Entropy Values Research Articles

Cobalt Nanoparticles Supported Active Carbon from Chitosan Biopolymer Using Thermal Method: Synthesis, Characterization, and Hydrogen Production

AbstractActivated carbon based Cobalt nanoparticles (Co@AC NPs), considered in the context of hydrogen energy, which is a renewable and sustainable energy, were synthesized by the hydrothermal method, and their catalytic activities were tested. For this, hydrogen production tests were carried out with the help of sodium borohydride (NaBH4) methanolysis of Co@AC NPs synthesized by the thermal method. Ultraviolet–visible spectroscopy (UV–Vis), Fourier transmission spectroscopy (FTIR), transmission electron microscopy (TEM), and X-ray diffraction (XRD) characterization tests were performed. According to the TEM characterization result, it has been observed that the NPs have a spherical shape and an average size of 2.52 ± 0.92 nm. Then, using the catalytic studies, it was observed that hydrogen production’s reusability is found to be 86% . The activation energy (Ea), enthalpy (∆H), and entropy (∆S) values were found to be 20.28 kJ⋅mol−1, 17.74 kJ⋅mol−1, and −125.97 J⋅mol−1 K−1, respectively. The obtained values have yielded excellent results and guide future sustainable and renewable hydrogen energy studies by reducing costs, ensuring environmental sustainability by avoiding the formation of undesirable by-products, and producing hydrogen from NaBH4 through its high catalytic properties.

Date nawah powder as a promising waste for β-mannanase production from a new isolate Aspergillus niger MSSFW, statistically improving production and enzymatic characterization

β-Mannanase producing fungus was isolated from coffee powder waste and identified as Aspergillus niger MSSFW (Gen Bank accession number OR668928). Dates nawah powder as industrial and agricultural waste was the most inducer of β-mannanase production. The Plackett–Burman and Central Composite designs were used to improve β-mannanase titer. Optimization studies enhanced the enzyme yield with approximate 13.50-times. β-Mannanase was purified by Sephadex G-150 gel filtration column and the molecular weight was estimated to be 60 kDa by SDS–PAGE. Crude and purified β-mannanase displayed maximum activity at temperature 60 °C and 50 °C, respectively. Crude β-mannanase showed an activation energy value 2.35-times higher than the purified enzyme. Activation energy for thermal denaturation of the purified β-mannanase was 1.08-times higher than that of the crude enzyme. Purified β-mannanase exhibited higher deactivation rate constant (Kd) and lower half-life (t0.5) and decimal reduction time (D-value) compared with the crude enzyme. Thermodynamic parameters of enthalpy, entropy, and free energy values for crude and purified β-mannanase were calculated. Substrate kinetic parameters suggested that the purified β-mannanase had a strong affinity toward locust bean gum by showing 3.44-times lower Km and 1.99-times higher Vmax compared to the crude enzyme.

A study of the influence of the stability of the population's income and expenditure on the development of restaurant chains

This study examines the connection between the income of Chinese residents and the expansion of restaurant chains from 2005 to 2021. It employs multiple methodologies, such as an evaluation index system, the entropy value method, and the coupled coordination degree model. There are sixteen indicators divided into two groups: residents' income and expenditures, and restaurant chain expansion. The analysis reveals the following trends: instability from 2005 to 2009 as a result of the global financial crisis; regional disparities from 2010 to 2014; and coordinated development from 2015 to 2021 as a result of government policies that stimulate domestic demand. This research contributes to strategic planning and decision-making in the hotel, catering, and tourism industries by assessing the effect of Chinese residents' stable income and expenditures on restaurant chains.

