autoimmune Diseases.

Over the last decade, autoimmune diseases (ADs) have undergone a significant increase because of genetic and/or environmental factors; therefore, their simple and fast diagnosis is of high importance. The conventional diagnostic techniques for ADs require tedious sample preparation, sophisticated instruments, a dedicated laboratory, and qualified personnel. For these reasons, biosensors could represent a useful alternative to these methods. Biosensors are considered to be promising tools that can be used in clinical analysis for an early diagnosis due to their high sensitivity, simplicity, low cost, possible miniaturization (POCT), and potential ability for real-time analysis. In this review, recently developed biosensors for the detection of autoimmune disease biomarkers are discussed. In the first part, we focus on the main AD biomarkers and the current methods of their detection. Then, we discuss the principles and different types of biosensors. Finally, we overview the characteristics of biosensors based on different bioreceptors reported in the literature.

Quantifying Imaging Agent Binding and Dissociation in 3-D Cancer Spheroid Tissue Culture Using Paired-Agent Principles.

Binding kinetics play an important role in cancer diagnosis and therapeutics. However, current methods of quantifying binding kinetics fail to consider the three-dimensional environment that drugs and imaging agents experience in biological tissue. In response, a methodology to assay agent binding and dissociation in 3-D tissue culture was developed using paired-agent molecular imaging principles. To test the methodology, the uptakes of ABY-029 (an IRDye 800CW-labeled epidermal growth factor receptor (EGFR)-targeted antibody mimetic) and IRDye-700DX carboxylate in 3-D spheroids were measured in four different human cancer cell lines throughout staining and rinsing. A compartment model (optimized for the application) was then fit to the kinetic curves of both imaging agents to estimate binding and dissociation rate constants of the EGFR-targeted ABY-029 agent. A statistically significant correlation was observed between apparent association rate constant (k3) and the receptor concentration experimentally and in simulations (r = 0.99, p < 0.05). A statistically significant difference was found between effective k3 (apparent rate constant of ABY-029 binding to EGFR) values for cell lines with varying levels of EGFR expression (p < 0.05), with no significant difference found between cell lines and controls for other fit parameters. Additionally, a similar binding affinity profile compared to a gold standard method was determined by this model. This low-cost methodology to quantify imaging agent or drug binding affinity in clinically relevant 3-D tumor spheroid models can be used to guide timing of imaging in molecular guided surgery and could have implications in drug development.

Knowledge graph reasoning for cyber attack detection

AbstractIn today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.

Test mass charge estimation for the space inertial sensor with extended Kalman filter

Charge Management System (CMS), aimed at mitigating charge-induced noise on an isolated free-falling test mass (TM), is a crucial component of space inertial sensors in various spaceborne gravitational missions. The estimation of TM charge is one of the tasks of CMS, directly impacting CMS performance. However, current methods for TM charge estimation suffer from slow response and significant estimation errors. This paper presents a new charge estimation method that combines the force modulation principle with an extended Kalman filter (EKF). We analyzed the relationship between the TM surface potential and TM motion, then established a mathematical model. The estimation of TM charge is accomplished through the EKF algorithm. A charge estimation simulation model was developed in Simulink, and experiments were conducted to evaluate the performance of the charge estimation methods under varying charge conditions. The experimental results demonstrate that the EKF method obtains more accurate estimation results and faster convergence rates, compared with Kalman Filter (KF) and Quadrature demodulation (QD) methods. The KF and QD methods show larger errors when the estimated charge is greater than 106e order, meanwhile the QD method exhibits slower convergence rates. The presented method is expected to be beneficial for the CMS of on-orbit inertial sensors.

Comfort Prediction Method for Wearable Devices: Current Progress and Future Direction

Falls constitute a significant health risk, particularly among the elderly, thus prompting the introduction of various wearable devices capable of fall detection. However, the majority of these devices prioritize accuracy over wearer comfort, which significantly influences user adherence and, by extension, the broader development of wearable technologies. Addressing this oversight, this review first summarizes the current methods for predicting the comfort of wearable devices, evaluating them in terms of feasibility and accuracy, reliability and effectiveness, as well as safety and privacy. Subsequently, building upon the evaluation of existing methods, this review proposes a predictive solution based on the XGBoost algorithm.

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models.

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

In Pursuit for Legitimacy: An Overview of Contemporary Policing In Indonesia

The Indonesian National Police (Polri) is currently placing a fair amount of concern on the state of its police legitimacy. However, numerous evidence of public response to date indicated some problems that threaten the legitimacy of Polri and might affect their performance. Following the public response, Polri launched several significant alterations to its policing methods. Hence, several critics questioned the effectiveness of Polri’s current method. This study will examine the pros and cons of the new policing scheme taken by Polri based on literature review of previous studies. Some suggestions as an alternative method are also provided in this study to improve Polri’s approach. Implications of these findings for police practitioners and researchers are then discussed.

