Effectiveness Of Method Research Articles

Multisource working condition recognition via nonlinear kernel learning and p-Laplacian manifold learning

Effectively utilizing information from multiple sources and fewer labeled operating condition samples from a sucker-rod pumping system for oil production can improve the recognition effects and engineering practicability. Nevertheless, this is a challenging energy environment scientific application research subject, and therefore, this study proposes an operating state recognition scheme that relies on multisource nonlinear kernel learning and p-Laplacian high-order manifold regularization logistic regress. Specifically, three measured features are selected and extracted, i.e., wellhead temperature signal, electrical power signal, and ground dynamometer cards, based on mechanism analysis, expert experience, and prior knowledge. Finally, we establish the operating condition recognition model to recognize by the multisource p-Laplacian regularization kernel logistic regress algorithm. The experimental data are derived from 60 wells of a common high-pressure and low-permeability thin oil reservoir block of an oil field in China. The corresponding trials highlight that our scheme outperforms traditional recognition methods by exploiting single-source and multiple-feature data. In the context of fewer labeled samples, the proposed method has a greater recognition effect, engineering practicability, and better model robustness than the existing schemes based on other high-order manifold learning, verifying our method's effectiveness.

Heterogeneous ensemble prediction model of CO emission concentration in municipal solid waste incineration process using virtual data and real data hybrid-driven

Carbon monoxide (CO) is a hazardous gas discharged during municipal solid waste incineration (MSWI). Its emission concentration serves as a vital indicator for assessing the stability of the MSWI process. Therefore, accurate prediction of CO emissions is crucial. While existing research predominantly relies on historical real data-driven models, it often overlooks the effective utilization of the combustion mechanism. This article introduced a novel approach: a heterogeneous ensemble prediction model that integrates virtual and real data. Firstly, virtual mechanism data was obtained through a multi-condition mechanism model constructed using coupled numerical simulation software of FLIC and Aspen Plus. Secondly, based on this virtual mechanism data, a linear regression decision tree (LRDT) algorithm was employed to establish the mechanism mapping model. Simultaneously, a real historical data-driven model based on a long short-term memory (LSTM) neural network algorithm was developed. In the offline training verification phase, the heterogeneous models were combined using an inequality-constrained random weighted neural network (CIRWNN) after aligning virtual and real samples representing operating conditions based on the k-nearest neighbor (KNN) approach. Subsequently, in the online testing verification stage, CO online prediction was achieved by ensemble the LRDT-based mechanism mapping model and.the LSTM-based historical data-driven model. The proposed method's effectiveness and rationality were validated through an industrial case study of MSWI process in Beijing.

A Novel Magnetic β-Cyclodextrin-Modified Graphene Oxide and Chitosan Composite as an Adsorbent for Trace Extraction of Four Bisphenol Pollutants from Environmental Water Samples and Food Samples.

In this study, a novel functionalized magnetic composite (MNCGC) for magnetic solid-phase extraction of bisphenols from environmental and food samples was developed, featuring a multistep synthesis with Fe3O4, chitosan, graphene oxide, and β-cyclodextrin, crosslinked by glutaraldehyde. Characterization confirmed its advantageous morphology, intact crystal structure of the magnetic core, specific surface area, and magnetization, enabling efficient adsorption and separation via an external magnetic field. The optimized MSPE-HPLC-FLD method demonstrated excellent sensitivity, linearity, and recovery rates exceeding 80% for bisphenol pollutants, validating the method's effectiveness in enriching and detecting trace levels of bisphenols in complex matrices. This approach offers a new avenue for analyzing multiple bisphenol residues, with successful application to environmental water and food samples, showing high recovery rates.