An extraction method for early fault features of rolling bearings based on resonance sparse signal decomposition and non-convex second-order total variation denoising

A primary challenge in the field of fault diagnosis is extracting weak fault characteristics of bearings under large background noise and non-stationary conditions. Due to significant noise interference in the signals of rolling bearings, resonance sparse signal decomposition (RSSD) cannot efficiently extract the transient impact components in the early failure stage and the total variation denoising (TVD) method distorts signal waveforms. In this study, a combined extraction method for early fault features of rolling bearings based on RSSD and non-convex second-order total variation denoising (NCSOTVD) is proposed. Firstly, a non-convex function is introduced to define the regularisation term in the second-order TVD method, and the regularisation parameter and the convexity parameter in the NCSOTVD method are respectively screened using the noise standard deviation and the permutation entropy value to enhance the impact characteristics of signals and induce signal sparsity. The NCSOTVD model is solved using the optimisation-minimisation algorithm, so as to achieve noise reduction and feature enhancement of the vibration signals. Then, the low-resonance components of RSSD are denoised using the NCSOTVD method to highlight periodic pulse signals and extract the fault features of the rolling bearings. The simulation results and experimental data show that the method largely suppresses the noise interference, highlights the fault characteristics and reduces the problems of waveform distortion and poor sparsity of the TVD method in the denoising process.

Dual image watermarking based on NSST-LWT-DCT for color image

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively.

Adjusting Mood in Mandarin Chinese: A Game Theory Approach to Double and Redundant Negation with Entropy

Abstract This study investigates how Mandarin Chinese speakers use double and redundant negations to adjust information entropy and convey the speaker’s mood. Integrating Gricean implicature, signaling game theory, and information entropy, we quantified uncertainty in negation sentences by calculating self-information of negation markers and overall entropy for negations versus affirmatives. Analyzing sentences from the BCC Corpus of Mandarin Chinese newspapers, we extracted double and redundant negation patterns, calculated their frequencies, and compared entropy values in payoff matrices against affirmatives. Contextual analysis of surrounding discourse interpreted pragmatic functions. Our findings reveal that universal double negations reduce sentence entropy, intensifying assertiveness. Modal forms further decrease entropy, amplifying necessity, while conditional patterns strengthen the asserted conditions. Euphemistic double negations increase entropy, weaken assertiveness, and align with Levinson’s Manner Principle. This increase in entropy quantifies pragmatic markedness, allowing tentative critiques. Redundant negations significantly reduce entropy, strengthening negative attitudes. We elucidate how interlocutors navigate cooperative discourse by quantifying uncertainty via entropy measures, making contextually-optimal linguistic choices like assertion, indirectness, and emphasis. These findings impact signaling game theory and pragmatic markedness, with applications in natural language processing, cross-cultural communication, and computational models. Future research could explore other languages and cognitive and social factors in negation use.

Investigation of the impacts of several diols on the phase behavior and physico-chemical quantities associated with the Triton X-100 and antidiabetic drug mixture

Drug-surfactant interaction phenomena have received popularity in the pharmaceutical industry and technological sectors from the view point of wider applications. Herein, we aimed to investigate the clouding behavior and various thermodynamic properties of Triton X-100 (TX-100) and metformin hydrochloride drug (MNH; an antidiabetic drug) mixture in the attendance of diols followed by the cloud point measurement technique. The dosage form of metformin hydrochloride drug controls the glucose level in the blood. The different types of diols, i.e., ethylene glycol (EG), 1,2-propylene glycol (1,2-PrG), 2,3-butanediol (2,3-BD), and 1,5-pentanediol (1,5-PD) were uniformly used in this inspection. The effect of diols, with the variation of their concentrations on the clouding behavior of TX-100 + MNH mixture having fixed content of TX-100 (92.7 mmol kg−1) and MNH drug (2 mmol kg−1), were examined. The most enhanced CP value was experienced in the aqueous 1,5-PD medium, whereas the lowest CP value was observed in the aqueous EG medium. The CP values were recorded for the working system under the effect of changing concentrations of diols, which were observed to follow the sequential order: CPH2O+1,5−PD>CPH2O+2,3−BD>CPH2O+1,2−PrG>CPH2O+EG. The clouding progression of the working system was realized to be nonspontaneous as concluded from the positive free energy change (ΔGco) values. The positive and negative values of enthalpy (ΔHco) and entropy (ΔSco) change were documented, respectively, at the low and high concentrations of diols. The hydrophobic interactions amongst different components are predominant at the low concentrations of diols, whereas the electrostatic interactions are leading factors at the high concentrations of diols. Enthalpy-entropy compensation parameters were computed and interpreted with proper perceptive. The outcomes of this observation may be effective and helpful in the various industrial sectors, especially in the drug carrier system in the pharmaceutical arena.

RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment

The bearings have been exposed to a noisy environment for an extended period, making it challenging to identify fault characteristics accurately and resulting in low accuracy. In this study, we proposed a technique for diagnosing train-bearing faults in noisy environments using refined time-shift multiscale fractional order fuzzy dispersion entropy and hybrid kernel ridge regression (RTSMFFDE-HKRR) to enhance the precision of fault detection. Firstly, RTSMFFDE is proposed based on multiscale fuzzy dispersion entropy by combining the theories of refinement, time-shifting, and fractional order. Revising the refinement process helps to stabilize the entropy value when dealing with a large scale factor. Implementing time-shifting techniques can help preserve the original signal features more effectively. The theory of fractional order strengthens the noise-resistant performance of the algorithm, and the proposed RTSMFFDE has a superior feature extraction capability. Subsequently, a hybrid kernel function is formulated through the combination of the radial basis kernel function (RBF) and the linear kernel function, aiming to improve the nonlinear mapping ability and anti-noise ability of ridge regression classification algorithm. Finally, two sets of experimental cases were used to verify the proposed RTSMFFDE-HKRR. The results show that the fault feature information extracted by RTSMFFDE in noisy environment is more comprehensive, and the classification effect of HKRR is more significant. In the fault diagnosis of two bearing simulation test beds, the accuracy rate of RTSMFFDE-HKRR is up to 100%. In the noisy environment, the accuracy rate is 98.84% and 97.32%, which is much higher than other diagnostic models. RTSMFFDE-HKRR is suitable for train bearing fault diagnosis in noisy environment.

Classification of Avocado Ripeness Levels Using CNN Method

This research aims to develop a model for classifying the ripeness level of avocados using the Convolutional Neural Network (CNN) algorithm. The dataset comprises images of avocados categorized into three classes: unripe, ripe, and overripe. The CNN model is trained to classify the images into one of these three categories. The results indicate that the developed model can classify avocado images with high accuracy. The primary tool used for developing and implementing this method is MATLAB R2022a. The CNN algorithm is utilized to recognize and classify the ripeness level of avocados. This process involves several image processing steps, starting with preprocessing, image enhancement, and segmentation to isolate the avocado area. The dataset used in this research consists of 452 images distributed in 3 classes (unripe with 142, ripe with 66, and rotten with 244), with 80% used for training and 20% for testing. After 10 accuracy tests, the results indicate an accuracy rate of 90%. Additionally, features extracted from the images include color, shape, size, and texture characteristics, such as Mean, Standard Deviation, Kurtosis, Skewness, Variance, Entropy Value, Maximum Pixel, and Minimum Pixel. This research contributes to the field of agricultural technology by providing a robust method for the automatic classification of avocado ripeness. The findings are expected to facilitate accurate and efficient recognition of avocado ripeness, thereby supporting agricultural practices and market operations. Future research could explore the use of data augmentation techniques to further improve the accuracy and generalization of this model.

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method

With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.

Structural insights into the binding interaction between noraucuparin and bovine serum albumin: Implications for antibiotic design and function

Noraucuparin, a biphenyl phytoalexin, emerges as a promising drug candidate. Serum albumin plays a very important role in the in vivo transportation of drugs. The investigation delves into the binding interaction between noraucuparin and BSA employing comprehensive spectroscopic methods and molecular docking technique. UV–vis and FL spectrum results indicated that noraucuparin bond with BSA, and there was a new complex produced at the binding site II in domains IIIA (Ser-453 residue). The dissociation constants Kd from The ITC analysis was found to be 4.63 × 10−5 M at 298 K and 6.67 × 10−5 M at 310 K, showing 310 K is favorable for the combination of noraucuparin and BSA. The negative enthalpy (ΔH) and entropy (ΔS) values for the interaction revealed that the binding behavior was driven by hydrogen bonds. The results obtained from 3D fluorescence and CD spectrum illustrated that noraucuparin could change the secondary structure of BSA. According to molecule docking result, the binding energy of interaction is −7.09 kcal·mol−1. These revelations furnish a nuanced molecular-level comprehension of noraucuparin's mechanism of action on serum albumin. The discernments garnered from this study bear substantial reference significance and practical implications for ongoing lead compound exploration in antibiotics, particularly in vivo contexts.