HATF: Multi-Modal Feature Learning for Infrared and Visible Image Fusion via Hybrid Attention Transformer

Current CNN-based methods for infrared and visible image fusion are limited by the low discrimination of extracted structural features, the adoption of uniform loss functions, and the lack of inter-modal feature interaction, which make it difficult to obtain optimal fusion results. To alleviate the above problems, a framework for multimodal feature learning fusion using a cross-attention Transformer is proposed. To extract rich structural features at different scales, residual U-Nets with mixed receptive fields are adopted to capture salient object information at various granularities. Then, a hybrid attention fusion strategy is employed to integrate the complementing information from the input images. Finally, adaptive loss functions are designed to achieve optimal fusion results for different modal features. The fusion framework proposed in this study is thoroughly evaluated using the TNO, FLIR, and LLVIP datasets, encompassing diverse scenes and varying illumination conditions. In the comparative experiments, HATF achieved competitive results on three datasets, with EN, SD, MI, and SSIM metrics reaching the best performance on the TNO dataset, surpassing the second-best method by 2.3%, 18.8%, 4.2%, and 2.2%, respectively. These results validate the effectiveness of the proposed method in terms of both robustness and image fusion quality compared to several popular methods.

Enhanced Modeling for Analysis of Fine Particulate Interactions with Coated Surfaces

The adhesion of Martian surface dust to solar panels has been a longstanding challenge for Mars mission researchers. Anti-dust coatings have been developed to deter the adsorption of dust particles onto these solar panels. However, current ground testing methods struggle to accurately verify and assess the coating’s ability to inhibit dust particle adsorption. Consequently, this paper introduces a computational model capable of calculating the contact force between fine particles and the coated surface. This model, grounded in the classical adhesive elastic interactions paradigm, establishes a normal force solution by approximating the pressure distribution on the contact area between the sphere and the coating, subsequently computing the composite force acting on the particle. Utilizing the API module of the EDEM simulation platform, we conducted simulations of the motion of fine particles on both smooth and coated plates. The results reveal that van der Waals forces are more prominent for light-mass particles, and the application of the coating can diminish the pull of these forces, facilitating easier detachment of dust particles from the plate surface.

Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates

Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.

Melting-salt crystallization assisted controllable synthesis of core-shell zeolite catalyst for effective coupling of microwave hotspot and catalytic active site

The applications of zeolites to microwave(MW)-assisted heterogeneous reactions are limited by their MW-transparent properties, which call for essential development of core–shell catalysts with MW-absorbing cores to utilize MW hotspots efficiently. However, current preparation methods fail to address the dilemmas of high heat transfer resistance and low heat generation efficiency caused by excessively thick zeolite shells and insufficient core filling. This study proposes an innovative ship-in-bottle strategy based on melting-salt crystallization for achieving controlled preparation of core–shell zeolite catalysts with ultra-thin shells and large filling rate of MW-absorbing cores. Based on the exploration of optimal conditions, ultra-thin hollow mesoporous zeolite shells around 30 nm thickness are successfully prepared, effectively reducing the heat transfer resistance from cores to catalytic sites. Subsequently, precise adjustment of the shell mesopore size enabled quantitative filling of MW-absorbing cores and therefore successful preparation of β-MnO2@zeolite catalysts with significantly enhanced heat generation capabilities. Compared to conventional surface-loaded catalysts, the new core–shell catalysts improve toluene conversion by 44 % and paraxylene selectivity by 66 % in MW-assisted alkylation reactions. Fine numerical simulations of the catalyst heating process demonstrate that the excellent catalytic performance of core–shell catalysts is due to the effective coupling of MW hotspots and catalytic sites.

Microwave-based removal of polyurethane and epoxy floor coatings

A new debonding technique based on microwave stimulation is being investigated in epoxy and polyurethane floor coatings by combining specific microwave absorbers with temperature-induced blowing agents. Current methods, including mechanical processes such as grinding, typically produce materials with multiple fractions, impeding recycling and the establishment of a circular economy. The microwave method facilitates mono-fraction materials and enhances accessibility to commonly used recycling techniques, particularly in solvolytic processes such as polyurethane recycling. Consequently, a standard two-layer coating system is used, consisting of an epoxy primer and a polyurethane overlay. Various microwave absorbers, such as carbon black and carbon fibres, as well as blowing agents based on thermally expanding particles are investigated. The influence of the additives on the thermo-mechanical properties is studied to identify suitable combinations. The modified materials are later evaluated for their mechanical properties. Two microwave antennas, operating at a frequency of 2.45 GHz and power of 1 kW, are used and compared to activate the modified coating systems. The technology has been successfully integrated into the formulation to allow mono-fraction recovery of the materials. No significant negative effects on material quality were observed. However, due to the extended processing time required for the debonding, further optimisation is necessary. Nevertheless, the feasibility of utilising a laboratory-based microwave approach has been substantiated.