IoT Based Railway Track Faults Detection and Localization using Acoustic Analysis and Signalling

Ensuring the safety and efficiency of rail transportation is paramount, necessitating timely inspection of railway tracks to prevent potential hazards. This research introduces an innovative approach by implementing an Internet of Things (IoT)-based autonomous railway track fault detection system. Various track components, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels, can lead to hazardous situations if not properly monitored. This master's thesis proposes an efficient method for classifying track components using image processing techniques and deep learning algorithms. Specifically, it focuses on detecting missing fasteners by analyzing images captured by a line camera. Experimental results demonstrate the method's effectiveness and robustness in complex environments. Future work aims to develop a comprehensive model capable of classifying all track components simultaneously. Image processing is utilized to extract individual components such as fasteners, rail, ballast, and sleepers, with the model trained to classify fastener states. This research contributes to advancing railway inspection methods, mitigating operational costs, preventing accidents, and enhancing passenger experience

Using Nonlinear Features and Logistic Regression for Epilepsy Detection with Linear Complexity

Introduction: In this specific research study, a remarkably accurate and significantly simplified approach has been presented.Material and Metods: this research is encompasses three crucial stages. Firstly, the length of the signal is effectively diminished to an optimal magnitude through the utilization of a technique widely known as windowing. This technique plays a pivotal role in reducing the signal to an ideal size, ensuring the subsequent stages are executed with utmost precision. Secondly, the pertinent features are extracted from the shortened signal, specifically focusing on the Fractal Dimension, the Hurst Exponent, and the Ratio of Determinism to Recurrence Rate. These features are chosen due to the inherent nonlinear nature of the signal, as they provide valuable insights into the complex patterns and structures present within. Lastly, the extracted features undergo Logistic Regression, a widely employed classification algorithm, to effectively categorize and classify them. This step plays a crucial role in providing a clear and concise understanding of the underlying characteristics of the signal.Results: The implementation of the proposed method not only achieves an outstanding accuracy rate of 99.66%, but it also exhibits a linear time complexity, ensuring efficient processing. Additionally, this method leads to a significant reduction in the length of EEG signals, which is of utmost importance in practical applications.Conclusion: The primary objective of this proposed method revolves around the introduction of an online approach that can seamlessly integrate into healthcare systems. This objective is derived from a comprehensive analysis and evaluation of the obtained results, ensuring the method's practicality and effectiveness are thoroughly assessed.

Synthesis and characterization of MoS2–COOH/gly/Mn nanocomposite as an efficient adsorbent for Ultra-trace determination of trifluralin herbicide

The world is confronting a severe water crisis. To clean up water from heavy metals, microorganisms, chemicals, and other types of pollutants, nanocomposites have been receiving great attention specifically due to the high surface area affording to work effectually even at low concentrations. In this research, we synthesized a new amino acid-modified MoS2 nanocomposite by chemically immobilizing Mn (II). The synthesized absorbent MoS2–COOH/gly/Mn was identified by thermogravimetric analysis (TGA), nitrogen adsorption measurement, X-ray diffraction (XRD), analysis of energy dispersive X-ray mapping (EDAX and MAP), field emission scanning electron microscopy (FE-SEM), and Fourier Transform Infrared spectrometry (FT-IR). The nanocomposite was employed as an adsorbent through the solid phase microextraction (SPME) method while trifluralin herbicide was chosen as a model compound. For the monitoring of trifluralin molecules, we employed an ion mobility spectrometry apparatus featuring a corona discharge ionization source. The SPME method's effectiveness was examined by investigating the stirring rate and extraction time as two crucial parameters, aiming to achieve trace analysis of trifluralin. Under the optimized condition of the trifluralin extraction, the coefficient (R2) and linear dynamic range (LDR) correlation were obtained at 0.9961 and 0.5–10 μg L−1, respectively. Relative recovery values the described approach were obtained in the span of 96–97% for agricultural wastewater samples. The quantification (LOQ) and limit of detection (LOD) were calculated at 0.5 and 0.15 μg L−1, respectively. The proposed nanocomposite absorbent has the capability to be applied as an efficient material for the extraction of trifluralin herbicide from different solutions.

Utilizing the derivative of unwrapped phase in ultrasonic nondestructive evaluation of elastic properties of polymer filaments.