Harris Hawk optimization driven adaptive image encryption integrating Hilbert vibrational decomposition and chaos

Image encryption is a technique used to protect the confidentiality of digital images. Existing encryption algorithms use chaotic maps for generating random sequences. These sequences purely depend on the fixed initial parameters, which are not adaptive to different input images, thereby limiting their ability to produce the best-possible encrypted image. This paper introduces a new Harris Hawk optimization (HHO) based adaptive image encryption algorithm that integrates Hilbert vibrational decomposition (HVD), and multiple chaotic maps. The new fitness function is specifically designed to optimize the security and robustness of the encrypted image, considering statistical and differential attack parameters (NPCR, UACI, Entropy, and correlation coefficient values). HHO is used to optimize the initial parameters of chaotic maps (Henon map, Duffing map, Lorenz equation) subject to variations in the input image. In the confusion stage, the input image is decomposed into four components of same dimension but decreasing energy using HVD. Subsequently, the block based pixel scrambling is performed on alternate components using the Henon map and Duffing map. In the diffusion stage, the image from the confusion stage undergoes a first level diffusion by bitwise XOR operation with a random image generated from the Lorenz equation. Further, second level of diffusion is achieved with a complex operation generated image from the input image, enhancing its adaptability. Finally, Arnold Cat map having number of iterations determined by the input image is used for final pixel permutation to produces the encrypted image. The optimized initial parameters and input image dependent confusion and diffusion operations enable the encryption system to adapt to changes in the input image, thereby enhancing its resistance against known plain text and known cipher text attacks. Moreover, the integration of HVD with multiple chaotic maps makes the scheme more complex to decrypt with plain text attacks. The performance of proposed algorithm is evaluated on five standard test images in terms of security and quality measures. Experimental results exhibit statistical and differential attack parameters close to ideal values (with Max. Entropy≈7.9996, Min. CC≈ 10−5, Max. NPCR≈99.64, Max. UACI≈33.62). The use of multiple chaotic maps at different stages makes the key space larger. Robustness analysis demonstrates the algorithm's capability in withstanding noise and image enhancement attacks. The comparison with state of the arts metaheuristic based and classical encryption algorithms highlights the superior performance of proposed HHO driven algorithm. The significant improvement in security parameters and overall fitness compared to fixed initial parameters is achieved by optimizing the encryption process.

Study on the Measurement and Evaluation of the Domestic Circular Development Level of China's Economy

Since reform and opening up, China has actively promoted the development of the international economic cycle, but there are still some areas that are at a disadvantage in the value and supply chain in long-term international transactions. Although the New Crown epidemic has had a huge impact on the global economy, the domestic economy has gradually recovered and international trade has recovered. Against this background, General Secretary Xi Jinping has proposed to accelerate the construction of a new development pattern “with the domestic cycle as the main body and the domestic and international cycles promoting each other”. This paper takes the demand side of the domestic cycle of China's economy as the entry point, selects the consumption base, consumption willingness, consumption structure and other specific indicators of 31 provinces in China from 2015 to 2020, and adopts the entropy value method to study China's domestic cycle at this stage. The study finds that (1) there are obvious regional differences in China's economic domestic cycle, and the eastern and central regions are important contributors to the domestic cycle; (2) the development of China's economic domestic cycle is in a positive trend after the epidemic; (3) the level of economic exchanges between regions is low, and there is the problem of unbalanced and insufficient development. An in-depth understanding of the role and pattern of China's economic double cycle in the domestic economy will help grasp the actual level of the new development pattern.

Radiomic Gradient in Peritumoural Tissue of Liver Metastases: A Biomarker for Clinical Practice? Analysing Density, Entropy, and Uniformity Variations with Distance from the Tumour.