Polymer filaments represent the fundamental materials employed in fused filament fabrication additive manufacturing. This paper uses an innovative nondestructive evaluation technique for gauging the elastic properties of these polymer filaments. The method hinges on acoustic wave scattering theory, wherein a polymer filament is immersed in water and exposed to an incident acoustic wave. The waves scattered from the cylinder contain crucial information concerning the elastic properties of the filament. To extract these properties, an inverse method is applied which compares the resonance frequencies of the scattered signal with those anticipated based on a theoretical model. To improve the performance of this method in identifying the resonance frequencies, the derivative of the unwrapped phase within the backscattered pressure spectrum is analyzed. This is an advantageous approach as it can be equally applied to both experimental and theoretical pressure spectra, simplifying the task of identification of resonance frequencies considerably. The method's effectiveness is exemplified through its application to an aluminum rod followed by its application to two polymer filaments. Comparing the elastic constants of the polymer filaments determined through the proposed method with values reported in the literature underscores the method's capability to accurately measure the elastic constants of polymer filaments.

The Construction of a Lattice Image and Dislocation Analysis in High-Resolution Characterizations Based on Diffraction Extinctions.

This paper introduces a method for high-resolution lattice image reconstruction and dislocation analysis based on diffraction extinction. The approach primarily involves locating extinction spots in the Fourier transform spectrum (reciprocal space) and constructing corresponding diffraction wave functions. By the coherent combination of diffraction and transmission waves, the lattice image of the extinction planes is reconstructed. This lattice image is then used for dislocation localization, enabling the observation and analysis of crystal planes that exhibit electron diffraction extinction effects and atomic jump arrangements during high-resolution transmission electron microscopy (HRTEM) characterization. Furthermore, due to the method's effectiveness in localizing dislocations, it offers a unique advantage when analyzing high-resolution images with relatively poor quality. The feasibility of this method is theoretically demonstrated in this paper. Additionally, the method was successfully applied to observed edge dislocations, such as 1/6[211-], 1/6[2-11-], and 1/2[01-1], which are not easily observable in conventional HRTEM characterization processes, in electro-deposited Cu thin films. The Burgers vectors were determined. Moreover, this paper also attempted to observe screw dislocations that are challenging to observe in high-resolution transmission electron microscopy. By shifting a pair of diffraction extinction spots and superimposing the reconstructed images before and after the shift, screw dislocations with a Burgers vector of 1/2[011-] were successfully observed in electro-deposited Cu thin films.

Heat transfer characteristics in vertical tube condensers: Experimental investigation and numerical validation

The paper establishes a vertical steam condensation heat transfer experimental device to investigate the process of liquid film condensation heat transfer under the countercurrent heat transfer of a secondary coolant. An improved experimental method, integrating measurements and simulations, accurately calculates local heat transfer coefficients and heat flux. The validity of the data is confirmed through comparison with established condensation correlations, affirming the method's effectiveness. The correlations of previous research (i.e., Numrich, Carey, Oh, Lee, and Shah) demonstrate greater consistency with the experimental results, with mean relative deviations (MRD) of −4.90%, 7.18%, 11.42%, −8.63% −4.45%, and mean absolute relative deviations (MARD) of 11.61%, 21.57%, 23.60%, 18.97%, and 12.53%, respectively. Numrich's correlation demonstrated the closest congruence. Based on these results, the research proposes a new coupled heat transfer model, effectively simulating the interaction between external cooling water and steam condensation. This model simplifies the process by pre-defining coolant temperature changes per unit length, thereby reducing the need for extensive iterative calculations and eliminating assumptions about wall temperature. It accurately predicts the wall temperature, local condensing temperature, and coolant temperature in a vertical condenser. These experimental methods and models offer valuable insights for optimizing vertical condenser design and analysis. The study underscores the significance of combining simulation models with experimental measurements to enhance the understanding of heat transfer in complex systems. The findings promise to advance research in film condensation heat transfer and assist in condenser design and optimization.