The radiomic analysis of the tissue surrounding colorectal liver metastases (CRLM) enhances the prediction accuracy of pathology data and survival. We explored the variation of the textural features in the peritumoural tissue as the distance from CRLM increases. We considered patients with hypodense CRLMs >10 mm and high-quality computed tomography (CT). In the portal phase, we segmented (1) the tumour, (2) a series of concentric rims at a progressively increasing distance from CRLM (from one to ten millimetres), and (3) a cylinder of normal parenchyma (Liver-VOI). Sixty-three CRLMs in 51 patients were analysed. Median peritumoural HU values were similar to Liver-VOI, except for the first millimetre around the CRLM. Entropy progressively decreased (from 3.11 of CRLM to 2.54 of Liver-VOI), while uniformity increased (from 0.135 to 0.199, p < 0.001). At 10 mm from CRLM, entropy was similar to the Liver-VOI in 62% of cases and uniformity in 46%. In small CRLMs (≤30 mm) and responders to chemotherapy, normalisation of entropy and uniformity values occurred in a higher proportion of cases and at a shorter distance. The radiomic analysis of the parenchyma surrounding CRLMs unveiled a wide halo of progressively decreasing entropy and increasing uniformity despite a normal radiological aspect. Underlying pathology data should be investigated.

Investigation of Two‐Dimensional Ruddlesden–Popper BZA2PbX4 (X = I, Br, and Cl) and Mixed‐Halides BZA2PbBrxCl4−x Perovskites: Opto‐Electromechanical, Thermodynamic Properties, Moisture, and Strain Effects

Herein, the properties of BZA2PbX4 (X = I, Br, and Cl) and mixed halides BZA2PbBrxCl4‐x (x = 1, 2, and 3) perovskites are comprehensively investigated, utilizing density functional theory (DFT). As there has been no prior comprehensive study on the substantial characteristics of mixed halides and the distinctive features of BZA2PbI4, BZA2PbBr4, and BZA2PbCl4 using DFT, a significant gap in the existing literature is addressed. The optical and electronic properties, band structures, band edge orbitals, and effective masses with and without spin‐orbit coupling effects are analyzed. The investigation also examines the impact of strain (tensile and compressive) on the properties of these materials. It is revealed that among the investigated compounds, BZA2PbCl4 emerges as the most thermodynamic and moisture stable. This trend is corroborated by the energy fluctuation, with BZA2PbCl4 demonstrating a narrower range, indicative of superior thermal stability. Furthermore, Voigt–Ruess–Hill approximations indicate that the mechanical moduli increase as the halide changes from I to Cl. Moreover, BZA2PbI4 exhibits the highest level of disorder, whereas BZA2PbCl4 has the lowest entropy value at the same temperature. The results highlight that these materials emerge as promising candidates for applications in the active layer of perovskite light‐emitting diodes and optoelectronic devices.

Ant Colony Optimization Based Edge Detection Algorithm

The problem of edge detection represents one of the most elementary assignments in image processing, providing an essentialbase for all further study and interpretation of the visual data analysis. This paper proposed an enhanced version of the Ant Colony Optimization (ACO) algorithm for edge detection. The following paper tries to compare the Proposed ACO method with the conventional techniques of edge detection like Canny, Prewitt, and Sobel, using various quantitative metrics like Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Entropy, Natural Image Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) applied over different images. The datasets for this evaluation are considered as a standard Cameraman, a biological cell, and MRIimage, with and without noise, considering the ranges of complexities and textures.The results of our study prove the competencies of ACO's algorithm. In some cases, it stands out against standard algorithms for MSE and PSNR values and maintains high Entropy values, suggesting the robustness of detail-keeping in an image. Further, the quality assessment of the images by using NIQE and BRISQUE shows the ability of ACO to maintain a natural appearance post-edge detection. In this regard, the studyhighlights that the proposed ACO isan effective method for edge detection in varying image conditions,and, in doing so, it even validates the effectiveness of bio-inspired algorithms in image processing domains.