Intelligent electric vehicle charging optimization and horse herd-inspired power generation for enhanced energy management

This article focuses on optimizing electric vehicle charging in distribution networks, emphasizing technical and economic considerations. Unlike traditional methods, the proposed intelligent approach tailors each EV's charging based on specific daily trip energy requirements. Vehicle owners provide trip data to the charge management system, enabling precise charging calculations considering factors such as energy tariffs, distribution network limits, and charging levels. The paper introduces the horse herd optimization algorithm, inspired by horse herd behavior, offering advantages like reduced computational time and improved convergence in maximizing power generation, especially under shading conditions. The comparative analysis of smart EV charging under normal and fast conditions, considering various constraints and load response programs, demonstrates the proposed method's effectiveness. Numerical results reveal a 46.02 % average load reduction and a 20.53 % peak load decrease with a Load Response Program. Charging costs are optimized, with Case 2 exhibiting a 2.61 % cost reduction compared to Case 3. The study delves into charging frequencies, discharge frequencies, total unsupplied energy, and unloaded energy for each case, providing crucial insights into algorithmic performance. The horse herd optimization algorithm-based approach proves superior, offering a promising solution for efficient and cost-effective electric vehicle charging in distribution networks. Furthermore, graphical representations illustrate the algorithm's impact on charging power, energy allocation, power passing through distribution posts, and megavolt-ampere flow through network lines. These numerical and graphical analyses provide a comprehensive understanding of the horse herd optimization algorithm capabilities, emphasizing its potential to optimize power distribution, reduce costs, and enhance the resilience of residential distribution networks.

Algorithmically designed flaps in tongue reconstruction: a feasibility analysis.

Despite the significance and complexity of tongue reconstruction surgery, a digital tool for flap design is currently lacking. This study investigates the effectiveness of employing inverse finite element method (IFEM) for meticulously designing the geometric characteristics of harvested tissue (free flap) for tongue reconstruction. In the case of an artificially simulated hemiglossectomy, IFEM algorithm was applied for algorithmic flap design. The method's effectiveness was evaluated by assessing flap deformation in a simplified virtual reconstruction, focusing on parameters such as stress, strain, and thickness. The IFEM algorithm successfully generated an optimal flap design for the intended surgical removal. Analysis of the flap's overall surface area, deformation characteristics, and safety margins demonstrated the feasibility of the deformation. Notably, the stress and thickness assessments suggested that the flap's tension post-surgery would not adversely affect the mobility of the reconstructed tongue, suggesting a positive outcome for functional recovery. The IFEM demonstrates significant potential as a tool for precise free flap design in tongue reconstruction surgeries. Its application could lead to improved surgical accuracy and better quality of life for patients undergoing such procedures.

A preference group consensus method with three-way decisions and regret theory under multi-scale information systems

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

Tournament rituals and experiential competence development in higher education: A case of a unique conference series

The Hungarian Student Research Society (HSRS) movement is a unique, experience-based tournament ritual promoting student-centered learning via instructors working as facilitators. Within the framework of this 70-year-old conference series, students conduct mentor-supported research activities on self-chosen topics, prepare academic papers, and present their research work to peers and expert juries within their institutions. The best research is included in the National Student Research Conference (NSRC), organized by discipline. The present study reviews and analyzes this process from the perspective of tournament rituals, skill development, and student experience. The study utilizes student opinions, perceptions, and feedback from a Student Research Conference in Economics to illustrate the method's effectiveness. Findings suggest that such conference event series – which function as tournaments, professional conferences, and festivals via community events – effectively develop the soft skills that are becoming increasingly important in the 21st century. In addition to professional competencies, Student Research Conferences (SRC) provide student participants with positive experiences.

An improved method for measuring catalase activity in biological samples.

Catalase (CAT) is an important enzyme that protects biomolecules against oxidative damage by breaking down hydrogen peroxide (H2O2) into water and oxygen. CAT is present in all aerobic microbes, animals, and plants. It is, however, absent from normal human urine but can be detected in pathological urine. CAT testing can thus help to detect such urine. This study presents a novel spectrophotometric method for determining CAT activity characterized by its simplicity, sensitivity, specificity, and rapidity. The method involves incubating enzyme-containing samples with a carefully chosen concentration of H2O2 for a specified incubation period. Subsequently, a solution containing ferrous ammonium sulfate (FAS) and sulfosalicylic acid (SSA) is added to terminate the enzyme activity. A distinctive maroon-colored ferrisulfosalicylate complex is formed. The formation of this complex is a direct result of the reaction between FAS and any residual peroxide present. This leads to the generation of ferric ions when coordinated with SSA. The complex has a maximum absorbance of 490 nm. This advanced method eliminates the need for concentrated acids to stop CAT activity, making it safer and easier to handle. A comparative analysis against the standard ferrithiocyanate method showed a correlation coefficient of 0.99, demonstrating the new method's comparable effectiveness and reliability. In conclusion, a simple and reliable protocol for assessing CAT activity, which utilizes a cuvette or microplate, has been demonstrated in this study. This interference-free protocol can easily be used in research and clinical analysis with considerable accuracy and precision.

An effective DOA estimation method for low SIR in small-size hydrophone array

The estimation ability of traditional direction of arrival (DOA) estimation methods is relatively fragile in small-size hydrophone arrays with limited space. Especially in low signal to interference ratio (SIR), the strong interference signals may submerge some weak signals of interest (SOI) and make DOA estimation difficult in response to this issue. This paper introduces an improved sparse DOA estimation method for practical multi-objective DOA estimation in complex scenarios. The main work is to introduce a noise weight constraint in the sparse iterative covariance process. It leads the algorithm to output sparse peaks and smooth spatial energy spectra and achieve faster fitting while reducing the probability of false peaks. The algorithm can complete DOA estimation of the multi-target reliably without prior information of sources. Then, we propose a fast region grid refinement method based on allocation reconstruction to increase angle resolution. The method increases the accuracy of multi-objective DOA estimation while reducing computational costs. Finally, simulation and experiment have verified the method's effectiveness.

Single-Molecule Analysis of SARS-CoV-2 Double-Stranded Polynucleotides Using Solid-State Nanopore with AI-Assisted Detection and Classification: Implications for Understanding Disease Severity.

This study utilized solid-state nanopores, combined with artificial intelligence (AI), to analyze the double-stranded polynucleotides encoding angiotensin-converting enzyme 2, receptor-binding domain, and N protein, important parts of SARS-CoV-2 infection. By examining ionic current signals during DNA translocation, we revealed the dynamic interactions and structural characteristics of these nucleotide sequences and also quantified their abundance. Nanopores of sizes 3 and 10 nm were efficiently fabricated and characterized, ensuring an optimal experimental approach. Our results showed a clear relationship between DNA capture rates and concentration, proving our method's effectiveness. Notably, longer DNA sequences had higher capture rates, suggesting their importance for potential disease marker analysis. The 3 nm nanopore demonstrated superior performance in our DNA analysis. Using dwell time measurements and excluded currents, we were able to distinguish the longer DNA fragments, paving the way for a DNA length-based analysis. Overall, our research underscores the potential of nanopore technology, enhanced with AI, in analyzing COVID-19-related DNA and its implications for understanding disease severity. This provides insight into innovative diagnostic and treatment strategies.

Urban Energy Transition Policy Implementation: Navigating Complex Political Realities and Overcoming Barriers to Effective Strategy Deployment

As the transition from traditional power grids to smart grids gains momentum, the integration of a cyber-layer into the system becomes imperative specially be considering policy and government intervention. However, this advancement also exposes the system to potential cyber threats, raising concerns about its security and resilience. This study focuses on bolstering cyber security resilience and detecting cyber-attacks (CAs) within urban microgrids. Specifically, the research investigates the impact of false data injection cyber-attacks, a significant threat to microgrids, on the steady-state performance of isolated microgrids. To combat such threats, a novel approach utilizing generative adversarial networks (GANs) is introduced to identify attacks on smart metering systems within microgrids. However, the GANs alone cannot make informed decisions, necessitating a decision-making framework. To address this requirement, a novel method based on prediction intervals is proposed to establish decision boundaries, determining the legitimacy of received data. To evaluate the proposed methodology's performance and accuracy, real-world data from a wind farm is employed. Results demonstrate the method's effectiveness and high accuracy in detecting false data injection attacks. Additionally, the study simulates a false data injection attack on a test microgrid to assess its resilience against CAs, revealing that a successful attack can significantly disrupt normal system operations. This research contributes to enhancing urban microgrid energy security by considering policy-informed decision-making, navigating complexity, and surmounting deployment barriers considering policy and government intervention.