Multi-weighted symbolic sequence entropy: a novel approach to fault diagnosis and degradation monitoring of rotary machinery

Abstract Structural health monitoring relies heavily on measurements. Entropy theory is emerging as a critical quantitative analysis technique for interpreting measured data for both health monitoring and fault identification of structural systems. This paper introduces the algorithms of weighted symbolic sequence entropy (WSSE) and its multi-weighted derivation, termed multi-weighted symbolic sequence entropy (MWSSE). WSSE optimizes the existing algorithms, including symbolic sequence entropy (SSE) and improved symbolic sequence entropy (ISSE), by introducing a weighting factor in the computation process of the algorithm. WSSE significantly contributes to enlarging the difference between normal and fault sequences of signals in rotary machinery. The multi-weighted derivation of WSSE, namely MWSSE, more effectively depicts the dynamical characteristics of rotary machinery by utilizing entropy values of multiple weighting factors as health indicators. The MWSSE-based degradation monitoring and fault identification approaches developed by the authors are validated through application research and comparative analysis. These novel entropy algorithms offer innovative quantitative analysis techniques for fault diagnosis in rotary machinery and structural health monitoring and hold potential for application in broader research fields.

Fish 3D Locomotion app: a user-friendly computer application package for automatic data calculation and endpoint extraction for novel tank behavior in fish.

This paper introduces the Fish 3D Locomotion app (F3LA), a Python-based, Graphical User Interface (GUI)-equipped tool designed to automate behavioral endpoint extraction in zebrafish locomotion assays. Building on our previous work, which utilized a specialized aquatic tank with a mirror and a single camera for fish movement tracking in three dimensions, F3LA significantly enhances data processing efficiency. Its accuracy was tested by reanalyzing and comprehensively comparing the calculated data with the previously published data from prior publications. From the comparison results, 90% of endpoints showed a similar statistical difference result. These minor differences were due to the different starting points for the dataset and updated calculation formulas that are implemented in F3LA. In addition, shoaling area or shoaling volume calculations are also included in F3LA as a new feature that can serve as sensitive indicators of social cohesion, group dynamics, or stress responses, offering insights into neuropsychological conditions or the effects of pharmacological interventions. Furthermore, F3LA offers a marked improvement over manual operations, being at least five times faster, while maintaining consistent accuracy as it reduces human-induced errors, ensuring a higher degree of reliability in the results. Finally, the potency of F3LA was tested to evaluate the toxicities of 14 rare earth elements (REEs) to the adult zebrafish behaviors. Based on the results, our findings suggested that each tested REE altered fish behaviors in different patterns and magnitudes to each other. However, among the tested light rare earth elements (LREEs), neodymium was demonstrated to cause more relatively severe behavior alterations than other LREEs, indicated by the statistically higher value of entropy (0.2695 ± 0.04977 (mean with a standard deviation)) than the control group (0.2352 ± 0.05896). Meanwhile, in terms of heavy rare earth elements (HREEs), erbium seemed to lead to more distinct behavior toxicities than other HREEs, which was shown by the statistically lower level of fractal dimension (2.022 ± 0.3412) than the untreated group (2.255 ± 0.1661). Taken together, F3LA's development marks a significant advance in high-throughput toxicological and pharmacological assessments in zebrafish, leveraging three-dimensional locomotion data for a more comprehensive analysis of fish behavior performance, providing a significant contribution to research in various fields.

A Wind Power Combination Forecasting Method Based on GASF Image Representation and UniFormer

In the field of wind power prediction, traditional methods typically rely on one-dimensional time-series data for feature extraction and prediction. In this study, we propose an innovative short-term wind power forecasting approach using a “visual” 2D image prediction method that effectively utilizes spatial pattern information in time-series data by combining wind power series and related environmental features into a 2D GASF image. Firstly, the wind power data are decomposed using the ICEEMDAN algorithm optimized by the BWO (Beluga Whale Optimization) algorithm, extracting the submodal IMF (Intrinsic Mode Function) components with different frequencies. Then, modal reconstruction is performed on the basis of the permutation entropy value of the IMF components, selecting meteorological features highly correlated with reconstructed components through Spearman correlation analysis for data splicing and superposition before converting them into GASF images. Finally, the GASF images are input into the UniFormer model for wind power sequence prediction. By leveraging wind power data predictions from a coastal wind farm in East China and Sotavento in Spain, this study demonstrates the significant benefits and potential applications of this methodology for precise wind power forecasting. This research combines the advantages of image feature extraction and time-series prediction to offer novel perspectives and tools for predicting renewable energy sources such as wind power.