A Comparative Analysis of IoT based Network Anomaly Detection and Prediction Using Vector Autoregressive Models

This research provides a comparative analysis of the use of Vector Autoregressive models for network anomaly detection and prediction. It starts by giving a brief overview of the models and going over the two versions that are available for network anomaly detection. Ultimately, the study offers an empirical assessment of the two types of models, just considering how well they detect and forecast anomalies overall. The results show that the unmarried-node anomaly detection performance of the model is superior. Simultaneously, the Adaptive Learning version is particularly effective in identifying anomalies among a few nodes. The fundamental reasons for the differences in the two fashions' overall performance are also examined in this research. This work provides a comparative analysis of two widely utilized algorithmic approaches: vector autoregressive models and community anomaly detection and prediction. Each method's effectiveness is assessed using two different network datasets: one based on real-world global measurements of latency and mobility ranges, and the other focused on a fictional community. The study also examines the trade-offs between employing the versus other modern and classic techniques, Markov Chain Monte Carlo, and Artificial Neural Networks for network anomaly detection. Finally, it provides an overview of the advantages and disadvantages of each technique as well as suggestions for improving performance.

A Dual-Task Learning Approach for Bearing Anomaly Detection and State Evaluation of Safe Region

Predictive maintenance has emerged as an effective tool for curbing maintenance costs, yet prevailing research predominantly concentrates on the abnormal phases. Within the ostensibly stable healthy phase, the reliance on anomaly detection to preempt equipment malfunctions faces the challenge of sudden anomaly discernment. To address this challenge, this paper proposes a dual-task learning approach for bearing anomaly detection and state evaluation of safe regions. The proposed method transforms the execution of the two tasks into an optimization issue of the hypersphere center. By leveraging the monotonicity and distinguishability pertinent to the tasks as the foundation for optimization, it reconstructs the SVDD model to ensure equilibrium in the model's performance across the two tasks. Subsequent experiments verify the proposed method's effectiveness, which is interpreted from the perspectives of parameter adjustment and enveloping trade-offs. In the meantime, experimental results also show two deficiencies in anomaly detection accuracy and state evaluation metrics. Their theoretical analysis inspires us to focus on feature extraction and data collection to achieve improvements. The proposed method lays the foundation for realizing predictive maintenance in a healthy stage by improving condition awareness in safe regions.

PCB-C4D coupled with paper-based microfluidic sampling for the rapid detection of liquid conductivity.

The detection of liquid electrical conductivity has board applications in food safety testing, water quality monitoring, and agricultural soil analysis. Electrodes used in traditional liquid electrical conductivity detection come into direct contact with liquid, leading to electrode contamination and affecting the accuracy of the detection results. The capacitively coupled contactless conductivity detection (C4D) method effectively addresses this issue. However, impurity particles present in the solution can compromise the consistency and repeatability of detection results. This study combines paper-based microfluidic technology with printed circuit board-capacitively coupled contactless conductivity detection (PCB-C4D) to address this issue. Prior to sample detection, in situ rapid filtration is employed to remove impurity particles from the raw solution sample, significantly enhancing detection consistency and reliability. Simultaneously, Optimization of PCB-C4D parameters, channel size, filtration time, and solution drop rate ensures optimal detection conditions. A compact kit design facilitates reliable assembly of the PCB-C4D electrodes and paper-based channel, enhancing practicality. Practical measurements on the conductivity of orange juice, cucumber, and soil solution further validate the method's accuracy, rapidity, and effectiveness in in situ conductivity detection. This work advances the practical application of PCB-C4D technology.